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def lowerCAmelCase_ ( _snake_case : Tuple , _snake_case : str ) -> Optional[Any]:
'''simple docstring'''
print("\nThe shortest path matrix using Floyd Warshall algorithm\n" )
for i in range(_snake_case ):
for j in range(_snake_case ):
if dist[i][j] != float("inf" ):
print(int(dist[i][j] ) , end="\t" )
else:
print("INF" , end="\t" )
print()
def lowerCAmelCase_ ( _snake_case : Tuple , _snake_case : List[Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Dict = [[float("inf" ) for _ in range(_snake_case )] for _ in range(_snake_case )]
for i in range(_snake_case ):
for j in range(_snake_case ):
__magic_name__ : List[Any] = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(_snake_case ):
# looping through rows of graph array
for i in range(_snake_case ):
# looping through columns of graph array
for j in range(_snake_case ):
if (
dist[i][k] != float("inf" )
and dist[k][j] != float("inf" )
and dist[i][k] + dist[k][j] < dist[i][j]
):
__magic_name__ : str = dist[i][k] + dist[k][j]
_print_dist(_snake_case , _snake_case )
return dist, v
if __name__ == "__main__":
snake_case : List[str] = int(input("Enter number of vertices: "))
snake_case : Dict = int(input("Enter number of edges: "))
snake_case : Any = [[float("inf") for i in range(v)] for j in range(v)]
for i in range(v):
snake_case : Optional[int] = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("\nEdge ", i + 1)
snake_case : Union[str, Any] = int(input("Enter source:"))
snake_case : List[str] = int(input("Enter destination:"))
snake_case : Union[str, Any] = float(input("Enter weight:"))
snake_case : Tuple = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 281
|
def lowerCAmelCase_ ( _snake_case : list[list[int | float]] ) -> int:
'''simple docstring'''
__magic_name__ : Any = len(_snake_case )
__magic_name__ : Optional[Any] = len(matrix[0] )
__magic_name__ : Union[str, Any] = min(_snake_case , _snake_case )
for row in range(_snake_case ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , _snake_case ):
__magic_name__ : Optional[Any] = matrix[col][row] / matrix[row][row]
for i in range(_snake_case , _snake_case ):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
__magic_name__ : str = True
for i in range(row + 1 , _snake_case ):
if matrix[i][row] != 0:
__magic_name__ , __magic_name__ : List[str] = matrix[i], matrix[row]
__magic_name__ : Union[str, Any] = False
break
if reduce:
rank -= 1
for i in range(_snake_case ):
__magic_name__ : Any = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 281
| 1
|
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : torch.FloatTensor
class lowerCAmelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ):
'''simple docstring'''
@register_to_config
def __init__( self : str , SCREAMING_SNAKE_CASE_ : int = 6_55_36 , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 0 , SCREAMING_SNAKE_CASE_ : str = "fourier" , SCREAMING_SNAKE_CASE_ : bool = True , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : float = 0.0 , SCREAMING_SNAKE_CASE_ : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , SCREAMING_SNAKE_CASE_ : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , SCREAMING_SNAKE_CASE_ : Tuple[str] = "UNetMidBlock1D" , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : Tuple[int] = (32, 32, 64) , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : int = 8 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : bool = False , ) -> Tuple:
'''simple docstring'''
super().__init__()
A: Optional[Any] = sample_size
# time
if time_embedding_type == "fourier":
A: Tuple = GaussianFourierProjection(
embedding_size=8 , set_W_to_weight=SCREAMING_SNAKE_CASE_ , log=SCREAMING_SNAKE_CASE_ , flip_sin_to_cos=SCREAMING_SNAKE_CASE_ )
A: List[str] = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
A: str = Timesteps(
block_out_channels[0] , flip_sin_to_cos=SCREAMING_SNAKE_CASE_ , downscale_freq_shift=SCREAMING_SNAKE_CASE_ )
A: Any = block_out_channels[0]
if use_timestep_embedding:
A: Optional[Any] = block_out_channels[0] * 4
A: List[Any] = TimestepEmbedding(
in_channels=SCREAMING_SNAKE_CASE_ , time_embed_dim=SCREAMING_SNAKE_CASE_ , act_fn=SCREAMING_SNAKE_CASE_ , out_dim=block_out_channels[0] , )
A: Optional[Any] = nn.ModuleList([] )
A: str = None
A: str = nn.ModuleList([] )
A: Tuple = None
# down
A: Any = in_channels
for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE_ ):
A: Optional[int] = output_channel
A: List[Any] = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
A: List[Any] = i == len(SCREAMING_SNAKE_CASE_ ) - 1
A: Optional[int] = get_down_block(
SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , out_channels=SCREAMING_SNAKE_CASE_ , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , )
self.down_blocks.append(SCREAMING_SNAKE_CASE_ )
# mid
A: Union[str, Any] = get_mid_block(
SCREAMING_SNAKE_CASE_ , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=SCREAMING_SNAKE_CASE_ , add_downsample=SCREAMING_SNAKE_CASE_ , )
# up
A: Optional[Any] = list(reversed(SCREAMING_SNAKE_CASE_ ) )
A: List[str] = reversed_block_out_channels[0]
if out_block_type is None:
A: int = out_channels
else:
A: Union[str, Any] = block_out_channels[0]
for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE_ ):
A: List[Any] = output_channel
A: int = (
reversed_block_out_channels[i + 1] if i < len(SCREAMING_SNAKE_CASE_ ) - 1 else final_upsample_channels
)
A: Optional[int] = i == len(SCREAMING_SNAKE_CASE_ ) - 1
A: Optional[Any] = get_up_block(
SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , out_channels=SCREAMING_SNAKE_CASE_ , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , )
self.up_blocks.append(SCREAMING_SNAKE_CASE_ )
A: Any = output_channel
# out
A: List[str] = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 32 )
A: Optional[int] = get_out_block(
out_block_type=SCREAMING_SNAKE_CASE_ , num_groups_out=SCREAMING_SNAKE_CASE_ , embed_dim=block_out_channels[0] , out_channels=SCREAMING_SNAKE_CASE_ , act_fn=SCREAMING_SNAKE_CASE_ , fc_dim=block_out_channels[-1] // 4 , )
def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : Union[torch.Tensor, float, int] , SCREAMING_SNAKE_CASE_ : bool = True , ) -> Union[UNetaDOutput, Tuple]:
'''simple docstring'''
A: Any = timestep
if not torch.is_tensor(SCREAMING_SNAKE_CASE_ ):
A: Union[str, Any] = torch.tensor([timesteps] , dtype=torch.long , device=sample.device )
elif torch.is_tensor(SCREAMING_SNAKE_CASE_ ) and len(timesteps.shape ) == 0:
A: List[str] = timesteps[None].to(sample.device )
A: int = self.time_proj(SCREAMING_SNAKE_CASE_ )
if self.config.use_timestep_embedding:
A: List[Any] = self.time_mlp(SCREAMING_SNAKE_CASE_ )
else:
A: str = timestep_embed[..., None]
A: Union[str, Any] = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
A: Tuple = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
A: List[str] = ()
for downsample_block in self.down_blocks:
A: Optional[int] = downsample_block(hidden_states=SCREAMING_SNAKE_CASE_ , temb=SCREAMING_SNAKE_CASE_ )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
A: Dict = self.mid_block(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
A: List[Any] = down_block_res_samples[-1:]
A: List[str] = down_block_res_samples[:-1]
A: Optional[int] = upsample_block(SCREAMING_SNAKE_CASE_ , res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ , temb=SCREAMING_SNAKE_CASE_ )
# 5. post-process
if self.out_block:
A: Any = self.out_block(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=SCREAMING_SNAKE_CASE_ )
| 356
|
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
UpperCamelCase = {
'''vocab_file''': {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
),
},
}
UpperCamelCase = {
'''vocab_file''': {
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
),
},
}
UpperCamelCase = {
'''vocab_file''': {
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
),
},
}
UpperCamelCase = {
'''facebook/dpr-ctx_encoder-single-nq-base''': 512,
'''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
UpperCamelCase = {
'''facebook/dpr-question_encoder-single-nq-base''': 512,
'''facebook/dpr-question_encoder-multiset-base''': 512,
}
UpperCamelCase = {
'''facebook/dpr-reader-single-nq-base''': 512,
'''facebook/dpr-reader-multiset-base''': 512,
}
UpperCamelCase = {
'''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
UpperCamelCase = {
'''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
UpperCamelCase = {
'''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : Union[str, Any] = VOCAB_FILES_NAMES
UpperCamelCase_ : Union[str, Any] = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Union[str, Any] = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : Optional[Any] = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ : Any = DPRContextEncoderTokenizer
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : Dict = VOCAB_FILES_NAMES
UpperCamelCase_ : List[str] = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : List[Any] = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : Tuple = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ : Optional[int] = DPRQuestionEncoderTokenizer
UpperCamelCase = collections.namedtuple(
'''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)
UpperCamelCase = collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])
UpperCamelCase = R'''
Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
with the format:
[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
Args:
questions (`str` or `List[str]`):
The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
`titles` or `texts`.
titles (`str` or `List[str]`):
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
texts (`str` or `List[str]`):
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).
- `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.
- `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `\'tf\'`: Return TensorFlow `tf.constant` objects.
- `\'pt\'`: Return PyTorch `torch.Tensor` objects.
- `\'np\'`: Return Numpy `np.ndarray` objects.
return_attention_mask (`bool`, *optional*):
Whether or not to return the attention mask. If not set, will return the attention mask according to the
specific tokenizer\'s default, defined by the `return_outputs` attribute.
[What are attention masks?](../glossary#attention-mask)
Return:
`Dict[str, List[List[int]]]`: A dictionary with the following keys:
- `input_ids`: List of token ids to be fed to a model.
- `attention_mask`: List of indices specifying which tokens should be attended to by the model.
'''
@add_start_docstrings(UpperCAmelCase_ )
class lowerCAmelCase_ :
'''simple docstring'''
def __call__( self : Dict , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[str] = None , SCREAMING_SNAKE_CASE_ : Optional[str] = None , SCREAMING_SNAKE_CASE_ : Union[bool, str] = False , SCREAMING_SNAKE_CASE_ : Union[bool, str] = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , **SCREAMING_SNAKE_CASE_ : Dict , ) -> BatchEncoding:
'''simple docstring'''
if titles is None and texts is None:
return super().__call__(
SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
elif titles is None or texts is None:
A: Union[str, Any] = titles if texts is None else texts
return super().__call__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
A: Union[str, Any] = titles if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else [titles]
A: Optional[Any] = texts if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else [texts]
A: str = len(SCREAMING_SNAKE_CASE_ )
A: List[Any] = questions if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else [questions] * n_passages
assert len(SCREAMING_SNAKE_CASE_ ) == len(
SCREAMING_SNAKE_CASE_ ), f"""There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE_ )} titles and {len(SCREAMING_SNAKE_CASE_ )} texts."""
A: Union[str, Any] = super().__call__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )['''input_ids''']
A: Dict = super().__call__(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )['''input_ids''']
A: str = {
'''input_ids''': [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
]
}
if return_attention_mask is not False:
A: Union[str, Any] = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
A: Optional[Any] = attention_mask
return self.pad(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : BatchEncoding , SCREAMING_SNAKE_CASE_ : DPRReaderOutput , SCREAMING_SNAKE_CASE_ : int = 16 , SCREAMING_SNAKE_CASE_ : int = 64 , SCREAMING_SNAKE_CASE_ : int = 4 , ) -> List[DPRSpanPrediction]:
'''simple docstring'''
A: Any = reader_input['''input_ids''']
A , A , A: str = reader_output[:3]
A: str = len(SCREAMING_SNAKE_CASE_ )
A: Union[str, Any] = sorted(range(SCREAMING_SNAKE_CASE_ ) , reverse=SCREAMING_SNAKE_CASE_ , key=relevance_logits.__getitem__ )
A: List[DPRReaderOutput] = []
for doc_id in sorted_docs:
A: List[str] = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
A: Dict = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
A: Union[str, Any] = sequence_ids.index(self.pad_token_id )
else:
A: int = len(SCREAMING_SNAKE_CASE_ )
A: Dict = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=SCREAMING_SNAKE_CASE_ , top_spans=SCREAMING_SNAKE_CASE_ , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=SCREAMING_SNAKE_CASE_ , start_index=SCREAMING_SNAKE_CASE_ , end_index=SCREAMING_SNAKE_CASE_ , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(SCREAMING_SNAKE_CASE_ ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , ) -> List[DPRSpanPrediction]:
'''simple docstring'''
A: Union[str, Any] = []
for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE_ ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
A: Any = sorted(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : x[1] , reverse=SCREAMING_SNAKE_CASE_ )
A: Dict = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
A: int = end_index - start_index + 1
assert length <= max_answer_length, f"""Span is too long: {length} > {max_answer_length}"""
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(SCREAMING_SNAKE_CASE_ ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(UpperCAmelCase_ )
class lowerCAmelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : Tuple = VOCAB_FILES_NAMES
UpperCamelCase_ : List[Any] = READER_PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Union[str, Any] = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : Dict = READER_PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ : Any = ["""input_ids""", """attention_mask"""]
UpperCamelCase_ : Optional[Any] = DPRReaderTokenizer
| 334
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ = {
'''configuration_efficientformer''': [
'''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EfficientFormerConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''EfficientFormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''EfficientFormerForImageClassification''',
'''EfficientFormerForImageClassificationWithTeacher''',
'''EfficientFormerModel''',
'''EfficientFormerPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFEfficientFormerForImageClassification''',
'''TFEfficientFormerForImageClassificationWithTeacher''',
'''TFEfficientFormerModel''',
'''TFEfficientFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientformer import EfficientFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 72
|
'''simple docstring'''
def snake_case ( UpperCAmelCase )-> list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(UpperCAmelCase , UpperCAmelCase ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(UpperCAmelCase )]
if __name__ == "__main__":
print(hexagonal_numbers(length=5))
print(hexagonal_numbers(length=1_0))
| 161
| 0
|
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
snake_case : str = logging.get_logger(__name__)
class snake_case_ (lowerCamelCase_ ):
UpperCAmelCase__ : Tuple = ['''pixel_values''']
def __init__( self :Any ,__snake_case :bool = True ,__snake_case :Optional[Dict[str, int]] = None ,__snake_case :PILImageResampling = PILImageResampling.BILINEAR ,__snake_case :bool = True ,__snake_case :Dict[str, int] = None ,__snake_case :bool = True ,__snake_case :Union[int, float] = 1 / 2_55 ,__snake_case :bool = True ,__snake_case :Optional[Union[float, List[float]]] = None ,__snake_case :Optional[Union[float, List[float]]] = None ,**__snake_case :Optional[int] ,) -> None:
super().__init__(**__snake_case )
a__ = size if size is not None else {'shortest_edge': 2_56}
a__ = get_size_dict(__snake_case ,default_to_square=__snake_case )
a__ = crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
a__ = get_size_dict(__snake_case ,param_name='crop_size' )
a__ = do_resize
a__ = size
a__ = resample
a__ = do_center_crop
a__ = crop_size
a__ = do_rescale
a__ = rescale_factor
a__ = do_normalize
a__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
a__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase__( self :Optional[Any] ,__snake_case :np.ndarray ,__snake_case :Dict[str, int] ,__snake_case :PILImageResampling = PILImageResampling.BICUBIC ,__snake_case :Optional[Union[str, ChannelDimension]] = None ,**__snake_case :str ,) -> np.ndarray:
a__ = get_size_dict(__snake_case ,default_to_square=__snake_case )
if "shortest_edge" not in size:
raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
a__ = get_resize_output_image_size(__snake_case ,size=size['shortest_edge'] ,default_to_square=__snake_case )
return resize(__snake_case ,size=__snake_case ,resample=__snake_case ,data_format=__snake_case ,**__snake_case )
def lowerCamelCase__( self :int ,__snake_case :np.ndarray ,__snake_case :Dict[str, int] ,__snake_case :Optional[Union[str, ChannelDimension]] = None ,**__snake_case :Tuple ,) -> np.ndarray:
a__ = get_size_dict(__snake_case )
if "height" not in size or "width" not in size:
raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
return center_crop(__snake_case ,size=(size['height'], size['width']) ,data_format=__snake_case ,**__snake_case )
def lowerCamelCase__( self :List[Any] ,__snake_case :np.ndarray ,__snake_case :float ,__snake_case :Optional[Union[str, ChannelDimension]] = None ,**__snake_case :str ) -> np.ndarray:
return rescale(__snake_case ,scale=__snake_case ,data_format=__snake_case ,**__snake_case )
def lowerCamelCase__( self :Optional[Any] ,__snake_case :np.ndarray ,__snake_case :Union[float, List[float]] ,__snake_case :Union[float, List[float]] ,__snake_case :Optional[Union[str, ChannelDimension]] = None ,**__snake_case :Tuple ,) -> np.ndarray:
return normalize(__snake_case ,mean=__snake_case ,std=__snake_case ,data_format=__snake_case ,**__snake_case )
def lowerCamelCase__( self :List[Any] ,__snake_case :ImageInput ,__snake_case :Optional[bool] = None ,__snake_case :Dict[str, int] = None ,__snake_case :PILImageResampling = None ,__snake_case :bool = None ,__snake_case :Dict[str, int] = None ,__snake_case :Optional[bool] = None ,__snake_case :Optional[float] = None ,__snake_case :Optional[bool] = None ,__snake_case :Optional[Union[float, List[float]]] = None ,__snake_case :Optional[Union[float, List[float]]] = None ,__snake_case :Optional[Union[str, TensorType]] = None ,__snake_case :Union[str, ChannelDimension] = ChannelDimension.FIRST ,**__snake_case :List[str] ,) -> str:
a__ = do_resize if do_resize is not None else self.do_resize
a__ = size if size is not None else self.size
a__ = get_size_dict(__snake_case ,default_to_square=__snake_case )
a__ = resample if resample is not None else self.resample
a__ = do_center_crop if do_center_crop is not None else self.do_center_crop
a__ = crop_size if crop_size is not None else self.crop_size
a__ = get_size_dict(__snake_case ,param_name='crop_size' )
a__ = do_rescale if do_rescale is not None else self.do_rescale
a__ = rescale_factor if rescale_factor is not None else self.rescale_factor
a__ = do_normalize if do_normalize is not None else self.do_normalize
a__ = image_mean if image_mean is not None else self.image_mean
a__ = image_std if image_std is not None else self.image_std
a__ = make_list_of_images(__snake_case )
if not valid_images(__snake_case ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
a__ = [to_numpy_array(__snake_case ) for image in images]
if do_resize:
a__ = [self.resize(image=__snake_case ,size=__snake_case ,resample=__snake_case ) for image in images]
if do_center_crop:
a__ = [self.center_crop(image=__snake_case ,size=__snake_case ) for image in images]
if do_rescale:
a__ = [self.rescale(image=__snake_case ,scale=__snake_case ) for image in images]
if do_normalize:
a__ = [self.normalize(image=__snake_case ,mean=__snake_case ,std=__snake_case ) for image in images]
a__ = [to_channel_dimension_format(__snake_case ,__snake_case ) for image in images]
a__ = {'pixel_values': images}
return BatchFeature(data=__snake_case ,tensor_type=__snake_case )
def lowerCamelCase__( self :List[Any] ,__snake_case :Any ,__snake_case :List[Tuple] = None ) -> Union[str, Any]:
a__ = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(__snake_case ) != len(__snake_case ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(__snake_case ):
a__ = target_sizes.numpy()
a__ = []
for idx in range(len(__snake_case ) ):
a__ = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode='bilinear' ,align_corners=__snake_case )
a__ = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(__snake_case )
else:
a__ = logits.argmax(dim=1 )
a__ = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 109
|
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case : int = logging.get_logger(__name__)
def __lowercase ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str ):
a__ = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'encoder.deit.blocks.{i}.norm1.weight', F'encoder.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'encoder.deit.blocks.{i}.norm1.bias', F'encoder.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.attn.proj.weight', F'encoder.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.attn.proj.bias', F'encoder.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.norm2.weight', F'encoder.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'encoder.deit.blocks.{i}.norm2.bias', F'encoder.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.mlp.fc1.weight', F'encoder.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.mlp.fc1.bias', F'encoder.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.mlp.fc2.weight', F'encoder.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'encoder.deit.blocks.{i}.mlp.fc2.bias', F'encoder.encoder.layer.{i}.output.dense.bias') )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
('encoder.deit.cls_token', 'encoder.embeddings.cls_token'),
('encoder.deit.pos_embed', 'encoder.embeddings.position_embeddings'),
('encoder.deit.patch_embed.proj.weight', 'encoder.embeddings.patch_embeddings.projection.weight'),
('encoder.deit.patch_embed.proj.bias', 'encoder.embeddings.patch_embeddings.projection.bias'),
('encoder.deit.norm.weight', 'encoder.layernorm.weight'),
('encoder.deit.norm.bias', 'encoder.layernorm.bias'),
] )
return rename_keys
def __lowercase ( __lowerCAmelCase : Dict , __lowerCAmelCase : Dict ):
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
a__ = state_dict.pop(F'encoder.deit.blocks.{i}.attn.qkv.weight' )
a__ = in_proj_weight[
: encoder_config.hidden_size, :
]
a__ = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
a__ = in_proj_weight[
-encoder_config.hidden_size :, :
]
def __lowercase ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] ):
a__ = dct.pop(__lowerCAmelCase )
a__ = val
def __lowercase ( __lowerCAmelCase : Optional[Any] ):
if "handwritten" in checkpoint_url:
a__ = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg' # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
a__ = 'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
a__ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw ).convert('RGB' )
return im
@torch.no_grad()
def __lowercase ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : int ):
a__ = ViTConfig(image_size=3_8_4 , qkv_bias=__lowerCAmelCase )
a__ = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
a__ = 7_6_8
elif "large" in checkpoint_url:
# use ViT-large encoder
a__ = 1_0_2_4
a__ = 4_0_9_6
a__ = 2_4
a__ = 1_6
a__ = 1_0_2_4
else:
raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL' )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
a__ = False
a__ = 'relu'
a__ = 1_0_2_4
a__ = True
a__ = False
a__ = False
# load HuggingFace model
a__ = ViTModel(__lowerCAmelCase , add_pooling_layer=__lowerCAmelCase )
a__ = TrOCRForCausalLM(__lowerCAmelCase )
a__ = VisionEncoderDecoderModel(encoder=__lowerCAmelCase , decoder=__lowerCAmelCase )
model.eval()
# load state_dict of original model, rename some keys
a__ = torch.hub.load_state_dict_from_url(__lowerCAmelCase , map_location='cpu' , check_hash=__lowerCAmelCase )['model']
a__ = create_rename_keys(__lowerCAmelCase , __lowerCAmelCase )
for src, dest in rename_keys:
rename_key(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
a__ = state_dict.pop(__lowerCAmelCase )
if key.startswith('decoder' ) and "output_projection" not in key:
a__ = val
else:
a__ = val
# load state dict
model.load_state_dict(__lowerCAmelCase )
# Check outputs on an image
a__ = ViTImageProcessor(size=encoder_config.image_size )
a__ = RobertaTokenizer.from_pretrained('roberta-large' )
a__ = TrOCRProcessor(__lowerCAmelCase , __lowerCAmelCase )
a__ = processor(images=prepare_img(__lowerCAmelCase ) , return_tensors='pt' ).pixel_values
# verify logits
a__ = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
a__ = model(pixel_values=__lowerCAmelCase , decoder_input_ids=__lowerCAmelCase )
a__ = outputs.logits
a__ = torch.Size([1, 1, 5_0_2_6_5] )
if "trocr-base-handwritten" in checkpoint_url:
a__ = torch.tensor(
[-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311] )
elif "trocr-large-handwritten" in checkpoint_url:
a__ = torch.tensor(
[-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170] )
elif "trocr-base-printed" in checkpoint_url:
a__ = torch.tensor(
[-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210] )
elif "trocr-large-printed" in checkpoint_url:
a__ = torch.tensor(
[-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :1_0] , __lowerCAmelCase , atol=1E-3 ), "First elements of logits not as expected"
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
print(F'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(__lowerCAmelCase )
print(F'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
snake_case : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_url''',
default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
type=str,
help='''URL to the original PyTorch checkpoint (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
snake_case : int = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 109
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXLOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaXLForCausalLM',
'XLMRobertaXLForMaskedLM',
'XLMRobertaXLForMultipleChoice',
'XLMRobertaXLForQuestionAnswering',
'XLMRobertaXLForSequenceClassification',
'XLMRobertaXLForTokenClassification',
'XLMRobertaXLModel',
'XLMRobertaXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
a__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 235
|
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 305
| 0
|
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowerCAmelCase : int = logging.getLogger(__name__)
@dataclass
class __lowercase :
"""simple docstring"""
_UpperCAmelCase : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase : Optional[str] = field(
default=UpperCAmelCase_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase : Optional[str] = field(
default='''NER''' , metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
_UpperCAmelCase : Optional[str] = field(
default=UpperCAmelCase_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase : bool = field(default=UpperCAmelCase_ , metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCAmelCase : Optional[str] = field(
default=UpperCAmelCase_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
@dataclass
class __lowercase :
"""simple docstring"""
_UpperCAmelCase : str = field(
metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
_UpperCAmelCase : Optional[str] = field(
default=UpperCAmelCase_ , metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''} , )
_UpperCAmelCase : int = field(
default=128 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCAmelCase : bool = field(
default=UpperCAmelCase_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def A_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE_: Optional[int] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Dict = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
" --overwrite_output_dir to overcome." )
SCREAMING_SNAKE_CASE_: Any = import_module("tasks" )
try:
SCREAMING_SNAKE_CASE_: Union[str, Any] = getattr(_UpperCAmelCase , model_args.task_type )
SCREAMING_SNAKE_CASE_: TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}" )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , _UpperCAmelCase )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
SCREAMING_SNAKE_CASE_: List[str] = token_classification_task.get_labels(data_args.labels )
SCREAMING_SNAKE_CASE_: Dict[int, str] = dict(enumerate(_UpperCAmelCase ) )
SCREAMING_SNAKE_CASE_: Any = len(_UpperCAmelCase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE_: Tuple = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCAmelCase , idalabel=_UpperCAmelCase , labelaid={label: i for i, label in enumerate(_UpperCAmelCase )} , cache_dir=model_args.cache_dir , )
SCREAMING_SNAKE_CASE_: Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
SCREAMING_SNAKE_CASE_: Any = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=_UpperCAmelCase , cache_dir=model_args.cache_dir , )
# Get datasets
SCREAMING_SNAKE_CASE_: Union[str, Any] = (
TokenClassificationDataset(
token_classification_task=_UpperCAmelCase , data_dir=data_args.data_dir , tokenizer=_UpperCAmelCase , labels=_UpperCAmelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
SCREAMING_SNAKE_CASE_: Optional[Any] = (
TokenClassificationDataset(
token_classification_task=_UpperCAmelCase , data_dir=data_args.data_dir , tokenizer=_UpperCAmelCase , labels=_UpperCAmelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(_UpperCAmelCase , _UpperCAmelCase ) -> Tuple[List[int], List[int]]:
SCREAMING_SNAKE_CASE_: List[Any] = np.argmax(_UpperCAmelCase , axis=2 )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str = preds.shape
SCREAMING_SNAKE_CASE_: List[Any] = [[] for _ in range(_UpperCAmelCase )]
SCREAMING_SNAKE_CASE_: int = [[] for _ in range(_UpperCAmelCase )]
for i in range(_UpperCAmelCase ):
for j in range(_UpperCAmelCase ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(_UpperCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[str] = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(_UpperCAmelCase , _UpperCAmelCase ),
"precision": precision_score(_UpperCAmelCase , _UpperCAmelCase ),
"recall": recall_score(_UpperCAmelCase , _UpperCAmelCase ),
"f1": fa_score(_UpperCAmelCase , _UpperCAmelCase ),
}
# Data collator
SCREAMING_SNAKE_CASE_: Optional[Any] = DataCollatorWithPadding(_UpperCAmelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
SCREAMING_SNAKE_CASE_: Union[str, Any] = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=_UpperCAmelCase , eval_dataset=_UpperCAmelCase , compute_metrics=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
SCREAMING_SNAKE_CASE_: Dict = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
SCREAMING_SNAKE_CASE_: Union[str, Any] = trainer.evaluate()
SCREAMING_SNAKE_CASE_: Tuple = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , _UpperCAmelCase , _UpperCAmelCase )
writer.write("%s = %s\n" % (key, value) )
results.update(_UpperCAmelCase )
# Predict
if training_args.do_predict:
SCREAMING_SNAKE_CASE_: Optional[int] = TokenClassificationDataset(
token_classification_task=_UpperCAmelCase , data_dir=data_args.data_dir , tokenizer=_UpperCAmelCase , labels=_UpperCAmelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: int = trainer.predict(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[Any] = align_predictions(_UpperCAmelCase , _UpperCAmelCase )
SCREAMING_SNAKE_CASE_: List[Any] = os.path.join(training_args.output_dir , "test_results.txt" )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , "w" ) as writer:
for key, value in metrics.items():
logger.info(" %s = %s" , _UpperCAmelCase , _UpperCAmelCase )
writer.write("%s = %s\n" % (key, value) )
# Save predictions
SCREAMING_SNAKE_CASE_: Union[str, Any] = os.path.join(training_args.output_dir , "test_predictions.txt" )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , "w" ) as writer:
with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f:
token_classification_task.write_predictions_to_file(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return results
def A_ ( _UpperCAmelCase ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 127
|
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
lowerCAmelCase : List[str] = """Run commands across TPU VMs for initial setup before running `accelerate launch`."""
def A_ ( _UpperCAmelCase=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE_: Optional[Any] = subparsers.add_parser("tpu-config" , description=_description )
else:
SCREAMING_SNAKE_CASE_: Optional[int] = argparse.ArgumentParser("Accelerate tpu-config command" , description=_description )
# Core arguments
SCREAMING_SNAKE_CASE_: Any = parser.add_argument_group(
"Config Arguments" , "Arguments that can be configured through `accelerate config`." )
config_args.add_argument(
"--config_file" , type=_UpperCAmelCase , default=_UpperCAmelCase , help="Path to the config file to use for accelerate." , )
config_args.add_argument(
"--tpu_name" , default=_UpperCAmelCase , help="The name of the TPU to use. If not specified, will use the TPU specified in the config file." , )
config_args.add_argument(
"--tpu_zone" , default=_UpperCAmelCase , help="The zone of the TPU to use. If not specified, will use the zone specified in the config file." , )
SCREAMING_SNAKE_CASE_: Tuple = parser.add_argument_group("TPU Arguments" , "Arguments for options ran inside the TPU." )
pod_args.add_argument(
"--use_alpha" , action="store_true" , help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`." , )
pod_args.add_argument(
"--command_file" , default=_UpperCAmelCase , help="The path to the file containing the commands to run on the pod on startup." , )
pod_args.add_argument(
"--command" , action="append" , nargs="+" , help="A command to run on the pod. Can be passed multiple times." , )
pod_args.add_argument(
"--install_accelerate" , action="store_true" , help="Whether to install accelerate on the pod. Defaults to False." , )
pod_args.add_argument(
"--accelerate_version" , default="latest" , help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub." , )
pod_args.add_argument(
"--debug" , action="store_true" , help="If set, will print the command that would be run instead of running it." )
if subparsers is not None:
parser.set_defaults(func=_UpperCAmelCase )
return parser
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Tuple = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(_UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
SCREAMING_SNAKE_CASE_: Any = defaults.command_file
if not args.command and defaults.commands is not None:
SCREAMING_SNAKE_CASE_: int = defaults.commands
if not args.tpu_name:
SCREAMING_SNAKE_CASE_: List[str] = defaults.tpu_name
if not args.tpu_zone:
SCREAMING_SNAKE_CASE_: Dict = defaults.tpu_zone
if args.accelerate_version == "dev":
SCREAMING_SNAKE_CASE_: Tuple = "git+https://github.com/huggingface/accelerate.git"
elif args.accelerate_version == "latest":
SCREAMING_SNAKE_CASE_: str = "accelerate -U"
elif isinstance(parse(args.accelerate_version ) , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = f"accelerate=={args.accelerate_version}"
if not args.command_file and not args.command:
raise ValueError("You must specify either a command file or a command to run on the pod." )
if args.command_file:
with open(args.command_file , "r" ) as f:
SCREAMING_SNAKE_CASE_: Dict = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
SCREAMING_SNAKE_CASE_: Dict = ["cd /usr/share"]
if args.install_accelerate:
new_cmd += [f"pip install {args.accelerate_version}"]
new_cmd += args.command
SCREAMING_SNAKE_CASE_: List[str] = "; ".join(_UpperCAmelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
SCREAMING_SNAKE_CASE_: int = ["gcloud"]
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f"Running {' '.join(_UpperCAmelCase )}" )
return
subprocess.run(_UpperCAmelCase )
print("Successfully setup pod." )
def A_ ( ):
SCREAMING_SNAKE_CASE_: List[str] = tpu_command_parser()
SCREAMING_SNAKE_CASE_: List[Any] = parser.parse_args()
tpu_command_launcher(_UpperCAmelCase )
| 127
| 1
|
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> tuple[float, float]:
if not all(isinstance(_lowerCamelCase , (float, int) ) for val in locals().values() ):
lowercase : str = f"Input values must either be float or int: {list(locals().values() )}"
raise TypeError(_lowerCamelCase )
lowercase : List[str] = ((x * distance) / (z + distance)) * scale
lowercase : List[Any] = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> tuple[float, float, float]:
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("""Axis must be a str""" )
lowercase : str = locals()
del input_variables["axis"]
if not all(isinstance(_lowerCamelCase , (float, int) ) for val in input_variables.values() ):
lowercase : Dict = (
"Input values except axis must either be float or int: "
f"{list(input_variables.values() )}"
)
raise TypeError(_lowerCamelCase )
lowercase : Optional[Any] = (angle % 360) / 450 * 180 / math.pi
if axis == "z":
lowercase : Tuple = x * math.cos(_lowerCamelCase ) - y * math.sin(_lowerCamelCase )
lowercase : Union[str, Any] = y * math.cos(_lowerCamelCase ) + x * math.sin(_lowerCamelCase )
lowercase : Any = z
elif axis == "x":
lowercase : Dict = y * math.cos(_lowerCamelCase ) - z * math.sin(_lowerCamelCase )
lowercase : Any = z * math.cos(_lowerCamelCase ) + y * math.sin(_lowerCamelCase )
lowercase : List[str] = x
elif axis == "y":
lowercase : Any = x * math.cos(_lowerCamelCase ) - z * math.sin(_lowerCamelCase )
lowercase : Any = z * math.cos(_lowerCamelCase ) + x * math.sin(_lowerCamelCase )
lowercase : Dict = y
else:
raise ValueError("""not a valid axis, choose one of 'x', 'y', 'z'""" )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
print(F'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''')
| 20
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, Any] = '▁'
lowercase : Tuple = {'vocab_file': 'spiece.model'}
lowercase : Dict = {
'vocab_file': {
'google/reformer-crime-and-punishment': (
'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
)
}
}
lowercase : Any = {
'google/reformer-crime-and-punishment': 524288,
}
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
_A = VOCAB_FILES_NAMES
_A = PRETRAINED_VOCAB_FILES_MAP
_A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A = ['input_ids', 'attention_mask']
def __init__( self :int , a :List[Any] , a :Tuple="</s>" , a :str="<unk>" , a :Dict=[] , a :Optional[Dict[str, Any]] = None , **a :Union[str, Any] , ) -> None:
__UpperCamelCase : str = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=a , unk_token=a , additional_special_tokens=a , sp_model_kwargs=self.sp_model_kwargs , **a , )
__UpperCamelCase : Optional[Any] = vocab_file
__UpperCamelCase : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(a )
@property
def _lowerCamelCase ( self :Optional[Any] ) -> Any:
return self.sp_model.get_piece_size()
def _lowerCamelCase ( self :Optional[int] ) -> Dict[str, int]:
__UpperCamelCase : str = {self.convert_ids_to_tokens(a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self :str ) -> List[str]:
__UpperCamelCase : Union[str, Any] = self.__dict__.copy()
__UpperCamelCase : Optional[Any] = None
return state
def __setstate__( self :int , a :List[str] ) -> int:
__UpperCamelCase : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
__UpperCamelCase : int = {}
__UpperCamelCase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _lowerCamelCase ( self :List[Any] , a :str ) -> List[str]:
return self.sp_model.encode(a , out_type=a )
def _lowerCamelCase ( self :Optional[int] , a :Optional[Any] ) -> str:
return self.sp_model.piece_to_id(a )
def _lowerCamelCase ( self :Dict , a :Union[str, Any] ) -> Optional[int]:
if index < self.sp_model.get_piece_size():
__UpperCamelCase : Optional[int] = self.sp_model.IdToPiece(a )
return token
def _lowerCamelCase ( self :Dict , a :List[Any] ) -> Dict:
__UpperCamelCase : Optional[int] = []
__UpperCamelCase : str = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(a ) + token
__UpperCamelCase : List[Any] = []
else:
current_sub_tokens.append(a )
out_string += self.sp_model.decode(a )
return out_string.strip()
def _lowerCamelCase ( self :Optional[Any] , a :str , a :Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(a ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
__UpperCamelCase : List[Any] = os.path.join(
a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , a )
elif not os.path.isfile(self.vocab_file ):
with open(a , "wb" ) as fi:
__UpperCamelCase : int = self.sp_model.serialized_model_proto()
fi.write(a )
return (out_vocab_file,)
| 232
| 0
|
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
__lowerCamelCase = logging.get_logger(__name__)
class UpperCAmelCase ( _lowerCAmelCase ):
A__ : Optional[int] = ["pixel_values"]
def __init__(self : Tuple , snake_case__ : str = True , snake_case__ : Union[str, Any] = None , snake_case__ : int = PILImageResampling.BILINEAR , snake_case__ : List[str] = True , snake_case__ : List[Any] = None , snake_case__ : int = True , snake_case__ : List[Any] = 1 / 2_55 , snake_case__ : Optional[Any] = True , snake_case__ : int = None , snake_case__ : Optional[Any] = None , **snake_case__ : Tuple , ) -> None:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = size if size is not None else {"""shortest_edge""": 2_56}
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : Tuple = crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
snake_case : str = get_size_dict(SCREAMING_SNAKE_CASE_ , param_name="crop_size" )
snake_case : Union[str, Any] = do_resize
snake_case : List[str] = size
snake_case : List[str] = resample
snake_case : Optional[int] = do_center_crop
snake_case : int = crop_size
snake_case : List[Any] = do_rescale
snake_case : str = rescale_factor
snake_case : Dict = do_normalize
snake_case : int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
snake_case : str = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _SCREAMING_SNAKE_CASE (self : int , snake_case__ : List[Any] , snake_case__ : str , snake_case__ : Optional[int] = PILImageResampling.BICUBIC , snake_case__ : Dict = None , **snake_case__ : List[Any] , ) -> np.ndarray:
'''simple docstring'''
snake_case : int = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
if "shortest_edge" not in size:
raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
snake_case : Any = get_resize_output_image_size(SCREAMING_SNAKE_CASE_ , size=size["shortest_edge"] , default_to_square=SCREAMING_SNAKE_CASE_ )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE (self : Any , snake_case__ : int , snake_case__ : Union[str, Any] , snake_case__ : List[Any] = None , **snake_case__ : List[Any] , ) -> np.ndarray:
'''simple docstring'''
snake_case : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}""" )
return center_crop(SCREAMING_SNAKE_CASE_ , size=(size["height"], size["width"]) , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE (self : str , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : List[str] = None , **snake_case__ : Any ) -> np.ndarray:
'''simple docstring'''
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE (self : List[str] , snake_case__ : Tuple , snake_case__ : str , snake_case__ : str , snake_case__ : Dict = None , **snake_case__ : List[Any] , ) -> np.ndarray:
'''simple docstring'''
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE (self : int , snake_case__ : Union[str, Any] , snake_case__ : int = None , snake_case__ : Any = None , snake_case__ : str = None , snake_case__ : List[Any] = None , snake_case__ : str = None , snake_case__ : List[Any] = None , snake_case__ : int = None , snake_case__ : List[str] = None , snake_case__ : Optional[Any] = None , snake_case__ : Any = None , snake_case__ : Optional[Any] = None , snake_case__ : Dict = ChannelDimension.FIRST , **snake_case__ : int , ) -> Any:
'''simple docstring'''
snake_case : str = do_resize if do_resize is not None else self.do_resize
snake_case : List[str] = size if size is not None else self.size
snake_case : Any = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
snake_case : List[Any] = resample if resample is not None else self.resample
snake_case : int = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case : Optional[int] = crop_size if crop_size is not None else self.crop_size
snake_case : Optional[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ , param_name="crop_size" )
snake_case : Tuple = do_rescale if do_rescale is not None else self.do_rescale
snake_case : str = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case : Optional[int] = do_normalize if do_normalize is not None else self.do_normalize
snake_case : Tuple = image_mean if image_mean is not None else self.image_mean
snake_case : str = image_std if image_std is not None else self.image_std
snake_case : Union[str, Any] = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
snake_case : Any = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
snake_case : int = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_center_crop:
snake_case : Optional[int] = [self.center_crop(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
snake_case : Dict = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
snake_case : Any = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : List[Any] = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
snake_case : Optional[Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def _SCREAMING_SNAKE_CASE (self : int , snake_case__ : List[Any] , snake_case__ : Optional[int] = None ) -> Tuple:
'''simple docstring'''
snake_case : Optional[Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
snake_case : int = target_sizes.numpy()
snake_case : Optional[int] = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
snake_case : List[str] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="bilinear" , align_corners=SCREAMING_SNAKE_CASE_ )
snake_case : Dict = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
snake_case : str = logits.argmax(dim=1 )
snake_case : Union[str, Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 365
|
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__lowerCamelCase = Mapping[str, np.ndarray]
__lowerCamelCase = Mapping[str, Any] # Is a nested dict.
__lowerCamelCase = 0.01
@dataclasses.dataclass(frozen=A_ )
class UpperCAmelCase :
A__ : np.ndarray # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
A__ : np.ndarray # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
A__ : np.ndarray # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
A__ : np.ndarray # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
A__ : np.ndarray # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
A__ : Optional[np.ndarray] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
A__ : Optional[str] = None
# Templates used to generate this protein (prediction-only)
A__ : Optional[Sequence[str]] = None
# Chain corresponding to each parent
A__ : Optional[Sequence[int]] = None
def UpperCamelCase ( __lowerCamelCase : str ):
snake_case : Dict = r"(\[[A-Z]+\]\n)"
snake_case : List[str] = [tag.strip() for tag in re.split(__lowerCamelCase , __lowerCamelCase ) if len(__lowerCamelCase ) > 0]
snake_case : Iterator[Tuple[str, List[str]]] = zip(tags[0::2] , [l.split("\n" ) for l in tags[1::2]] )
snake_case : List[str] = ["N", "CA", "C"]
snake_case : str = None
snake_case : str = None
snake_case : Tuple = None
for g in groups:
if "[PRIMARY]" == g[0]:
snake_case : Tuple = g[1][0].strip()
for i in range(len(__lowerCamelCase ) ):
if seq[i] not in residue_constants.restypes:
snake_case : Optional[Any] = "X" # FIXME: strings are immutable
snake_case : Optional[int] = np.array(
[residue_constants.restype_order.get(__lowerCamelCase , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
snake_case : List[List[float]] = []
for axis in range(3 ):
tertiary.append(list(map(__lowerCamelCase , g[1][axis].split() ) ) )
snake_case : Union[str, Any] = np.array(__lowerCamelCase )
snake_case : str = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(__lowerCamelCase ):
snake_case : Dict = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
snake_case : int = np.array(list(map({"-": 0, "+": 1}.get , g[1][0].strip() ) ) )
snake_case : List[str] = np.zeros(
(
len(__lowerCamelCase ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(__lowerCamelCase ):
snake_case : Any = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=__lowerCamelCase , atom_mask=__lowerCamelCase , aatype=__lowerCamelCase , residue_index=np.arange(len(__lowerCamelCase ) ) , b_factors=__lowerCamelCase , )
def UpperCamelCase ( __lowerCamelCase : Protein , __lowerCamelCase : int = 0 ):
snake_case : List[str] = []
snake_case : str = prot.remark
if remark is not None:
pdb_headers.append(f"""REMARK {remark}""" )
snake_case : Union[str, Any] = prot.parents
snake_case : Dict = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
snake_case : Tuple = [p for i, p in zip(__lowerCamelCase , __lowerCamelCase ) if i == chain_id]
if parents is None or len(__lowerCamelCase ) == 0:
snake_case : int = ["N/A"]
pdb_headers.append(f"""PARENT {' '.join(__lowerCamelCase )}""" )
return pdb_headers
def UpperCamelCase ( __lowerCamelCase : Protein , __lowerCamelCase : str ):
snake_case : List[str] = []
snake_case : Any = pdb_str.split("\n" )
snake_case : int = prot.remark
if remark is not None:
out_pdb_lines.append(f"""REMARK {remark}""" )
snake_case : List[List[str]]
if prot.parents is not None and len(prot.parents ) > 0:
snake_case : Optional[Any] = []
if prot.parents_chain_index is not None:
snake_case : Dict[str, List[str]] = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(__lowerCamelCase ) , [] )
parent_dict[str(__lowerCamelCase )].append(__lowerCamelCase )
snake_case : List[str] = max([int(__lowerCamelCase ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
snake_case : Optional[Any] = parent_dict.get(str(__lowerCamelCase ) , ["N/A"] )
parents_per_chain.append(__lowerCamelCase )
else:
parents_per_chain.append(list(prot.parents ) )
else:
snake_case : Optional[Any] = [["N/A"]]
def make_parent_line(__lowerCamelCase : Sequence[str] ) -> str:
return f"""PARENT {' '.join(__lowerCamelCase )}"""
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
snake_case : List[Any] = 0
for i, l in enumerate(__lowerCamelCase ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(__lowerCamelCase )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(__lowerCamelCase ):
snake_case : int = parents_per_chain[chain_counter]
else:
snake_case : Any = ["N/A"]
out_pdb_lines.append(make_parent_line(__lowerCamelCase ) )
return "\n".join(__lowerCamelCase )
def UpperCamelCase ( __lowerCamelCase : Protein ):
snake_case : str = residue_constants.restypes + ["X"]
def res_atoa(__lowerCamelCase : int ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , "UNK" )
snake_case : List[Any] = residue_constants.atom_types
snake_case : List[str] = []
snake_case : Any = prot.atom_mask
snake_case : Any = prot.aatype
snake_case : Dict = prot.atom_positions
snake_case : List[str] = prot.residue_index.astype(np.intaa )
snake_case : Dict = prot.b_factors
snake_case : Tuple = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError("Invalid aatypes." )
snake_case : Any = get_pdb_headers(__lowerCamelCase )
if len(__lowerCamelCase ) > 0:
pdb_lines.extend(__lowerCamelCase )
snake_case : Dict = aatype.shape[0]
snake_case : Tuple = 1
snake_case : Any = 0
snake_case : Union[str, Any] = string.ascii_uppercase
snake_case : int = None
# Add all atom sites.
for i in range(__lowerCamelCase ):
snake_case : List[Any] = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(__lowerCamelCase , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
snake_case : Any = "ATOM"
snake_case : str = atom_name if len(__lowerCamelCase ) == 4 else f""" {atom_name}"""
snake_case : Optional[Any] = ""
snake_case : Dict = ""
snake_case : Optional[Any] = 1.00
snake_case : str = atom_name[0] # Protein supports only C, N, O, S, this works.
snake_case : Dict = ""
snake_case : Any = "A"
if chain_index is not None:
snake_case : str = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
snake_case : List[str] = (
f"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
f"""{res_name_a:>3} {chain_tag:>1}"""
f"""{residue_index[i]:>4}{insertion_code:>1} """
f"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
f"""{occupancy:>6.2f}{b_factor:>6.2f} """
f"""{element:>2}{charge:>2}"""
)
pdb_lines.append(__lowerCamelCase )
atom_index += 1
snake_case : Optional[int] = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
snake_case : Any = True
snake_case : Tuple = chain_index[i + 1]
if should_terminate:
# Close the chain.
snake_case : Optional[Any] = "TER"
snake_case : Optional[int] = (
f"""{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
)
pdb_lines.append(__lowerCamelCase )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(__lowerCamelCase , __lowerCamelCase ) )
pdb_lines.append("END" )
pdb_lines.append("" )
return "\n".join(__lowerCamelCase )
def UpperCamelCase ( __lowerCamelCase : Protein ):
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def UpperCamelCase ( __lowerCamelCase : FeatureDict , __lowerCamelCase : ModelOutput , __lowerCamelCase : Optional[np.ndarray] = None , __lowerCamelCase : Optional[np.ndarray] = None , __lowerCamelCase : Optional[str] = None , __lowerCamelCase : Optional[Sequence[str]] = None , __lowerCamelCase : Optional[Sequence[int]] = None , ):
return Protein(
aatype=features["aatype"] , atom_positions=result["final_atom_positions"] , atom_mask=result["final_atom_mask"] , residue_index=features["residue_index"] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"] ) , chain_index=__lowerCamelCase , remark=__lowerCamelCase , parents=__lowerCamelCase , parents_chain_index=__lowerCamelCase , )
| 10
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__UpperCAmelCase = {
'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
'ErnieForCausalLM',
'ErnieForMaskedLM',
'ErnieForMultipleChoice',
'ErnieForNextSentencePrediction',
'ErnieForPreTraining',
'ErnieForQuestionAnswering',
'ErnieForSequenceClassification',
'ErnieForTokenClassification',
'ErnieModel',
'ErniePreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 29
|
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
_lowerCamelCase ="\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n"
_lowerCamelCase ="\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"
_lowerCamelCase ="\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: \"c\" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric('mauve')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
"""simple docstring"""
def _lowerCAmelCase ( self : Tuple ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,homepage='https://github.com/krishnap25/mauve' ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'predictions': datasets.Value('string' ,id='sequence' ),
'references': datasets.Value('string' ,id='sequence' ),
} ) ,codebase_urls=['https://github.com/krishnap25/mauve'] ,reference_urls=[
'https://arxiv.org/abs/2102.01454',
'https://github.com/krishnap25/mauve',
] ,)
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int] ,snake_case : str ,snake_case : List[str]=None ,snake_case : str=None ,snake_case : int=None ,snake_case : Union[str, Any]=None ,snake_case : Optional[int]="auto" ,snake_case : List[str]=-1 ,snake_case : Union[str, Any]=0.9 ,snake_case : Tuple=5 ,snake_case : Union[str, Any]=500 ,snake_case : Union[str, Any]="gpt2-large" ,snake_case : Union[str, Any]=-1 ,snake_case : Optional[Any]=1024 ,snake_case : Optional[Any]=25 ,snake_case : List[str]=5 ,snake_case : List[str]=True ,snake_case : Optional[Any]=25 ,):
SCREAMING_SNAKE_CASE =compute_mauve(
p_text=snake_case ,q_text=snake_case ,p_features=snake_case ,q_features=snake_case ,p_tokens=snake_case ,q_tokens=snake_case ,num_buckets=snake_case ,pca_max_data=snake_case ,kmeans_explained_var=snake_case ,kmeans_num_redo=snake_case ,kmeans_max_iter=snake_case ,featurize_model_name=snake_case ,device_id=snake_case ,max_text_length=snake_case ,divergence_curve_discretization_size=snake_case ,mauve_scaling_factor=snake_case ,verbose=snake_case ,seed=snake_case ,)
return out
| 334
| 0
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : int = logging.get_logger(__name__)
_lowercase : Optional[Any] = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : Union[str, Any] = "openai-gpt"
__magic_name__ : Optional[int] = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Optional[int] , lowerCAmelCase : Optional[Any]=40478 , lowerCAmelCase : str=512 , lowerCAmelCase : List[Any]=768 , lowerCAmelCase : Tuple=12 , lowerCAmelCase : int=12 , lowerCAmelCase : List[str]="gelu" , lowerCAmelCase : List[str]=0.1 , lowerCAmelCase : List[Any]=0.1 , lowerCAmelCase : Dict=0.1 , lowerCAmelCase : Tuple=1E-5 , lowerCAmelCase : Tuple=0.02 , lowerCAmelCase : Optional[int]="cls_index" , lowerCAmelCase : Dict=True , lowerCAmelCase : Optional[int]=None , lowerCAmelCase : Dict=True , lowerCAmelCase : int=0.1 , **lowerCAmelCase : Optional[int] , )-> str:
"""simple docstring"""
UpperCAmelCase = vocab_size
UpperCAmelCase = n_positions
UpperCAmelCase = n_embd
UpperCAmelCase = n_layer
UpperCAmelCase = n_head
UpperCAmelCase = afn
UpperCAmelCase = resid_pdrop
UpperCAmelCase = embd_pdrop
UpperCAmelCase = attn_pdrop
UpperCAmelCase = layer_norm_epsilon
UpperCAmelCase = initializer_range
UpperCAmelCase = summary_type
UpperCAmelCase = summary_use_proj
UpperCAmelCase = summary_activation
UpperCAmelCase = summary_first_dropout
UpperCAmelCase = summary_proj_to_labels
super().__init__(**lowerCAmelCase )
| 91
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase__( lowerCAmelCase , lowerCAmelCase , unittest.TestCase ):
__magic_name__ : List[str] = StableDiffusionSAGPipeline
__magic_name__ : str = TEXT_TO_IMAGE_PARAMS
__magic_name__ : Any = TEXT_TO_IMAGE_BATCH_PARAMS
__magic_name__ : Optional[int] = TEXT_TO_IMAGE_IMAGE_PARAMS
__magic_name__ : List[Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
__magic_name__ : str = False
def a__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
UpperCAmelCase = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=lowerCAmelCase , set_alpha_to_one=lowerCAmelCase , )
torch.manual_seed(0 )
UpperCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
UpperCAmelCase = CLIPTextModel(lowerCAmelCase )
UpperCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def a__( self : Optional[Any] , lowerCAmelCase : str , lowerCAmelCase : Tuple=0 )-> str:
"""simple docstring"""
if str(lowerCAmelCase ).startswith('''mps''' ):
UpperCAmelCase = torch.manual_seed(lowerCAmelCase )
else:
UpperCAmelCase = torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
UpperCAmelCase = {
'''prompt''': '''.''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 1.0,
'''sag_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def a__( self : Any )-> List[str]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class UpperCamelCase__( unittest.TestCase ):
def a__( self : Union[str, Any] )-> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
UpperCAmelCase = StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
UpperCAmelCase = sag_pipe.to(lowerCAmelCase )
sag_pipe.set_progress_bar_config(disable=lowerCAmelCase )
UpperCAmelCase = '''.'''
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = sag_pipe(
[prompt] , generator=lowerCAmelCase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' )
UpperCAmelCase = output.images
UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
UpperCAmelCase = np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def a__( self : int )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
UpperCAmelCase = sag_pipe.to(lowerCAmelCase )
sag_pipe.set_progress_bar_config(disable=lowerCAmelCase )
UpperCAmelCase = '''.'''
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = sag_pipe(
[prompt] , generator=lowerCAmelCase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' )
UpperCAmelCase = output.images
UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
UpperCAmelCase = np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def a__( self : Optional[Any] )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
UpperCAmelCase = sag_pipe.to(lowerCAmelCase )
sag_pipe.set_progress_bar_config(disable=lowerCAmelCase )
UpperCAmelCase = '''.'''
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = sag_pipe(
[prompt] , width=768 , height=512 , generator=lowerCAmelCase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' , )
UpperCAmelCase = output.images
assert image.shape == (1, 512, 768, 3)
| 91
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A: str = logging.get_logger(__name__)
A: List[str] = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ , UpperCAmelCase__ ):
__lowerCAmelCase : List[Any] = 'focalnet'
def __init__( self , _SCREAMING_SNAKE_CASE=224 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=96 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=[192, 384, 768, 768] , _SCREAMING_SNAKE_CASE=[2, 2, 6, 2] , _SCREAMING_SNAKE_CASE=[2, 2, 2, 2] , _SCREAMING_SNAKE_CASE=[3, 3, 3, 3] , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=4.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=1E-4 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=1E-5 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , ) -> Dict:
'''simple docstring'''
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Union[str, Any] = image_size
UpperCAmelCase : Union[str, Any] = patch_size
UpperCAmelCase : Any = num_channels
UpperCAmelCase : Optional[int] = embed_dim
UpperCAmelCase : List[str] = use_conv_embed
UpperCAmelCase : Dict = hidden_sizes
UpperCAmelCase : Any = depths
UpperCAmelCase : str = focal_levels
UpperCAmelCase : Tuple = focal_windows
UpperCAmelCase : Tuple = hidden_act
UpperCAmelCase : Dict = mlp_ratio
UpperCAmelCase : List[Any] = hidden_dropout_prob
UpperCAmelCase : Dict = drop_path_rate
UpperCAmelCase : Tuple = use_layerscale
UpperCAmelCase : Dict = layerscale_value
UpperCAmelCase : Optional[int] = use_post_layernorm
UpperCAmelCase : Dict = use_post_layernorm_in_modulation
UpperCAmelCase : Tuple = normalize_modulator
UpperCAmelCase : List[Any] = initializer_range
UpperCAmelCase : Union[str, Any] = layer_norm_eps
UpperCAmelCase : List[Any] = encoder_stride
UpperCAmelCase : Optional[Any] = ["""stem"""] + [F"stage{idx}" for idx in range(1 , len(self.depths ) + 1 )]
UpperCAmelCase , UpperCAmelCase : Any = get_aligned_output_features_output_indices(
out_features=_SCREAMING_SNAKE_CASE , out_indices=_SCREAMING_SNAKE_CASE , stage_names=self.stage_names )
| 109
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A: List[str] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A: Tuple = ["DPTFeatureExtractor"]
A: int = ["DPTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A: Optional[Any] = [
"DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DPTForDepthEstimation",
"DPTForSemanticSegmentation",
"DPTModel",
"DPTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
A: str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 109
| 1
|
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__=99 , snake_case__=13 , snake_case__=16 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=True , snake_case__=False , snake_case__=True , snake_case__=2 , snake_case__=32 , snake_case__=4 , snake_case__=4 , snake_case__=30 , snake_case__=0 , snake_case__=1 , snake_case__=2 , snake_case__=None , ):
"""simple docstring"""
lowerCAmelCase : List[Any] = parent
lowerCAmelCase : Union[str, Any] = batch_size
lowerCAmelCase : Any = decoder_seq_length
# For common tests
lowerCAmelCase : Tuple = self.decoder_seq_length
lowerCAmelCase : Dict = is_training
lowerCAmelCase : List[str] = use_attention_mask
lowerCAmelCase : Optional[Any] = use_labels
lowerCAmelCase : Dict = vocab_size
lowerCAmelCase : Tuple = d_model
lowerCAmelCase : Tuple = d_model
lowerCAmelCase : List[str] = decoder_layers
lowerCAmelCase : str = decoder_layers
lowerCAmelCase : List[str] = decoder_ffn_dim
lowerCAmelCase : Tuple = decoder_attention_heads
lowerCAmelCase : str = decoder_attention_heads
lowerCAmelCase : Optional[Any] = eos_token_id
lowerCAmelCase : int = bos_token_id
lowerCAmelCase : Optional[int] = pad_token_id
lowerCAmelCase : Optional[int] = decoder_start_token_id
lowerCAmelCase : int = use_cache
lowerCAmelCase : Dict = max_position_embeddings
lowerCAmelCase : Tuple = None
lowerCAmelCase : Any = decoder_seq_length
lowerCAmelCase : int = 2
lowerCAmelCase : Dict = 1
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
lowerCAmelCase : Union[str, Any] = None
if self.use_attention_mask:
lowerCAmelCase : str = ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
lowerCAmelCase : Any = None
if self.use_labels:
lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
lowerCAmelCase : List[str] = TrOCRConfig(
vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )
return (config, input_ids, attention_mask, lm_labels)
def lowercase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = True
lowerCAmelCase : List[Any] = TrOCRDecoder(config=_UpperCAmelCase ).to(_UpperCAmelCase ).eval()
lowerCAmelCase : List[Any] = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
lowerCAmelCase : List[str] = model(_UpperCAmelCase , use_cache=_UpperCAmelCase )
lowerCAmelCase : Union[str, Any] = model(_UpperCAmelCase )
lowerCAmelCase : Optional[int] = model(_UpperCAmelCase , use_cache=_UpperCAmelCase )
self.parent.assertTrue(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) )
self.parent.assertTrue(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) + 1 )
lowerCAmelCase : List[Any] = outputs['''past_key_values''']
# create hypothetical next token and extent to next_input_ids
lowerCAmelCase : Optional[Any] = ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
lowerCAmelCase : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase : int = model(_UpperCAmelCase )['''last_hidden_state''']
lowerCAmelCase : List[str] = model(_UpperCAmelCase , past_key_values=_UpperCAmelCase )['''last_hidden_state''']
# select random slice
lowerCAmelCase : Optional[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase : List[str] = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
lowerCAmelCase : List[Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-3 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = self.prepare_config_and_inputs()
lowerCAmelCase : int = config_and_inputs
lowerCAmelCase : Tuple = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a : List[Any] =(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
a : Optional[Any] =(TrOCRForCausalLM,) if is_torch_available() else ()
a : int ={"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
a : int =True
a : Any =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[str] = TrOCRStandaloneDecoderModelTester(self , is_training=_UpperCAmelCase )
lowerCAmelCase : Optional[int] = ConfigTester(self , config_class=_UpperCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*_UpperCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
return
@unittest.skip("The model doesn\'t support left padding" ) # and it's not used enough to be worth fixing :)
def lowercase__ ( self ):
"""simple docstring"""
pass
| 352
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''VanForImageClassification''',
'''VanModel''',
'''VanPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 133
| 0
|
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE : Dict = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append(
(
f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
)
)
rename_keys.append(
(
f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
)
)
rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
)
rename_keys.append(
(f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
)
rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
rename_keys.append(
(f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
[
("input_proj.weight", "input_projection.weight"),
("input_proj.bias", "input_projection.bias"),
("query_embed.weight", "query_position_embeddings.weight"),
("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
("class_embed.weight", "class_labels_classifier.weight"),
("class_embed.bias", "class_labels_classifier.bias"),
("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
]
)
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
snake_case = state_dict.pop(UpperCamelCase_ )
snake_case = val
def UpperCAmelCase__ (UpperCamelCase_ ):
"""simple docstring"""
snake_case = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
snake_case = key.replace('''backbone.0.body''' ,'''backbone.conv_encoder.model''' )
snake_case = value
else:
snake_case = value
return new_state_dict
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_=False ):
"""simple docstring"""
snake_case = ''''''
if is_panoptic:
snake_case = '''conditional_detr.'''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
snake_case = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
snake_case = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case = in_proj_weight[:2_56, :]
snake_case = in_proj_bias[:2_56]
snake_case = in_proj_weight[2_56:5_12, :]
snake_case = in_proj_bias[2_56:5_12]
snake_case = in_proj_weight[-2_56:, :]
snake_case = in_proj_bias[-2_56:]
def UpperCAmelCase__ ():
"""simple docstring"""
snake_case = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case = Image.open(requests.get(UpperCamelCase_ ,stream=UpperCamelCase_ ).raw )
return im
@torch.no_grad()
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
snake_case = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
snake_case = '''resnet101'''
if "dc5" in model_name:
snake_case = True
snake_case = '''panoptic''' in model_name
if is_panoptic:
snake_case = 2_50
else:
snake_case = 91
snake_case = '''huggingface/label-files'''
snake_case = '''coco-detection-id2label.json'''
snake_case = json.load(open(hf_hub_download(UpperCamelCase_ ,UpperCamelCase_ ,repo_type='''dataset''' ) ,'''r''' ) )
snake_case = {int(UpperCamelCase_ ): v for k, v in idalabel.items()}
snake_case = idalabel
snake_case = {v: k for k, v in idalabel.items()}
# load image processor
snake_case = '''coco_panoptic''' if is_panoptic else '''coco_detection'''
snake_case = ConditionalDetrImageProcessor(format=UpperCamelCase_ )
# prepare image
snake_case = prepare_img()
snake_case = image_processor(images=UpperCamelCase_ ,return_tensors='''pt''' )
snake_case = encoding['''pixel_values''']
logger.info(F'''Converting model {model_name}...''' )
# load original model from torch hub
snake_case = torch.hub.load('''DeppMeng/ConditionalDETR''' ,UpperCamelCase_ ,pretrained=UpperCamelCase_ ).eval()
snake_case = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
snake_case = '''conditional_detr.''' + src
rename_key(UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ )
snake_case = rename_backbone_keys(UpperCamelCase_ )
# query, key and value matrices need special treatment
read_in_q_k_v(UpperCamelCase_ ,is_panoptic=UpperCamelCase_ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
snake_case = '''conditional_detr.model.''' if is_panoptic else '''model.'''
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('''conditional_detr''' )
and not key.startswith('''class_labels_classifier''' )
and not key.startswith('''bbox_predictor''' )
):
snake_case = state_dict.pop(UpperCamelCase_ )
snake_case = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
snake_case = state_dict.pop(UpperCamelCase_ )
snake_case = val
elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
continue
else:
snake_case = state_dict.pop(UpperCamelCase_ )
snake_case = val
else:
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
snake_case = state_dict.pop(UpperCamelCase_ )
snake_case = val
# finally, create HuggingFace model and load state dict
snake_case = ConditionalDetrForSegmentation(UpperCamelCase_ ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase_ )
model.load_state_dict(UpperCamelCase_ )
model.eval()
model.push_to_hub(repo_id=UpperCamelCase_ ,organization='''DepuMeng''' ,commit_message='''Add model''' )
# verify our conversion
snake_case = conditional_detr(UpperCamelCase_ )
snake_case = model(UpperCamelCase_ )
assert torch.allclose(outputs.logits ,original_outputs['''pred_logits'''] ,atol=1e-4 )
assert torch.allclose(outputs.pred_boxes ,original_outputs['''pred_boxes'''] ,atol=1e-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks ,original_outputs['''pred_masks'''] ,atol=1e-4 )
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(UpperCamelCase_ ).mkdir(exist_ok=UpperCamelCase_ )
model.save_pretrained(UpperCamelCase_ )
image_processor.save_pretrained(UpperCamelCase_ )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="conditional_detr_resnet50",
type=str,
help="Name of the CONDITIONAL_DETR model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
_SCREAMING_SNAKE_CASE : Any = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 127
|
class A__ :
"""simple docstring"""
def __init__( self , __snake_case , __snake_case , __snake_case ):
snake_case = name
snake_case = value
snake_case = weight
def __repr__( self ):
return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def a_ ( self ):
return self.value
def a_ ( self ):
return self.name
def a_ ( self ):
return self.weight
def a_ ( self ):
return self.value / self.weight
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
snake_case = []
for i in range(len(UpperCamelCase_ ) ):
menu.append(Things(name[i] ,value[i] ,weight[i] ) )
return menu
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
snake_case = sorted(UpperCamelCase_ ,key=UpperCamelCase_ ,reverse=UpperCamelCase_ )
snake_case = []
snake_case , snake_case = 0.0, 0.0
for i in range(len(UpperCamelCase_ ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def UpperCAmelCase__ ():
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 127
| 1
|
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
_a = None
_a = None
_a = None
_a = None
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : str, lowerCamelCase : str=1, lowerCamelCase : Any=0, lowerCamelCase : Optional[int]=2, lowerCamelCase : Tuple=512, lowerCamelCase : List[str]="cls", lowerCamelCase : List[Any]=False, lowerCamelCase : Dict=True, **lowerCamelCase : Union[str, Any], )-> Tuple:
super().__init__(pad_token_id=lowerCamelCase, bos_token_id=lowerCamelCase, eos_token_id=lowerCamelCase, **lowerCamelCase )
lowerCamelCase__ : List[Any] =project_dim
lowerCamelCase__ : Union[str, Any] =pooler_fn
lowerCamelCase__ : Optional[int] =learn_encoder
lowerCamelCase__ : Union[str, Any] =use_attention_mask
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
_a = [r'pooler', r'logit_scale']
_a = [r'position_ids', r'predictions.decoder.bias']
_a = 'roberta'
_a = RobertaSeriesConfig
def __init__( self : int, lowerCamelCase : Tuple )-> Dict:
super().__init__(lowerCamelCase )
lowerCamelCase__ : Any =XLMRobertaModel(lowerCamelCase )
lowerCamelCase__ : Dict =nn.Linear(config.hidden_size, config.project_dim )
lowerCamelCase__ : List[Any] =getattr(lowerCamelCase, '''has_pre_transformation''', lowerCamelCase )
if self.has_pre_transformation:
lowerCamelCase__ : int =nn.Linear(config.hidden_size, config.project_dim )
lowerCamelCase__ : List[Any] =nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps )
self.post_init()
def snake_case ( self : Any, lowerCamelCase : Optional[torch.Tensor] = None, lowerCamelCase : Optional[torch.Tensor] = None, lowerCamelCase : Optional[torch.Tensor] = None, lowerCamelCase : Optional[torch.Tensor] = None, lowerCamelCase : Optional[torch.Tensor] = None, lowerCamelCase : Optional[torch.Tensor] = None, lowerCamelCase : Optional[torch.Tensor] = None, lowerCamelCase : Optional[torch.Tensor] = None, lowerCamelCase : Optional[bool] = None, lowerCamelCase : Optional[bool] = None, lowerCamelCase : Optional[bool] = None, )-> List[Any]:
lowerCamelCase__ : List[Any] =return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase__ : Tuple =self.base_model(
input_ids=lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, position_ids=lowerCamelCase, head_mask=lowerCamelCase, inputs_embeds=lowerCamelCase, encoder_hidden_states=lowerCamelCase, encoder_attention_mask=lowerCamelCase, output_attentions=lowerCamelCase, output_hidden_states=True if self.has_pre_transformation else output_hidden_states, return_dict=lowerCamelCase, )
if self.has_pre_transformation:
lowerCamelCase__ : Optional[Any] =outputs['''hidden_states'''][-2]
lowerCamelCase__ : Optional[Any] =self.pre_LN(lowerCamelCase )
lowerCamelCase__ : List[str] =self.transformation_pre(lowerCamelCase )
return TransformationModelOutput(
projection_state=lowerCamelCase, last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
else:
lowerCamelCase__ : Union[str, Any] =self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=lowerCamelCase, last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
| 272
|
"""simple docstring"""
import numpy as np
from PIL import Image
def snake_case__ ( __lowerCamelCase : np.ndarray , __lowerCamelCase : int , __lowerCamelCase : int ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =np.array(__lowerCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input array is not a square matrix''' )
lowerCamelCase__ : int =0
lowerCamelCase__ : int =0
lowerCamelCase__ : Optional[int] =0
lowerCamelCase__ : List[Any] =0
# compute the shape of the output matrix
lowerCamelCase__ : Union[str, Any] =(arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
lowerCamelCase__ : Union[str, Any] =np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
lowerCamelCase__ : str =np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
lowerCamelCase__ : Optional[int] =0
lowerCamelCase__ : Optional[int] =0
return updated_arr
def snake_case__ ( __lowerCamelCase : np.ndarray , __lowerCamelCase : int , __lowerCamelCase : int ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =np.array(__lowerCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input array is not a square matrix''' )
lowerCamelCase__ : str =0
lowerCamelCase__ : List[Any] =0
lowerCamelCase__ : Optional[int] =0
lowerCamelCase__ : List[Any] =0
# compute the shape of the output matrix
lowerCamelCase__ : Dict =(arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
lowerCamelCase__ : Optional[Any] =np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
lowerCamelCase__ : Optional[int] =int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
lowerCamelCase__ : Optional[Any] =0
lowerCamelCase__ : int =0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="avgpooling", verbose=True)
# Loading the image
_lowercase : int = Image.open("path_to_image")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 272
| 1
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""google/pix2struct-textcaps-base""": (
"""https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
),
}
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''pix2struct_text_model'''
__lowerCamelCase = ['''past_key_values''']
__lowerCamelCase = {
'''hidden_size''': '''hidden_size''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _snake_case=50244 , _snake_case=768 , _snake_case=64 , _snake_case=2048 , _snake_case=12 , _snake_case=12 , _snake_case=32 , _snake_case=128 , _snake_case=0.1 , _snake_case=1e-6 , _snake_case=1.0 , _snake_case="gelu_new" , _snake_case=0 , _snake_case=False , _snake_case=0 , _snake_case=1 , _snake_case=False , _snake_case=True , **_snake_case , ):
"""simple docstring"""
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = d_kv
_lowerCAmelCase = d_ff
_lowerCAmelCase = num_layers
_lowerCAmelCase = num_heads
_lowerCAmelCase = relative_attention_num_buckets
_lowerCAmelCase = relative_attention_max_distance
_lowerCAmelCase = dropout_rate
_lowerCAmelCase = layer_norm_epsilon
_lowerCAmelCase = initializer_factor
_lowerCAmelCase = use_cache
_lowerCAmelCase = eos_token_id
_lowerCAmelCase = decoder_start_token_id
# for backwards compatibility
_lowerCAmelCase = dense_act_fn
super().__init__(
pad_token_id=_snake_case , eos_token_id=_snake_case , decoder_start_token_id=_snake_case , tie_word_embeddings=_snake_case , is_decoder=_snake_case , **_snake_case , )
@classmethod
def snake_case ( cls , _snake_case , **_snake_case ):
"""simple docstring"""
cls._set_token_in_kwargs(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = cls.get_config_dict(_snake_case , **_snake_case )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get("""model_type""" ) == "pix2struct":
_lowerCAmelCase = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_snake_case , **_snake_case )
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''pix2struct_vision_model'''
def __init__( self , _snake_case=768 , _snake_case=768 , _snake_case=2048 , _snake_case=64 , _snake_case=12 , _snake_case=12 , _snake_case="gelu_new" , _snake_case=1e-6 , _snake_case=0.0 , _snake_case=0.0 , _snake_case=1e-10 , _snake_case=1.0 , _snake_case=4096 , _snake_case=32 , _snake_case=128 , **_snake_case , ):
"""simple docstring"""
super().__init__(**_snake_case )
_lowerCAmelCase = hidden_size
_lowerCAmelCase = patch_embed_hidden_size
_lowerCAmelCase = d_ff
_lowerCAmelCase = dropout_rate
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = initializer_range
_lowerCAmelCase = initializer_factor
_lowerCAmelCase = attention_dropout
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = dense_act_fn
_lowerCAmelCase = seq_len
_lowerCAmelCase = relative_attention_num_buckets
_lowerCAmelCase = relative_attention_max_distance
_lowerCAmelCase = d_kv
@classmethod
def snake_case ( cls , _snake_case , **_snake_case ):
"""simple docstring"""
cls._set_token_in_kwargs(_snake_case )
_lowerCAmelCase , _lowerCAmelCase = cls.get_config_dict(_snake_case , **_snake_case )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get("""model_type""" ) == "pix2struct":
_lowerCAmelCase = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_snake_case , **_snake_case )
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = '''pix2struct'''
__lowerCamelCase = True
def __init__( self , _snake_case=None , _snake_case=None , _snake_case=1.0 , _snake_case=0.02 , _snake_case=False , _snake_case=False , _snake_case=True , **_snake_case , ):
"""simple docstring"""
super().__init__(tie_word_embeddings=_snake_case , is_encoder_decoder=_snake_case , **_snake_case )
if text_config is None:
_lowerCAmelCase = {}
logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values.""" )
if vision_config is None:
_lowerCAmelCase = {}
logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values.""" )
_lowerCAmelCase = PixaStructTextConfig(**_snake_case )
_lowerCAmelCase = PixaStructVisionConfig(**_snake_case )
_lowerCAmelCase = self.text_config.decoder_start_token_id
_lowerCAmelCase = self.text_config.pad_token_id
_lowerCAmelCase = self.text_config.eos_token_id
_lowerCAmelCase = initializer_factor
_lowerCAmelCase = initializer_range
_lowerCAmelCase = self.initializer_range
_lowerCAmelCase = self.initializer_range
_lowerCAmelCase = is_vqa
@classmethod
def snake_case ( cls , _snake_case , _snake_case , **_snake_case ):
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = copy.deepcopy(self.__dict__ )
_lowerCAmelCase = self.text_config.to_dict()
_lowerCAmelCase = self.vision_config.to_dict()
_lowerCAmelCase = self.__class__.model_type
return output
| 82
|
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self : Optional[Any] , UpperCAmelCase_ : int) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: Any =n
lowerCamelCase__: Tuple =[None] * self.n
lowerCamelCase__: str =0 # index of the first element
lowerCamelCase__: Tuple =0
lowerCamelCase__: Optional[Any] =0
def __len__(self : str) ->int:
'''simple docstring'''
return self.size
def SCREAMING_SNAKE_CASE_ (self : int) ->bool:
'''simple docstring'''
return self.size == 0
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->str:
'''simple docstring'''
return False if self.is_empty() else self.array[self.front]
def SCREAMING_SNAKE_CASE_ (self : int , UpperCAmelCase_ : Optional[int]) ->str:
'''simple docstring'''
if self.size >= self.n:
raise Exception("QUEUE IS FULL")
lowerCamelCase__: List[Any] =data
lowerCamelCase__: Dict =(self.rear + 1) % self.n
self.size += 1
return self
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->Tuple:
'''simple docstring'''
if self.size == 0:
raise Exception("UNDERFLOW")
lowerCamelCase__: Optional[Any] =self.array[self.front]
lowerCamelCase__: Optional[int] =None
lowerCamelCase__: Dict =(self.front + 1) % self.n
self.size -= 1
return temp
| 10
| 0
|
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCamelCase__ ( self ):
"""simple docstring"""
debug_launcher(test_script.main )
def UpperCamelCase__ ( self ):
"""simple docstring"""
debug_launcher(test_ops.main )
| 353
|
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__snake_case : Optional[Any] = TypeVar("""KEY""")
__snake_case : str = TypeVar("""VAL""")
@dataclass(frozen=__lowercase , slots=__lowercase)
class __SCREAMING_SNAKE_CASE ( Generic[KEY, VAL]):
_SCREAMING_SNAKE_CASE : KEY
_SCREAMING_SNAKE_CASE : VAL
class __SCREAMING_SNAKE_CASE ( _Item):
def __init__( self ):
"""simple docstring"""
super().__init__(_UpperCamelCase , _UpperCamelCase )
def __bool__( self ):
"""simple docstring"""
return False
__snake_case : int = _DeletedItem()
class __SCREAMING_SNAKE_CASE ( MutableMapping[KEY, VAL]):
def __init__( self , _UpperCamelCase = 8 , _UpperCamelCase = 0.75 ):
"""simple docstring"""
lowerCAmelCase__ = initial_block_size
lowerCAmelCase__ = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
lowerCAmelCase__ = capacity_factor
lowerCAmelCase__ = 0
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
return hash(_UpperCamelCase ) % len(self._buckets )
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
return (ind + 1) % len(self._buckets )
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = self._buckets[ind]
if not stored:
lowerCAmelCase__ = _Item(_UpperCamelCase , _UpperCamelCase )
self._len += 1
return True
elif stored.key == key:
lowerCAmelCase__ = _Item(_UpperCamelCase , _UpperCamelCase )
return True
else:
return False
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(_UpperCamelCase )
def UpperCamelCase__ ( self ):
"""simple docstring"""
if len(self._buckets ) <= self._initial_block_size:
return False
lowerCAmelCase__ = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = self._buckets
lowerCAmelCase__ = [None] * new_size
lowerCAmelCase__ = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._resize(len(self._buckets ) * 2 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._resize(len(self._buckets ) // 2 )
def UpperCamelCase__ ( self , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = self._get_bucket_index(_UpperCamelCase )
for _ in range(len(self._buckets ) ):
yield ind
lowerCAmelCase__ = self._get_next_ind(_UpperCamelCase )
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
for ind in self._iterate_buckets(_UpperCamelCase ):
if self._try_set(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
break
def __setitem__( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
if self._is_full():
self._size_up()
self._add_item(_UpperCamelCase , _UpperCamelCase )
def __delitem__( self , _UpperCamelCase ):
"""simple docstring"""
for ind in self._iterate_buckets(_UpperCamelCase ):
lowerCAmelCase__ = self._buckets[ind]
if item is None:
raise KeyError(_UpperCamelCase )
if item is _deleted:
continue
if item.key == key:
lowerCAmelCase__ = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , _UpperCamelCase ):
"""simple docstring"""
for ind in self._iterate_buckets(_UpperCamelCase ):
lowerCAmelCase__ = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(_UpperCamelCase )
def __len__( self ):
"""simple docstring"""
return self._len
def __iter__( self ):
"""simple docstring"""
yield from (item.key for item in self._buckets if item)
def __repr__( self ):
"""simple docstring"""
lowerCAmelCase__ = ' ,'.join(
F"{item.key}: {item.val}" for item in self._buckets if item )
return F"HashMap({val_string})"
| 122
| 0
|
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = TypeVar("""DatasetType""", Dataset, IterableDataset)
def _A (__a , __a = None , __a = None , __a = None , __a = None , __a = "first_exhausted" , ) -> DatasetType:
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('''Unable to interleave an empty list of datasets.''' )
for i, dataset in enumerate(__a ):
if not isinstance(__a , (Dataset, IterableDataset) ):
if isinstance(__a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'''is an empty dataset dictionary.''' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(__a )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__a ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__a ).__name__}.' )
if i == 0:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
(Dataset, IterableDataset) if isinstance(__a , __a ) else (IterableDataset, Dataset)
)
elif not isinstance(__a , __a ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
__a , __a , __a , info=__a , split=__a , stopping_strategy=__a )
else:
return _interleave_iterable_datasets(
__a , __a , __a , info=__a , split=__a , stopping_strategy=__a )
def _A (__a , __a = None , __a = None , __a = 0 , ) -> DatasetType:
"""simple docstring"""
if not dsets:
raise ValueError('''Unable to concatenate an empty list of datasets.''' )
for i, dataset in enumerate(__a ):
if not isinstance(__a , (Dataset, IterableDataset) ):
if isinstance(__a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'''is an empty dataset dictionary.''' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(__a )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__a ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__a ).__name__}.' )
if i == 0:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[str] = (
(Dataset, IterableDataset) if isinstance(__a , __a ) else (IterableDataset, Dataset)
)
elif not isinstance(__a , __a ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(__a , info=__a , split=__a , axis=__a )
else:
return _concatenate_iterable_datasets(__a , info=__a , split=__a , axis=__a )
| 91
|
"""simple docstring"""
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 91
| 1
|
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("""TEST_SAGEMAKER""" , """False""" ) ) is not True , reason="""Skipping test because should only be run when releasing minor transformers version""" , )
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
[
{
"""framework""": """pytorch""",
"""script""": """run_glue.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.p3.16xlarge""",
"""results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
},
{
"""framework""": """pytorch""",
"""script""": """run_ddp.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.p3.16xlarge""",
"""results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
},
{
"""framework""": """tensorflow""",
"""script""": """run_tf_dist.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.p3.16xlarge""",
"""results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
},
] )
class A_ ( unittest.TestCase ):
def _lowerCAmelCase (self :Dict )-> Union[str, Any]:
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding='''utf-8''' , check=_UpperCamelCase , )
assert hasattr(self , '''env''' )
def _lowerCAmelCase (self :Union[str, Any] , _UpperCamelCase :int )-> List[str]:
__A = f"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
# distributed data settings
__A = {'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=_UpperCamelCase , instance_count=_UpperCamelCase , instance_type=self.instance_type , debugger_hook_config=_UpperCamelCase , hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=_UpperCamelCase , py_version='''py36''' , )
def _lowerCAmelCase (self :Optional[int] , _UpperCamelCase :Any )-> Any:
TrainingJobAnalytics(_UpperCamelCase ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(2,)] )
def _lowerCAmelCase (self :List[str] , _UpperCamelCase :Optional[int] )-> Any:
# create estimator
__A = self.create_estimator(_UpperCamelCase )
# run training
estimator.fit()
# result dataframe
__A = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
__A = list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
__A = list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
__A = (
Session().describe_training_job(estimator.latest_training_job.name ).get('''TrainingTimeInSeconds''' , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy )
assert all(t <= self.results['''eval_loss'''] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , '''w''' ) as outfile:
json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss} , _UpperCamelCase )
| 250
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class A_ ( unittest.TestCase ):
@slow
def _lowerCAmelCase (self :Dict )-> Dict:
__A = AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' , return_dict=_UpperCamelCase ).to(_UpperCamelCase )
__A = AutoTokenizer.from_pretrained('''google/mt5-small''' )
__A = tokenizer('''Hello there''' , return_tensors='''pt''' ).input_ids
__A = tokenizer('''Hi I am''' , return_tensors='''pt''' ).input_ids
__A = model(input_ids.to(_UpperCamelCase ) , labels=labels.to(_UpperCamelCase ) ).loss
__A = -(labels.shape[-1] * loss.item())
__A = -8_4.9_1_2_7
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 250
| 1
|
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_=7 ):
'''simple docstring'''
_UpperCAmelCase = None
if token is not None:
_UpperCAmelCase = {"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}
# The id of a workflow (not of a workflow run)
_UpperCAmelCase = "636036"
_UpperCAmelCase = f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""
_UpperCAmelCase = requests.get(snake_case_ , headers=snake_case_ ).json()
return result["workflow_runs"]
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = get_daily_ci_runs(snake_case_ )
_UpperCAmelCase = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
_UpperCAmelCase = workflow_run["id"]
break
return workflow_run_id
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = get_last_daily_ci_runs(snake_case_ )
if workflow_run_id is not None:
_UpperCAmelCase = get_artifacts_links(worflow_run_id=snake_case_ , token=snake_case_ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
_UpperCAmelCase = artifacts_links[artifact_name]
download_artifact(
artifact_name=snake_case_ , artifact_url=snake_case_ , output_dir=snake_case_ , token=snake_case_ )
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
get_last_daily_ci_artifacts(snake_case_ , snake_case_ , snake_case_ )
_UpperCAmelCase = {}
for artifact_name in artifact_names:
_UpperCAmelCase = os.path.join(snake_case_ , f"""{artifact_name}.zip""" )
if os.path.isfile(snake_case_ ):
_UpperCAmelCase = {}
with zipfile.ZipFile(snake_case_ ) as z:
for filename in z.namelist():
if not os.path.isdir(snake_case_ ):
# read the file
with z.open(snake_case_ ) as f:
_UpperCAmelCase = f.read().decode("UTF-8" )
return results
| 133
|
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowercase_ : Optional[Any] = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|'),
datarow=DataRow('', '|', '|'),
padding=1,
with_header_hide=None,
)
lowercase_ : str = []
lowercase_ : int = []
lowercase_ : Dict = {'type': 'section', 'text': {'type': 'plain_text', 'text': 'No failed tests! 🤗', 'emoji': True}}
lowercase_ : int = [
{
'type': 'header',
'text': {
'type': 'plain_text',
'text': f"""🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results""",
'emoji': True,
},
}
]
lowercase_ : int = 0
for log in Path().glob('*.log'):
lowercase_ : int = 0
with open(log, 'r') as f:
for line in f:
lowercase_ : List[str] = json.loads(line)
if line.get('nodeid', '') != "":
lowercase_ : List[str] = line['nodeid']
if line.get('duration', None) is not None:
lowercase_ : Tuple = f"""{line["duration"]:.4f}"""
if line.get('outcome', '') == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split('_')[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
lowercase_ : List[Any] = []
log.unlink()
lowercase_ : int = ''
lowercase_ : int = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += f"*{name[1:]}: {num_failed} failed test*\n"
else:
message += f"*{name[1:]}: {num_failed} failed tests*\n"
lowercase_ : Optional[Any] = []
lowercase_ : Any = {}
for test in failed_tests:
lowercase_ : List[str] = test[0].split('::')
lowercase_ : int = data[0].split('/')[-1]
if data[0] not in filesafailed:
lowercase_ : Dict = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
lowercase_ : Any = [test[0] for test in failed_table]
lowercase_ : Optional[Any] = list(set(files))
# Count number of instances in failed_tests
lowercase_ : Optional[Any] = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
lowercase_ : Optional[Any] = tabulate(
table,
headers=['Test Location', 'Num Failed'],
tablefmt=hf_table_format,
stralign='right',
)
message += f"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 30_00:
lowercase_ : List[Any] = 'Too many failed tests, please see the full report in the Action results.'
lowercase_ : Union[str, Any] = len(err) + 10
lowercase_ : Tuple = message[: 30_00 - offset] + f"""\n...\n```\n{err}"""
print(f"""### {message}""")
else:
lowercase_ : int = 'No failed tests! 🤗'
print(f"""## {message}""")
payload.append(no_error_payload)
if os.environ.get('TEST_TYPE', '') != "":
from slack_sdk import WebClient
lowercase_ : Union[str, Any] = WebClient(token=os.environ['SLACK_API_TOKEN'])
if message != "No failed tests! 🤗":
lowercase_ : List[Any] = {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': message,
},
}
payload.append(md_report)
lowercase_ : List[Any] = {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': '*For more details:*',
},
'accessory': {
'type': 'button',
'text': {
'type': 'plain_text',
'text': 'Check Action results',
'emoji': True,
},
'url': f"""https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
},
}
payload.append(action_button)
lowercase_ : List[str] = {
'type': 'context',
'elements': [
{
'type': 'plain_text',
'text': f"""Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}""",
}
],
}
payload.append(date_report)
lowercase_ : Any = client.chat_postMessage(channel='#accelerate-ci-daily', text=message, blocks=payload)
lowercase_ : Any = response.data['ts']
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
lowercase_ : Optional[int] = ''
for i, row in enumerate(test_failures):
if row[0] != test_class:
lowercase_ : Tuple = row[0]
else:
lowercase_ : Tuple = ''
lowercase_ : int = {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': f"""Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```""",
},
}
client.chat_postMessage(
channel='#accelerate-ci-daily',
thread_ts=ts,
blocks=[payload],
)
| 133
| 1
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
UpperCamelCase__ = {
"""vocab_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
),
"""google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-openqa""": (
"""https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-reader""": (
"""https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-openqa""": (
"""https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-reader""": (
"""https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
),
},
}
UpperCamelCase__ = {
"""google/realm-cc-news-pretrained-embedder""": 512,
"""google/realm-cc-news-pretrained-encoder""": 512,
"""google/realm-cc-news-pretrained-scorer""": 512,
"""google/realm-cc-news-pretrained-openqa""": 512,
"""google/realm-orqa-nq-openqa""": 512,
"""google/realm-orqa-nq-reader""": 512,
"""google/realm-orqa-wq-openqa""": 512,
"""google/realm-orqa-wq-reader""": 512,
}
UpperCamelCase__ = {
"""google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}
class a__ ( snake_case_ ):
_a : Union[str, Any] = VOCAB_FILES_NAMES
_a : Tuple = PRETRAINED_VOCAB_FILES_MAP
_a : Dict = PRETRAINED_INIT_CONFIGURATION
_a : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a : Any = RealmTokenizer
def __init__( self , _A=None , _A=None , _A=True , _A="[UNK]" , _A="[SEP]" , _A="[PAD]" , _A="[CLS]" , _A="[MASK]" , _A=True , _A=None , **_A , ):
"""simple docstring"""
super().__init__(
_A , tokenizer_file=_A , do_lower_case=_A , unk_token=_A , sep_token=_A , pad_token=_A , cls_token=_A , mask_token=_A , tokenize_chinese_chars=_A , strip_accents=_A , **_A , )
__lowerCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , _A ) != do_lower_case
or normalizer_state.get("strip_accents" , _A ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , _A ) != tokenize_chinese_chars
):
__lowerCAmelCase = getattr(_A , normalizer_state.pop("type" ) )
__lowerCAmelCase = do_lower_case
__lowerCAmelCase = strip_accents
__lowerCAmelCase = tokenize_chinese_chars
__lowerCAmelCase = normalizer_class(**_A )
__lowerCAmelCase = do_lower_case
def __SCREAMING_SNAKE_CASE( self , _A , **_A ):
"""simple docstring"""
__lowerCAmelCase = PaddingStrategy.MAX_LENGTH
__lowerCAmelCase = text
__lowerCAmelCase = kwargs.pop("text_pair" , _A )
__lowerCAmelCase = kwargs.pop("return_tensors" , _A )
__lowerCAmelCase = {
'input_ids': [],
'attention_mask': [],
'token_type_ids': [],
}
for idx, candidate_text in enumerate(_A ):
if batch_text_pair is not None:
__lowerCAmelCase = batch_text_pair[idx]
else:
__lowerCAmelCase = None
__lowerCAmelCase = super().__call__(_A , _A , return_tensors=_A , **_A )
__lowerCAmelCase = encoded_candidates.get("input_ids" )
__lowerCAmelCase = encoded_candidates.get("attention_mask" )
__lowerCAmelCase = encoded_candidates.get("token_type_ids" )
if encoded_input_ids is not None:
output_data["input_ids"].append(_A )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(_A )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(_A )
__lowerCAmelCase = {key: item for key, item in output_data.items() if len(_A ) != 0}
return BatchEncoding(_A , tensor_type=_A )
def __SCREAMING_SNAKE_CASE( self , _A , _A=None ):
"""simple docstring"""
__lowerCAmelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __SCREAMING_SNAKE_CASE( self , _A , _A = None ):
"""simple docstring"""
__lowerCAmelCase = [self.sep_token_id]
__lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __SCREAMING_SNAKE_CASE( self , _A , _A = None ):
"""simple docstring"""
__lowerCAmelCase = self._tokenizer.model.save(_A , name=_A )
return tuple(_A )
| 356
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
UpperCamelCase__ = 16
UpperCamelCase__ = 32
def _a ( SCREAMING_SNAKE_CASE_ : Accelerator , SCREAMING_SNAKE_CASE_ : int = 16 ):
__lowerCAmelCase = AutoTokenizer.from_pretrained("bert-base-cased" )
__lowerCAmelCase = load_dataset("glue" , "mrpc" )
def tokenize_function(SCREAMING_SNAKE_CASE_ : str ):
# max_length=None => use the model max length (it's actually the default)
__lowerCAmelCase = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowerCAmelCase = datasets.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCAmelCase = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(SCREAMING_SNAKE_CASE_ : int ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowerCAmelCase = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowerCAmelCase = 16
elif accelerator.mixed_precision != "no":
__lowerCAmelCase = 8
else:
__lowerCAmelCase = None
return tokenizer.pad(
SCREAMING_SNAKE_CASE_ , padding="longest" , max_length=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_tensors="pt" , )
# Instantiate dataloaders.
__lowerCAmelCase = DataLoader(
tokenized_datasets["train"] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = DataLoader(
tokenized_datasets["validation"] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
UpperCamelCase__ = mocked_dataloaders # noqa: F811
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS" , SCREAMING_SNAKE_CASE_ ) == "1":
__lowerCAmelCase = 2
# New Code #
__lowerCAmelCase = int(args.gradient_accumulation_steps )
# Initialize accelerator
__lowerCAmelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=SCREAMING_SNAKE_CASE_ )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
"Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCAmelCase = config["lr"]
__lowerCAmelCase = int(config["num_epochs"] )
__lowerCAmelCase = int(config["seed"] )
__lowerCAmelCase = int(config["batch_size"] )
__lowerCAmelCase = evaluate.load("glue" , "mrpc" )
set_seed(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase , __lowerCAmelCase = get_dataloaders(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=SCREAMING_SNAKE_CASE_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowerCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
__lowerCAmelCase = AdamW(params=model.parameters() , lr=SCREAMING_SNAKE_CASE_ )
# Instantiate scheduler
__lowerCAmelCase = get_linear_schedule_with_warmup(
optimizer=SCREAMING_SNAKE_CASE_ , num_warmup_steps=1_00 , num_training_steps=(len(SCREAMING_SNAKE_CASE_ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = accelerator.prepare(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Now we train the model
for epoch in range(SCREAMING_SNAKE_CASE_ ):
model.train()
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(SCREAMING_SNAKE_CASE_ ):
__lowerCAmelCase = model(**SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = output.loss
accelerator.backward(SCREAMING_SNAKE_CASE_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowerCAmelCase = model(**SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = outputs.logits.argmax(dim=-1 )
__lowerCAmelCase , __lowerCAmelCase = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ , )
__lowerCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , SCREAMING_SNAKE_CASE_ )
def _a ( ):
__lowerCAmelCase = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
# New Code #
parser.add_argument(
"--gradient_accumulation_steps" , type=SCREAMING_SNAKE_CASE_ , default=1 , help="The number of minibatches to be ran before gradients are accumulated." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main()
| 102
| 0
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
'''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}
class a__( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = '''encodec'''
def __init__( self , __lowerCAmelCase=[1.5, 3.0, 6.0, 12.0, 24.0] , __lowerCAmelCase=24000 , __lowerCAmelCase=1 , __lowerCAmelCase=False , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=128 , __lowerCAmelCase=32 , __lowerCAmelCase=1 , __lowerCAmelCase=[8, 5, 4, 2] , __lowerCAmelCase="weight_norm" , __lowerCAmelCase=7 , __lowerCAmelCase=7 , __lowerCAmelCase=3 , __lowerCAmelCase=2 , __lowerCAmelCase=True , __lowerCAmelCase="reflect" , __lowerCAmelCase=2 , __lowerCAmelCase=2 , __lowerCAmelCase=1.0 , __lowerCAmelCase=1024 , __lowerCAmelCase=None , __lowerCAmelCase=True , **__lowerCAmelCase , ):
"""simple docstring"""
lowerCAmelCase = target_bandwidths
lowerCAmelCase = sampling_rate
lowerCAmelCase = audio_channels
lowerCAmelCase = normalize
lowerCAmelCase = chunk_length_s
lowerCAmelCase = overlap
lowerCAmelCase = hidden_size
lowerCAmelCase = num_filters
lowerCAmelCase = num_residual_layers
lowerCAmelCase = upsampling_ratios
lowerCAmelCase = norm_type
lowerCAmelCase = kernel_size
lowerCAmelCase = last_kernel_size
lowerCAmelCase = residual_kernel_size
lowerCAmelCase = dilation_growth_rate
lowerCAmelCase = use_causal_conv
lowerCAmelCase = pad_mode
lowerCAmelCase = compress
lowerCAmelCase = num_lstm_layers
lowerCAmelCase = trim_right_ratio
lowerCAmelCase = codebook_size
lowerCAmelCase = codebook_dim if codebook_dim is not None else hidden_size
lowerCAmelCase = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}")
super().__init__(**__lowerCAmelCase)
@property
def a_ ( self):
"""simple docstring"""
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate)
@property
def a_ ( self):
"""simple docstring"""
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length))
@property
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = np.prod(self.upsampling_ratios)
return math.ceil(self.sampling_rate / hop_length)
@property
def a_ ( self):
"""simple docstring"""
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10))
| 272
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
'''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NezhaForNextSentencePrediction''',
'''NezhaForMaskedLM''',
'''NezhaForPreTraining''',
'''NezhaForMultipleChoice''',
'''NezhaForQuestionAnswering''',
'''NezhaForSequenceClassification''',
'''NezhaForTokenClassification''',
'''NezhaModel''',
'''NezhaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
__lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 272
| 1
|
"""simple docstring"""
__snake_case = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def A_ ( _lowerCAmelCase : Dict, _lowerCAmelCase : str, _lowerCAmelCase : str, _lowerCAmelCase : int ):
"""simple docstring"""
_a = [False] * len(_lowerCAmelCase )
_a = [s]
_a = True
while queue:
_a = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_lowerCAmelCase )
_a = True
_a = u
return visited[t]
def A_ ( _lowerCAmelCase : Optional[int], _lowerCAmelCase : List[Any], _lowerCAmelCase : Tuple ):
"""simple docstring"""
_a = [-1] * (len(_lowerCAmelCase ))
_a = 0
_a = []
_a = [i[:] for i in graph] # Record original cut, copy.
while bfs(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase ):
_a = float('''Inf''' )
_a = sink
while s != source:
# Find the minimum value in select path
_a = min(_lowerCAmelCase, graph[parent[s]][s] )
_a = parent[s]
max_flow += path_flow
_a = sink
while v != source:
_a = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
_a = parent[v]
for i in range(len(_lowerCAmelCase ) ):
for j in range(len(graph[0] ) ):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j) )
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| 153
|
"""simple docstring"""
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_a = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_a = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
_a = remove_duplicates(key.upper() )
_a = len(_lowerCAmelCase )
# First fill cipher with key characters
_a = {alphabet[i]: char for i, char in enumerate(_lowerCAmelCase )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(_lowerCAmelCase ), 26 ):
_a = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
_a = alphabet[i - offset]
_a = char
return cipher_alphabet
def A_ ( _lowerCAmelCase : str, _lowerCAmelCase : dict[str, str] ):
"""simple docstring"""
return "".join(cipher_map.get(_lowerCAmelCase, _lowerCAmelCase ) for ch in message.upper() )
def A_ ( _lowerCAmelCase : str, _lowerCAmelCase : dict[str, str] ):
"""simple docstring"""
_a = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(_lowerCAmelCase, _lowerCAmelCase ) for ch in message.upper() )
def A_ ( ):
"""simple docstring"""
_a = input('''Enter message to encode or decode: ''' ).strip()
_a = input('''Enter keyword: ''' ).strip()
_a = input('''Encipher or decipher? E/D:''' ).strip()[0].lower()
try:
_a = {'''e''': encipher, '''d''': decipher}[option]
except KeyError:
raise KeyError('''invalid input option''' )
_a = create_cipher_map(_lowerCAmelCase )
print(func(_lowerCAmelCase, _lowerCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 153
| 1
|
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __A , __A=13 , __A=7 , __A=True , __A=True , __A=True , __A=True , __A=99 , __A=32 , __A=5 , __A=4 , __A=4 , __A="gelu" , __A=0.0 , __A=0.1 , __A=True , __A=512 , __A=16 , __A=2 , __A=0.02 , __A=3 , __A=4 , __A=None , ):
"""simple docstring"""
lowerCamelCase : List[str] = parent
lowerCamelCase : Optional[int] = batch_size
lowerCamelCase : Optional[Any] = seq_length
lowerCamelCase : Union[str, Any] = is_training
lowerCamelCase : int = use_input_mask
lowerCamelCase : Optional[Any] = use_token_type_ids
lowerCamelCase : Optional[Any] = use_labels
lowerCamelCase : Optional[Any] = vocab_size
lowerCamelCase : Optional[Any] = hidden_size
lowerCamelCase : int = num_hidden_layers
lowerCamelCase : Any = num_attention_heads
lowerCamelCase : Any = intermediate_multiple_size
lowerCamelCase : List[Any] = hidden_act
lowerCamelCase : Optional[Any] = hidden_dropout
lowerCamelCase : Dict = attention_dropout
lowerCamelCase : Dict = weight_tying
lowerCamelCase : Optional[Any] = max_position_embeddings
lowerCamelCase : Any = type_vocab_size
lowerCamelCase : Any = type_sequence_label_size
lowerCamelCase : Any = initializer_range
lowerCamelCase : List[Any] = num_labels
lowerCamelCase : str = num_choices
lowerCamelCase : int = scope
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase : List[Any] = None
if self.use_input_mask:
lowerCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase : Dict = None
if self.use_labels:
lowerCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase : str = self.get_config()
return config, input_ids, input_mask, token_labels
def _snake_case ( self ):
"""simple docstring"""
return GPTNeoXJapaneseConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_multiple_size=self.intermediate_multiple_size , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , weight_tying=self.weight_tying , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : int = self.prepare_config_and_inputs()
lowerCamelCase : Tuple = True
return config, input_ids, input_mask, token_labels
def _snake_case ( self , __A , __A , __A ):
"""simple docstring"""
lowerCamelCase : Any = GPTNeoXJapaneseModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
lowerCamelCase : Tuple = model(__UpperCamelCase , attention_mask=__UpperCamelCase )
lowerCamelCase : str = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self , __A , __A , __A ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = True
lowerCamelCase : Dict = GPTNeoXJapaneseModel(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
lowerCamelCase : Any = model(__UpperCamelCase , attention_mask=__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self , __A , __A , __A , __A ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = GPTNeoXJapaneseForCausalLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
lowerCamelCase : Optional[int] = model(__UpperCamelCase , attention_mask=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self , __A , __A , __A ):
"""simple docstring"""
lowerCamelCase : Optional[int] = True
lowerCamelCase : Optional[Any] = GPTNeoXJapaneseForCausalLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
# first forward pass
lowerCamelCase : Union[str, Any] = model(__UpperCamelCase , attention_mask=__UpperCamelCase , use_cache=__UpperCamelCase )
lowerCamelCase : Tuple = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCamelCase : int = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCamelCase : Dict = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowerCamelCase : int = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCamelCase : List[str] = torch.cat([input_mask, next_mask] , dim=-1 )
lowerCamelCase : List[Any] = model(__UpperCamelCase , attention_mask=__UpperCamelCase , output_hidden_states=__UpperCamelCase )
lowerCamelCase : str = output_from_no_past["hidden_states"][0]
lowerCamelCase : Any = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , past_key_values=__UpperCamelCase , output_hidden_states=__UpperCamelCase , )["hidden_states"][0]
# select random slice
lowerCamelCase : Optional[int] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCamelCase : List[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCamelCase : List[str] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : List[str] = self.prepare_config_and_inputs()
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : List[str] = config_and_inputs
lowerCamelCase : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
__A : Optional[Any] = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
__A : Union[str, Any] = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
__A : List[str] = (
{"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
__A : Union[str, Any] = False
__A : List[str] = False
__A : Union[str, Any] = False
__A : str = False
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = GPTNeoXJapaneseModelTester(self )
lowerCamelCase : List[str] = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=37 )
def _snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
lowerCamelCase : Optional[Any] = None
self.model_tester.create_and_check_model_as_decoder(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*__UpperCamelCase )
@slow
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = "abeja/gpt-neox-japanese-2.7b"
lowerCamelCase : Any = ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]
lowerCamelCase : List[str] = [
"データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
"100年後に必要とされる会社は、「人」が中心の会社です。",
"フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
"国境の長いトンネルを抜けると、そこは雪国だった。",
"美味しい日本食といえば、やっぱりお寿司ですよね。",
]
lowerCamelCase : List[Any] = GPTNeoXJapaneseTokenizer.from_pretrained(__UpperCamelCase )
lowerCamelCase : Dict = GPTNeoXJapaneseForCausalLM.from_pretrained(__UpperCamelCase )
lowerCamelCase : Tuple = []
for prompt in prompts:
lowerCamelCase : Any = tokenizer(__UpperCamelCase , return_tensors="pt" ).input_ids
lowerCamelCase : str = model.generate(__UpperCamelCase , max_length=50 )
lowerCamelCase : Optional[int] = tokenizer.batch_decode(__UpperCamelCase , skip_special_tokens=__UpperCamelCase )
predicted_outputs += generated_string
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
| 283
|
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase_ ( __SCREAMING_SNAKE_CASE ):
A__ : int = ["""image_processor""", """tokenizer"""]
A__ : Union[str, Any] = """LayoutLMv2ImageProcessor"""
A__ : Optional[int] = ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")
def __init__( self , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase ):
"""simple docstring"""
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , __UpperCamelCase , )
UpperCamelCase_ = kwargs.pop("""feature_extractor""" )
UpperCamelCase_ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(__UpperCamelCase , __UpperCamelCase )
def __call__( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = True , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = 0 , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = True , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"""You cannot provide bounding boxes """
"""if you initialized the image processor with apply_ocr set to True.""" )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"""You cannot provide word labels if you initialized the image processor with apply_ocr set to True.""" )
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping.""" )
# first, apply the image processor
UpperCamelCase_ = self.image_processor(images=__UpperCamelCase , return_tensors=__UpperCamelCase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCamelCase_ = [text] # add batch dimension (as the image processor always adds a batch dimension)
UpperCamelCase_ = features["""words"""]
UpperCamelCase_ = self.tokenizer(
text=text if text is not None else features["""words"""] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["""boxes"""] , word_labels=__UpperCamelCase , add_special_tokens=__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , stride=__UpperCamelCase , pad_to_multiple_of=__UpperCamelCase , return_token_type_ids=__UpperCamelCase , return_attention_mask=__UpperCamelCase , return_overflowing_tokens=__UpperCamelCase , return_special_tokens_mask=__UpperCamelCase , return_offsets_mapping=__UpperCamelCase , return_length=__UpperCamelCase , verbose=__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase , )
# add pixel values
UpperCamelCase_ = features.pop("""pixel_values""" )
if return_overflowing_tokens is True:
UpperCamelCase_ = self.get_overflowing_images(__UpperCamelCase , encoded_inputs["""overflow_to_sample_mapping"""] )
UpperCamelCase_ = images
return encoded_inputs
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
raise ValueError(
"""Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
f''' {len(__UpperCamelCase )} and {len(__UpperCamelCase )}''' )
return images_with_overflow
def lowerCamelCase_ ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
return self.tokenizer.batch_decode(*__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
return self.tokenizer.decode(*__UpperCamelCase , **__UpperCamelCase )
@property
def lowerCamelCase_ ( self ):
"""simple docstring"""
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def lowerCamelCase_ ( self ):
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , __UpperCamelCase , )
return self.image_processor_class
@property
def lowerCamelCase_ ( self ):
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , __UpperCamelCase , )
return self.image_processor
| 122
| 0
|
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
a = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
a = model(lowerCamelCase__ )["""last_hidden_state"""]
a = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , lowerCamelCase__ )
# compare the actual values for a slice.
a = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 357
|
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def __A ( __lowerCamelCase , __lowerCamelCase ) -> Optional[int]:
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
a = flax_key_tuple[:-1] + ("""weight""",)
a = torch.permute(__lowerCamelCase , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCamelCase ):
# linear layer
a = flax_key_tuple[:-1] + ("""weight""",)
a = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
a = flax_key_tuple[:-1] + ("""weight""",)
return flax_key_tuple, flax_tensor
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
if "metadata" in layer:
a = layer.split("""metadata""" )
a = """""".join(split_layer[0] )[:-1]
a = [tuple(("""metadata""" + split_layer[1]).split("""/""" ) )]
elif "kvstore" in layer:
a = layer.split("""kvstore""" )
a = """""".join(split_layer[0] )[:-1]
a = [tuple(("""kvstore""" + split_layer[1]).split("""/""" ) )]
else:
a = layer.split("""/""" )
a = """/""".join(split_layer[:-1] )
a = (split_layer[-1],)
if "kvstore/path" in layer:
a = f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
elif "kvstore/driver" in layer:
a = """file"""
else:
a = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def __A ( __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
a = rename_keys(__lowerCamelCase )
a = {}
for k, v in current_block.items():
a = v
a = new_current_block
torch.save(__lowerCamelCase , __lowerCamelCase )
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = WEIGHTS_NAME ) -> List[str]:
a = convert_file_size_to_int(__lowerCamelCase )
a = []
a = {}
a = 0
a = 0
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
with gfile.GFile(switch_checkpoint_path + """/checkpoint""" , """rb""" ) as fp:
a = serialization.msgpack_restore(fp.read() )["""optimizer"""]["""target"""]
a = flatten_dict(__lowerCamelCase , sep="""/""" )
a = {}
for layer in checkpoint_info.keys():
a , a , a = get_key_and_tensorstore_dict(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if curr_real_layer_name in all_layers:
a = content
else:
a = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
a = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
a = torch.tensor(__lowerCamelCase )
a = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
a , a = rename_base_flax_keys(tuple(key.split("""/""" ) ) , __lowerCamelCase )
a = """/""".join(__lowerCamelCase )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
a = os.path.join(
__lowerCamelCase , weights_name.replace(""".bin""" , f'-{len(__lowerCamelCase )+1:05d}-of-???.bin' ) )
rename_and_save_block(__lowerCamelCase , __lowerCamelCase )
sharded_state_dicts.append(current_block.keys() )
del current_block
a = {}
a = 0
a = raw_weights.to(getattr(__lowerCamelCase , __lowerCamelCase ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
a = os.path.join(__lowerCamelCase , weights_name.replace(""".bin""" , f'-{len(__lowerCamelCase )+1:05d}-of-???.bin' ) )
rename_and_save_block(__lowerCamelCase , __lowerCamelCase )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(__lowerCamelCase ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
a = {}
a = {}
for idx, shard in enumerate(__lowerCamelCase ):
a = weights_name.replace(
""".bin""" , f'-{idx+1:05d}-of-{len(__lowerCamelCase ):05d}.bin' ) # len(sharded_state_dicts):05d}
a = os.path.join(__lowerCamelCase , weights_name.replace(""".bin""" , f'-{idx+1:05d}-of-???.bin' ) )
os.rename(__lowerCamelCase , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
a = shard
for key in shard:
a = shard_file
# Add the metadata
a = {"""total_size""": total_size}
a = {"""metadata""": metadata, """weight_map""": weight_map}
with open(os.path.join(__lowerCamelCase , __lowerCamelCase ) , """w""" , encoding="""utf-8""" ) as f:
a = json.dumps(__lowerCamelCase , indent=2 , sort_keys=__lowerCamelCase ) + """\n"""
f.write(__lowerCamelCase )
return metadata, index
if __name__ == "__main__":
__UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
type=str,
required=False,
help="Path to a directory containing a folder per layer. Follows the original Google format.",
)
parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
parser.add_argument(
"--pytorch_dump_folder_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
type=str,
required=False,
help="Path to the output pytorch model.",
)
__UpperCamelCase : Any = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def __A ( ) -> Tuple:
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
a = SwitchTransformersConfig.from_pretrained("""google/switch-base-8""" )
config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted""" )
a = SwitchTransformersForConditionalGeneration.from_pretrained(
"""/home/arthur_huggingface_co/transformers/switch_converted""" , device_map="""auto""" )
a = TaTokenizer.from_pretrained("""t5-small""" )
a = """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""
a = tokenizer(__lowerCamelCase , return_tensors="""pt""" ).input_ids
a = model.generate(__lowerCamelCase , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 347
| 0
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {'configuration_timm_backbone': ['TimmBackboneConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['TimmBackbone']
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 250
|
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class a__ ( lowerCamelCase_ , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : str = ReformerTokenizer
_SCREAMING_SNAKE_CASE : List[Any] = ReformerTokenizerFast
_SCREAMING_SNAKE_CASE : Any = True
_SCREAMING_SNAKE_CASE : Union[str, Any] = False
_SCREAMING_SNAKE_CASE : Optional[Any] = True
def _lowerCamelCase ( self ):
"""simple docstring"""
super().setUp()
_lowercase : Union[str, Any] = ReformerTokenizer(_UpperCamelCase , keep_accents=_UpperCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : List[Any] = "<s>"
_lowercase : str = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase ) , _UpperCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase ) , _UpperCamelCase )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : List[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "j" )
self.assertEqual(len(_UpperCamelCase ) , 1000 )
def _lowerCamelCase ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def _lowerCamelCase ( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_lowercase : List[str] = self.get_tokenizer()
_lowercase : int = self.get_rust_tokenizer()
_lowercase : Optional[Any] = "I was born in 92000, and this is falsé."
_lowercase : Union[str, Any] = tokenizer.tokenize(_UpperCamelCase )
_lowercase : int = rust_tokenizer.tokenize(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
_lowercase : Tuple = tokenizer.encode(_UpperCamelCase , add_special_tokens=_UpperCamelCase )
_lowercase : Any = rust_tokenizer.encode(_UpperCamelCase , add_special_tokens=_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
_lowercase : List[str] = self.get_rust_tokenizer()
_lowercase : Optional[int] = tokenizer.encode(_UpperCamelCase )
_lowercase : Any = rust_tokenizer.encode(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
def _lowerCamelCase ( self , _UpperCamelCase=15 ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_lowercase : Optional[Any] = self.rust_tokenizer_class.from_pretrained(_UpperCamelCase , **_UpperCamelCase )
# Simple input
_lowercase : Union[str, Any] = "This is a simple input"
_lowercase : Union[str, Any] = ["This is a simple input 1", "This is a simple input 2"]
_lowercase : Any = ("This is a simple input", "This is a pair")
_lowercase : List[str] = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(_UpperCamelCase , tokenizer_r.encode , _UpperCamelCase , max_length=_UpperCamelCase , padding="max_length" )
# Simple input
self.assertRaises(_UpperCamelCase , tokenizer_r.encode_plus , _UpperCamelCase , max_length=_UpperCamelCase , padding="max_length" )
# Simple input
self.assertRaises(
_UpperCamelCase , tokenizer_r.batch_encode_plus , _UpperCamelCase , max_length=_UpperCamelCase , padding="max_length" , )
# Pair input
self.assertRaises(_UpperCamelCase , tokenizer_r.encode , _UpperCamelCase , max_length=_UpperCamelCase , padding="max_length" )
# Pair input
self.assertRaises(_UpperCamelCase , tokenizer_r.encode_plus , _UpperCamelCase , max_length=_UpperCamelCase , padding="max_length" )
# Pair input
self.assertRaises(
_UpperCamelCase , tokenizer_r.batch_encode_plus , _UpperCamelCase , max_length=_UpperCamelCase , padding="max_length" , )
def _lowerCamelCase ( self ):
"""simple docstring"""
pass
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[Any] = ReformerTokenizer(_UpperCamelCase , keep_accents=_UpperCamelCase )
_lowercase : List[str] = tokenizer.tokenize("This is a test" )
self.assertListEqual(_UpperCamelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_UpperCamelCase ) , [285, 46, 10, 170, 382] , )
_lowercase : Optional[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
_UpperCamelCase , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
_lowercase : str = tokenizer.convert_tokens_to_ids(_UpperCamelCase )
self.assertListEqual(
_UpperCamelCase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_lowercase : Optional[int] = tokenizer.convert_ids_to_tokens(_UpperCamelCase )
self.assertListEqual(
_UpperCamelCase , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
@cached_property
def _lowerCamelCase ( self ):
"""simple docstring"""
return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment" )
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : int = "Hello World!"
_lowercase : Optional[Any] = [126, 32, 262, 152, 38, 72, 287]
self.assertListEqual(_UpperCamelCase , self.big_tokenizer.encode(_UpperCamelCase ) )
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[int] = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
_lowercase : Dict = [
108,
265,
24,
111,
4,
258,
156,
35,
28,
275,
3,
259,
297,
260,
84,
4,
35,
110,
44,
8,
259,
91,
268,
21,
11,
209,
274,
109,
266,
277,
117,
86,
93,
315,
258,
278,
258,
277,
258,
0,
258,
288,
258,
319,
258,
0,
258,
0,
258,
0,
258,
0,
258,
287,
258,
315,
258,
289,
258,
278,
99,
269,
266,
262,
8,
259,
241,
4,
217,
230,
268,
266,
55,
168,
106,
75,
193,
266,
223,
27,
49,
26,
282,
25,
264,
299,
19,
26,
0,
258,
277,
117,
86,
93,
176,
183,
270,
11,
262,
42,
61,
265,
]
self.assertListEqual(_UpperCamelCase , self.big_tokenizer.encode(_UpperCamelCase ) )
@require_torch
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
_lowercase : Any = list(self.big_tokenizer.get_vocab().keys() )[:10]
_lowercase : Tuple = " ".join(_UpperCamelCase )
_lowercase : str = self.big_tokenizer.encode_plus(_UpperCamelCase , return_tensors="pt" )
_lowercase : List[Any] = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors="pt" )
_lowercase : Optional[Any] = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
_lowercase : str = encoded_sequence["input_ids"].shape
_lowercase : Dict = ReformerModel(_UpperCamelCase )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_UpperCamelCase )
model(**_UpperCamelCase )
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Union[str, Any] = {"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
_lowercase : Optional[int] = [
"This is a very simple sentence.",
"The quick brown fox jumps over the lazy dog.",
]
self.tokenizer_integration_test_util(
expected_encoding=_UpperCamelCase , model_name="google/reformer-crime-and-punishment" , revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a" , padding=_UpperCamelCase , sequences=_UpperCamelCase , )
| 250
| 1
|
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
lowercase : Any = logging.get_logger(__name__)
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_=None , lowerCAmelCase_=None ):
"""simple docstring"""
if not conversation_id:
_snake_case = uuid.uuida()
if past_user_inputs is None:
_snake_case = []
if generated_responses is None:
_snake_case = []
_snake_case = conversation_id
_snake_case = past_user_inputs
_snake_case = generated_responses
_snake_case = text
def __eq__( self , lowerCAmelCase_ ):
"""simple docstring"""
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = False ):
"""simple docstring"""
if self.new_user_input:
if overwrite:
logger.warning(
F'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
F'with: "{text}".' )
_snake_case = text
else:
logger.warning(
F'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
F'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input' )
else:
_snake_case = text
def lowerCamelCase ( self ):
"""simple docstring"""
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
_snake_case = None
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
self.generated_responses.append(lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
"""simple docstring"""
_snake_case = F'Conversation id: {self.uuid} \n'
for is_user, text in self.iter_texts():
_snake_case = 'user' if is_user else 'bot'
output += F'{name} >> {text} \n'
return output
@add_end_docstrings(
_lowerCamelCase , r"""
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
""" , )
class __UpperCAmelCase ( _lowerCamelCase ):
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
super().__init__(*lowerCAmelCase_ , **lowerCAmelCase_ )
if self.tokenizer.pad_token_id is None:
_snake_case = self.tokenizer.eos_token
def lowerCamelCase ( self , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = {}
_snake_case = {}
_snake_case = {}
if min_length_for_response is not None:
_snake_case = min_length_for_response
if minimum_tokens is not None:
_snake_case = minimum_tokens
if "max_length" in generate_kwargs:
_snake_case = generate_kwargs['max_length']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
_snake_case = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(lowerCAmelCase_ )
return preprocess_params, forward_params, postprocess_params
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_=0 , **lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = super().__call__(lowerCAmelCase_ , num_workers=lowerCAmelCase_ , **lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and len(lowerCAmelCase_ ) == 1:
return outputs[0]
return outputs
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=32 ):
"""simple docstring"""
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise ValueError('ConversationalPipeline, expects Conversation as inputs' )
if conversation.new_user_input is None:
raise ValueError(
F'Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '
'Add user inputs with the conversation\'s `add_user_input` method' )
if hasattr(self.tokenizer , '_build_conversation_input_ids' ):
_snake_case = self.tokenizer._build_conversation_input_ids(lowerCAmelCase_ )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
_snake_case = self._legacy_parse_and_tokenize(lowerCAmelCase_ )
if self.framework == "pt":
_snake_case = torch.LongTensor([input_ids] )
elif self.framework == "tf":
_snake_case = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=10 , **lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = generate_kwargs.get('max_length' , self.model.config.max_length )
_snake_case = model_inputs['input_ids'].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})' )
_snake_case = max_length - minimum_tokens
_snake_case = model_inputs['input_ids'][:, -trim:]
if "attention_mask" in model_inputs:
_snake_case = model_inputs['attention_mask'][:, -trim:]
_snake_case = model_inputs.pop('conversation' )
_snake_case = max_length
_snake_case = self.model.generate(**lowerCAmelCase_ , **lowerCAmelCase_ )
if self.model.config.is_encoder_decoder:
_snake_case = 1
else:
_snake_case = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=True ):
"""simple docstring"""
_snake_case = model_outputs['output_ids']
_snake_case = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=lowerCAmelCase_ , clean_up_tokenization_spaces=lowerCAmelCase_ , )
_snake_case = model_outputs['conversation']
conversation.mark_processed()
conversation.append_response(lowerCAmelCase_ )
return conversation
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = self.tokenizer.eos_token_id
_snake_case = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) )
if len(lowerCAmelCase_ ) > self.tokenizer.model_max_length:
_snake_case = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 160
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def SCREAMING_SNAKE_CASE__ ( __A ) -> Dict:
_snake_case = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:] )
class __UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
__lowercase = StableDiffusionLatentUpscalePipeline
__lowercase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"""height""",
"""width""",
"""cross_attention_kwargs""",
"""negative_prompt_embeds""",
"""prompt_embeds""",
}
__lowercase = PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
__lowercase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
__lowercase = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__lowercase = frozenset([] )
__lowercase = True
@property
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = 1
_snake_case = 4
_snake_case = (16, 16)
_snake_case = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(lowerCAmelCase_ )
return image
def lowerCamelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_snake_case = UNetaDConditionModel(
act_fn='gelu' , attention_head_dim=8 , norm_num_groups=lowerCAmelCase_ , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=1_60 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=(
'KDownBlock2D',
'KCrossAttnDownBlock2D',
'KCrossAttnDownBlock2D',
'KCrossAttnDownBlock2D',
) , in_channels=8 , mid_block_type=lowerCAmelCase_ , only_cross_attention=lowerCAmelCase_ , out_channels=5 , resnet_time_scale_shift='scale_shift' , time_embedding_type='fourier' , timestep_post_act='gelu' , up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D') , )
_snake_case = AutoencoderKL(
block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[
'DownEncoderBlock2D',
'DownEncoderBlock2D',
'DownEncoderBlock2D',
'DownEncoderBlock2D',
] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
_snake_case = EulerDiscreteScheduler(prediction_type='sample' )
_snake_case = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act='quick_gelu' , projection_dim=5_12 , )
_snake_case = CLIPTextModel(lowerCAmelCase_ )
_snake_case = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_snake_case = {
'unet': model.eval(),
'vae': vae.eval(),
'scheduler': scheduler,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
}
return components
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=0 ):
"""simple docstring"""
if str(lowerCAmelCase_ ).startswith('mps' ):
_snake_case = torch.manual_seed(lowerCAmelCase_ )
else:
_snake_case = torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
_snake_case = {
'prompt': 'A painting of a squirrel eating a burger',
'image': self.dummy_image.cpu(),
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = 'cpu'
_snake_case = self.get_dummy_components()
_snake_case = self.pipeline_class(**lowerCAmelCase_ )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_snake_case = self.get_dummy_inputs(lowerCAmelCase_ )
_snake_case = pipe(**lowerCAmelCase_ ).images
_snake_case = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 2_56, 2_56, 3) )
_snake_case = np.array(
[0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055] )
_snake_case = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCAmelCase_ , 1E-3 )
def lowerCamelCase ( self ):
"""simple docstring"""
super().test_attention_slicing_forward_pass(expected_max_diff=7E-3 )
def lowerCamelCase ( self ):
"""simple docstring"""
super().test_cpu_offload_forward_pass(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
"""simple docstring"""
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def lowerCamelCase ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=7E-3 )
def lowerCamelCase ( self ):
"""simple docstring"""
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3 )
def lowerCamelCase ( self ):
"""simple docstring"""
super().test_save_load_local(expected_max_difference=3E-3 )
def lowerCamelCase ( self ):
"""simple docstring"""
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = [
'DDIMScheduler',
'DDPMScheduler',
'PNDMScheduler',
'HeunDiscreteScheduler',
'EulerAncestralDiscreteScheduler',
'KDPM2DiscreteScheduler',
'KDPM2AncestralDiscreteScheduler',
'DPMSolverSDEScheduler',
]
_snake_case = self.get_dummy_components()
_snake_case = self.pipeline_class(**lowerCAmelCase_ )
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=lowerCAmelCase_ )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_snake_case = self.get_dummy_inputs(lowerCAmelCase_ )
_snake_case = 2
_snake_case = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
_snake_case = getattr(lowerCAmelCase_ , scheduler_enum.name )
_snake_case = scheduler_cls.from_config(pipe.scheduler.config )
_snake_case = pipe(**lowerCAmelCase_ )[0]
outputs.append(lowerCAmelCase_ )
assert check_same_shape(lowerCAmelCase_ )
@require_torch_gpu
@slow
class __UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = torch.manual_seed(33 )
_snake_case = StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' , torch_dtype=torch.floataa )
pipe.to('cuda' )
_snake_case = StableDiffusionLatentUpscalePipeline.from_pretrained(
'stabilityai/sd-x2-latent-upscaler' , torch_dtype=torch.floataa )
upscaler.to('cuda' )
_snake_case = 'a photo of an astronaut high resolution, unreal engine, ultra realistic'
_snake_case = pipe(lowerCAmelCase_ , generator=lowerCAmelCase_ , output_type='latent' ).images
_snake_case = upscaler(
prompt=lowerCAmelCase_ , image=lowerCAmelCase_ , num_inference_steps=20 , guidance_scale=0 , generator=lowerCAmelCase_ , output_type='np' , ).images[0]
_snake_case = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy' )
assert np.abs((expected_image - image).mean() ) < 5E-2
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = torch.manual_seed(33 )
_snake_case = StableDiffusionLatentUpscalePipeline.from_pretrained(
'stabilityai/sd-x2-latent-upscaler' , torch_dtype=torch.floataa )
upscaler.to('cuda' )
_snake_case = 'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'
_snake_case = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png' )
_snake_case = upscaler(
prompt=lowerCAmelCase_ , image=lowerCAmelCase_ , num_inference_steps=20 , guidance_scale=0 , generator=lowerCAmelCase_ , output_type='np' , ).images[0]
_snake_case = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy' )
assert np.abs((expected_image - image).max() ) < 5E-2
| 160
| 1
|
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Dict = ["""image_processor""", """tokenizer"""]
lowerCamelCase_ : Optional[Any] = """FlavaImageProcessor"""
lowerCamelCase_ : Optional[Any] = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ ) -> List[Any]:
lowerCamelCase : Tuple = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , UpperCamelCase__ , )
lowerCamelCase : Dict = kwargs.pop("feature_extractor" )
lowerCamelCase : List[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : List[Any] = self.image_processor
def __call__( self , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = True , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = None , UpperCamelCase__ = 0 , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = True , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> Optional[int]:
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none." )
if text is not None:
lowerCamelCase : Union[str, Any] = self.tokenizer(
text=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , stride=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_token_type_ids=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , return_overflowing_tokens=UpperCamelCase__ , return_special_tokens_mask=UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , return_length=UpperCamelCase__ , verbose=UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ , )
if images is not None:
lowerCamelCase : str = self.image_processor(
UpperCamelCase__ , return_image_mask=UpperCamelCase__ , return_codebook_pixels=UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ , )
if text is not None and images is not None:
encoding.update(UpperCamelCase__ )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCamelCase__ ) , tensor_type=UpperCamelCase__ )
def _lowercase ( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
return self.tokenizer.batch_decode(*UpperCamelCase__ , **UpperCamelCase__ )
def _lowercase ( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
return self.tokenizer.decode(*UpperCamelCase__ , **UpperCamelCase__ )
@property
def _lowercase ( self ) -> str:
lowerCamelCase : Tuple = self.tokenizer.model_input_names
lowerCamelCase : Dict = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _lowercase ( self ) -> Tuple:
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , UpperCamelCase__ , )
return self.image_processor_class
@property
def _lowercase ( self ) -> Any:
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , UpperCamelCase__ , )
return self.image_processor
| 48
|
"""simple docstring"""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowercase ( _snake_case : List[Any] , _snake_case : Tuple , _snake_case : int ) ->List[Any]:
"""simple docstring"""
if openai_config_file == "":
__snake_case : Dict = OpenAIGPTConfig()
else:
__snake_case : int = OpenAIGPTConfig.from_json_file(_snake_case )
__snake_case : Tuple = OpenAIGPTModel(_snake_case )
# Load weights from numpy
load_tf_weights_in_openai_gpt(_snake_case , _snake_case , _snake_case )
# Save pytorch-model
__snake_case : str = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
__snake_case : Optional[Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(f"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(model.state_dict() , _snake_case )
print(f"""Save configuration file to {pytorch_config_dump_path}""" )
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--openai_checkpoint_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the TensorFlow checkpoint path.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--openai_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained OpenAI model. \n"""
"""This specifies the model architecture."""
),
)
SCREAMING_SNAKE_CASE : Optional[int] = parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
| 102
| 0
|
def __A ( _lowercase ):
'''simple docstring'''
if n == 1 or not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return 0
elif n == 2:
return 1
else:
_A = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def __A ( _lowercase ):
'''simple docstring'''
_A = 0
_A = 2
while digits < n:
index += 1
_A = len(str(fibonacci(SCREAMING_SNAKE_CASE_ ) ) )
return index
def __A ( _lowercase = 10_00 ):
'''simple docstring'''
return fibonacci_digits_index(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 366
|
import os
__A = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
def __A ( _lowercase ):
'''simple docstring'''
_A = 0
_A = 0
while index < len(_lowercase ) - 1:
_A = SYMBOLS[numerals[index]]
_A = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def __A ( _lowercase ):
'''simple docstring'''
_A = ''''''
_A = num // 10_00
numerals += m_count * "M"
num %= 10_00
_A = num // 1_00
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_00
_A = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def __A ( _lowercase = "/p089_roman.txt" ):
'''simple docstring'''
_A = 0
with open(os.path.dirname(_lowercase ) + roman_numerals_filename ) as filea:
_A = filea.readlines()
for line in lines:
_A = line.strip()
_A = parse_roman_numerals(_lowercase )
_A = generate_roman_numerals(_lowercase )
savings += len(_lowercase ) - len(_lowercase )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 75
| 0
|
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=[] ):
"""simple docstring"""
UpperCamelCase = size[0] - overlap_pixels * 2
UpperCamelCase = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
UpperCamelCase = np.ones((size_y, size_x) , dtype=np.uinta ) * 255
UpperCamelCase = np.pad(_SCREAMING_SNAKE_CASE , mode="linear_ramp" , pad_width=_SCREAMING_SNAKE_CASE , end_values=0 )
if "l" in remove_borders:
UpperCamelCase = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
UpperCamelCase = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
UpperCamelCase = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
UpperCamelCase = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return max(_SCREAMING_SNAKE_CASE , min(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = list(_SCREAMING_SNAKE_CASE )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
UpperCamelCase = clamp_rect(_SCREAMING_SNAKE_CASE , [0, 0] , [image_size[0], image_size[1]] )
return rect
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = Image.new("RGB" , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(_SCREAMING_SNAKE_CASE , (original_slice, 0) )
return result
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
UpperCamelCase = tile.crop(_SCREAMING_SNAKE_CASE )
return tile
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = n % d
return n - divisor
class _lowerCamelCase ( _lowercase ):
def __init__(self , __a , __a , __a , __a , __a , __a , __a = 3_50 , ) -> int:
super().__init__(
vae=__a , text_encoder=__a , tokenizer=__a , unet=__a , low_res_scheduler=__a , scheduler=__a , max_noise_level=__a , )
def snake_case_ (self , __a , __a , __a , __a , __a , __a , __a , **__a ) -> Tuple:
torch.manual_seed(0 )
UpperCamelCase = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
UpperCamelCase = add_overlap_rect(__a , __a , image.size )
UpperCamelCase = image.crop(__a )
UpperCamelCase = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
UpperCamelCase = translated_slice_x - (original_image_slice / 2)
UpperCamelCase = max(0 , __a )
UpperCamelCase = squeeze_tile(__a , __a , __a , __a )
UpperCamelCase = to_input.size
UpperCamelCase = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
UpperCamelCase = super(__a , self ).__call__(image=__a , **__a ).images[0]
UpperCamelCase = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
UpperCamelCase = unsqueeze_tile(__a , __a )
UpperCamelCase = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
UpperCamelCase = []
if x == 0:
remove_borders.append("l" )
elif crop_rect[2] == image.size[0]:
remove_borders.append("r" )
if y == 0:
remove_borders.append("t" )
elif crop_rect[3] == image.size[1]:
remove_borders.append("b" )
UpperCamelCase = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=__a ) , mode="L" , )
final_image.paste(
__a , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , __a )
@torch.no_grad()
def __call__(self , __a , __a , __a = 75 , __a = 9.0 , __a = 50 , __a = None , __a = 1 , __a = 0.0 , __a = None , __a = None , __a = None , __a = 1 , __a = 1_28 , __a = 32 , __a = 32 , ) -> int:
UpperCamelCase = Image.new("RGB" , (image.size[0] * 4, image.size[1] * 4) )
UpperCamelCase = math.ceil(image.size[0] / tile_size )
UpperCamelCase = math.ceil(image.size[1] / tile_size )
UpperCamelCase = tcx * tcy
UpperCamelCase = 0
for y in range(__a ):
for x in range(__a ):
self._process_tile(
__a , __a , __a , __a , __a , __a , __a , prompt=__a , num_inference_steps=__a , guidance_scale=__a , noise_level=__a , negative_prompt=__a , num_images_per_prompt=__a , eta=__a , generator=__a , latents=__a , )
current_count += 1
if callback is not None:
callback({"progress": current_count / total_tile_count, "image": final_image} )
return final_image
def a__ ( ):
"""simple docstring"""
UpperCamelCase = "stabilityai/stable-diffusion-x4-upscaler"
UpperCamelCase = StableDiffusionTiledUpscalePipeline.from_pretrained(_SCREAMING_SNAKE_CASE , revision="fp16" , torch_dtype=torch.floataa )
UpperCamelCase = pipe.to("cuda" )
UpperCamelCase = Image.open("../../docs/source/imgs/diffusers_library.jpg" )
def callback(_SCREAMING_SNAKE_CASE ):
print(F"progress: {obj['progress']:.4f}" )
obj["image"].save("diffusers_library_progress.jpg" )
UpperCamelCase = pipe(image=_SCREAMING_SNAKE_CASE , prompt="Black font, white background, vector" , noise_level=40 , callback=_SCREAMING_SNAKE_CASE )
final_image.save("diffusers_library.jpg" )
if __name__ == "__main__":
main()
| 153
|
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _lowerCamelCase ( _lowercase , unittest.TestCase ):
UpperCAmelCase_ = VideoToVideoSDPipeline
UpperCAmelCase_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"} ) - {"image", "width", "height"}
UpperCAmelCase_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"} ) - {"image"}
UpperCAmelCase_ = PipelineTesterMixin.required_optional_params - {"latents"}
UpperCAmelCase_ = False
# No `output_type`.
UpperCAmelCase_ = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def snake_case_ (self ) -> List[Any]:
torch.manual_seed(0 )
UpperCamelCase = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D") , up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D") , cross_attention_dim=32 , attention_head_dim=4 , )
UpperCamelCase = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=__a , set_alpha_to_one=__a , )
torch.manual_seed(0 )
UpperCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , sample_size=1_28 , )
torch.manual_seed(0 )
UpperCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act="gelu" , projection_dim=5_12 , )
UpperCamelCase = CLIPTextModel(__a )
UpperCamelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
UpperCamelCase = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def snake_case_ (self , __a , __a=0 ) -> Dict:
# 3 frames
UpperCamelCase = floats_tensor((1, 3, 3, 32, 32) , rng=random.Random(__a ) ).to(__a )
if str(__a ).startswith("mps" ):
UpperCamelCase = torch.manual_seed(__a )
else:
UpperCamelCase = torch.Generator(device=__a ).manual_seed(__a )
UpperCamelCase = {
"prompt": "A painting of a squirrel eating a burger",
"video": video,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "pt",
}
return inputs
def snake_case_ (self ) -> List[Any]:
UpperCamelCase = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase = self.get_dummy_components()
UpperCamelCase = VideoToVideoSDPipeline(**__a )
UpperCamelCase = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
UpperCamelCase = self.get_dummy_inputs(__a )
UpperCamelCase = "np"
UpperCamelCase = sd_pipe(**__a ).frames
UpperCamelCase = frames[0][-3:, -3:, -1]
assert frames[0].shape == (32, 32, 3)
UpperCamelCase = np.array([1_06, 1_17, 1_13, 1_74, 1_37, 1_12, 1_48, 1_51, 1_31] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def snake_case_ (self ) -> Dict:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__a , expected_max_diff=5e-3 )
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def snake_case_ (self ) -> List[Any]:
pass
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def snake_case_ (self ) -> Optional[Any]:
pass
@unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline." )
def snake_case_ (self ) -> Dict:
pass
def snake_case_ (self ) -> Optional[int]:
return super().test_progress_bar()
@slow
@skip_mps
class _lowerCamelCase ( unittest.TestCase ):
def snake_case_ (self ) -> List[str]:
UpperCamelCase = VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_XL" , torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
UpperCamelCase = torch.Generator(device="cpu" ).manual_seed(0 )
UpperCamelCase = torch.randn((1, 10, 3, 10_24, 5_76) , generator=__a )
UpperCamelCase = video.to("cuda" )
UpperCamelCase = "Spiderman is surfing"
UpperCamelCase = pipe(__a , video=__a , generator=__a , num_inference_steps=3 , output_type="pt" ).frames
UpperCamelCase = np.array([-1.0458984, -1.1279297, -0.9663086, -0.91503906, -0.75097656] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1e-2
| 153
| 1
|
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
lowerCamelCase__ : Tuple = 'pytorch_model.bin'
lowerCamelCase__ : Any = 'pytorch_model.bin.index.json'
lowerCamelCase__ : Any = 'adapter_config.json'
lowerCamelCase__ : List[str] = 'adapter_model.bin'
lowerCamelCase__ : Tuple = 'adapter_model.safetensors'
lowerCamelCase__ : Optional[Any] = 'tf_model.h5'
lowerCamelCase__ : Tuple = 'tf_model.h5.index.json'
lowerCamelCase__ : Optional[Any] = 'model.ckpt'
lowerCamelCase__ : Any = 'flax_model.msgpack'
lowerCamelCase__ : Dict = 'flax_model.msgpack.index.json'
lowerCamelCase__ : List[str] = 'model.safetensors'
lowerCamelCase__ : List[str] = 'model.safetensors.index.json'
lowerCamelCase__ : Any = 'config.json'
lowerCamelCase__ : int = 'preprocessor_config.json'
lowerCamelCase__ : Any = FEATURE_EXTRACTOR_NAME
lowerCamelCase__ : Any = 'generation_config.json'
lowerCamelCase__ : Optional[int] = 'modelcard.json'
lowerCamelCase__ : Optional[int] = '▁'
lowerCamelCase__ : Tuple = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
lowerCamelCase__ : Any = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
lowerCamelCase__ : Dict = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowerCamelCase__ : int = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def UpperCAmelCase_ ( __UpperCAmelCase : int ) -> Tuple:
if version.parse(__a ) < version.parse(__a ):
if "dev" in min_version:
SCREAMING_SNAKE_CASE_ = (
'''This example requires a source install from HuggingFace Transformers (see '''
'''`https://huggingface.co/docs/transformers/installation#install-from-source`),'''
)
else:
SCREAMING_SNAKE_CASE_ = f"This example requires a minimum version of {min_version},"
error_message += f" but the version found is {__version__}.\n"
raise ImportError(
error_message
+ 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
'versions of HuggingFace Transformers.' )
| 365
|
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> str:
SCREAMING_SNAKE_CASE_ = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> dict[str, str]:
SCREAMING_SNAKE_CASE_ = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
SCREAMING_SNAKE_CASE_ = remove_duplicates(key.upper() )
SCREAMING_SNAKE_CASE_ = len(__UpperCAmelCase )
# First fill cipher with key characters
SCREAMING_SNAKE_CASE_ = {alphabet[i]: char for i, char in enumerate(__UpperCAmelCase )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(__UpperCAmelCase ) , 26 ):
SCREAMING_SNAKE_CASE_ = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
SCREAMING_SNAKE_CASE_ = alphabet[i - offset]
SCREAMING_SNAKE_CASE_ = char
return cipher_alphabet
def UpperCAmelCase_ ( __UpperCAmelCase : str , __UpperCAmelCase : dict[str, str] ) -> str:
return "".join(cipher_map.get(__UpperCAmelCase , __UpperCAmelCase ) for ch in message.upper() )
def UpperCAmelCase_ ( __UpperCAmelCase : str , __UpperCAmelCase : dict[str, str] ) -> str:
SCREAMING_SNAKE_CASE_ = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(__UpperCAmelCase , __UpperCAmelCase ) for ch in message.upper() )
def UpperCAmelCase_ ( ) -> None:
SCREAMING_SNAKE_CASE_ = input('Enter message to encode or decode: ' ).strip()
SCREAMING_SNAKE_CASE_ = input('Enter keyword: ' ).strip()
SCREAMING_SNAKE_CASE_ = input('Encipher or decipher? E/D:' ).strip()[0].lower()
try:
SCREAMING_SNAKE_CASE_ = {'e': encipher, 'd': decipher}[option]
except KeyError:
raise KeyError('invalid input option' )
SCREAMING_SNAKE_CASE_ = create_cipher_map(__UpperCAmelCase )
print(func(__UpperCAmelCase , __UpperCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 210
| 0
|
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def SCREAMING_SNAKE_CASE__ ( ) -> Optional[Any]:
__lowerCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
'-m' , '--pretrained_model_name_or_path' , type=lowerCamelCase__ , default=lowerCamelCase__ , required=lowerCamelCase__ , help='Path to pretrained model or model identifier from huggingface.co/models.' , )
parser.add_argument(
'-c' , '--caption' , type=lowerCamelCase__ , default='robotic cat with wings' , help='Text used to generate images.' , )
parser.add_argument(
'-n' , '--images_num' , type=lowerCamelCase__ , default=4 , help='How much images to generate.' , )
parser.add_argument(
'-s' , '--seed' , type=lowerCamelCase__ , default=4_2 , help='Seed for random process.' , )
parser.add_argument(
'-ci' , '--cuda_id' , type=lowerCamelCase__ , default=0 , help='cuda_id.' , )
__lowerCamelCase : Any = parser.parse_args()
return args
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
if not len(lowerCamelCase__ ) == rows * cols:
raise ValueError('The specified number of rows and columns are not correct.' )
__lowerCamelCase , __lowerCamelCase : Optional[int] = imgs[0].size
__lowerCamelCase : List[Any] = Image.new('RGB' , size=(cols * w, rows * h) )
__lowerCamelCase , __lowerCamelCase : List[str] = grid.size
for i, img in enumerate(lowerCamelCase__ ):
grid.paste(lowerCamelCase__ , box=(i % cols * w, i // cols * h) )
return grid
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__="robotic cat with wings" , lowerCamelCase__=7.5 , lowerCamelCase__=5_0 , lowerCamelCase__=1 , lowerCamelCase__=4_2 , ) -> int:
__lowerCamelCase : Any = torch.Generator(pipeline.device ).manual_seed(lowerCamelCase__ )
__lowerCamelCase : int = pipeline(
lowerCamelCase__ , guidance_scale=lowerCamelCase__ , num_inference_steps=lowerCamelCase__ , generator=lowerCamelCase__ , num_images_per_prompt=lowerCamelCase__ , ).images
__lowerCamelCase : Union[str, Any] = int(math.sqrt(lowerCamelCase__ ) )
__lowerCamelCase : str = image_grid(lowerCamelCase__ , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
a =parse_args()
# Load models and create wrapper for stable diffusion
a =CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
a =CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
a =AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
a =UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")
a =StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
a =lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
a =load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, """unet""", unet)
else:
a =unet.to(torch.device("""cuda""", args.cuda_id))
a =pipeline.to(unet.device)
a , a =generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
a =os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))
| 73
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class __A (snake_case__):
'''simple docstring'''
def __init__( self : str , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : int ) ->None:
"""simple docstring"""
warnings.warn(
"""The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use MobileViTImageProcessor instead.""" , UpperCAmelCase_ , )
super().__init__(*UpperCAmelCase_ , **UpperCAmelCase_ )
| 347
| 0
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
"umberto-commoncrawl-cased-v1": (
"https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
),
"umberto-wikipedia-uncased-v1": (
"https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
),
}
class lowercase ( A__ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """camembert"""
def __init__( self , _snake_case=3_0522 , _snake_case=768 , _snake_case=12 , _snake_case=12 , _snake_case=3072 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=2 , _snake_case=0.02 , _snake_case=1e-12 , _snake_case=1 , _snake_case=0 , _snake_case=2 , _snake_case="absolute" , _snake_case=True , _snake_case=None , **_snake_case , ) -> Any:
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , **_snake_case )
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = hidden_act
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = position_embedding_type
UpperCAmelCase = use_cache
UpperCAmelCase = classifier_dropout
class lowercase ( A__ ):
'''simple docstring'''
@property
def snake_case_ ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
UpperCAmelCase = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCAmelCase = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 363
|
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
__magic_name__ = logging.getLogger()
def _lowerCAmelCase ( A__: Path , A__: list ):
'''simple docstring'''
UpperCAmelCase = '''\n'''.join(A__ )
Path(A__ ).open('''w''' ).writelines(A__ )
__magic_name__ = "patrickvonplaten/t5-tiny-random"
__magic_name__ = "sshleifer/bart-tiny-random"
__magic_name__ = "sshleifer/tiny-mbart"
__magic_name__ = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowercase ( A__ ):
'''simple docstring'''
def snake_case_ ( self , _snake_case ) -> int:
"""simple docstring"""
UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / '''utest_input.source'''
UpperCAmelCase = input_file_name.parent / '''utest_output.txt'''
assert not output_file_name.exists()
UpperCAmelCase = [''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
_dump_articles(_snake_case , _snake_case )
UpperCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / '''scores.json''' )
UpperCAmelCase = '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
UpperCAmelCase = f"""
run_eval_search.py
{model}
{input_file_name}
{output_file_name}
--score_path {score_path}
--task {task}
--num_beams 2
--length_penalty 2.0
""".split()
with patch.object(_snake_case , '''argv''' , _snake_case ):
run_generate()
assert Path(_snake_case ).exists()
# os.remove(Path(output_file_name))
def snake_case_ ( self ) -> Dict:
"""simple docstring"""
self.run_eval_tester(_snake_case )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def snake_case_ ( self , _snake_case ) -> Any:
"""simple docstring"""
self.run_eval_tester(_snake_case )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def snake_case_ ( self , _snake_case ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / '''utest_input.source'''
UpperCAmelCase = input_file_name.parent / '''utest_output.txt'''
assert not output_file_name.exists()
UpperCAmelCase = {
'''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
'''de''': [
'''Maschinelles Lernen ist großartig, oder?''',
'''Ich esse gerne Bananen''',
'''Morgen ist wieder ein toller Tag!''',
],
}
UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() )
UpperCAmelCase = str(tmp_dir / '''scores.json''' )
UpperCAmelCase = str(tmp_dir / '''val.target''' )
_dump_articles(_snake_case , text['''en'''] )
_dump_articles(_snake_case , text['''de'''] )
UpperCAmelCase = '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
UpperCAmelCase = f"""
run_eval_search.py
{model}
{str(_snake_case )}
{str(_snake_case )}
--score_path {score_path}
--reference_path {reference_path}
--task {task}
""".split()
testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0'''] )
with patch.object(_snake_case , '''argv''' , _snake_case ):
with CaptureStdout() as cs:
run_search()
UpperCAmelCase = [''' num_beams | length_penalty''', model, '''Best score args''']
UpperCAmelCase = ['''Info''']
if "translation" in task:
expected_strings.append('''bleu''' )
else:
expected_strings.extend(_snake_case )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(_snake_case ).exists()
os.remove(Path(_snake_case ) )
| 152
| 0
|
"""simple docstring"""
A = 9.80665
def __A ( a_ :float , a_ :float , a_ :float = g) -> float:
if fluid_density <= 0:
raise ValueError('''Impossible fluid density''')
if volume < 0:
raise ValueError('''Impossible Object volume''')
if gravity <= 0:
raise ValueError('''Impossible Gravity''')
return fluid_density * gravity * volume
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
| 160
|
"""simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
A = logging.get_logger('''transformers.models.encodec''')
A = {
'''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
'''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
'''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
'''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
A = {
'''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
'''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
'''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
'''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
'''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
'''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
'''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
'''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
'''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
'''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
'''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
'''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
'''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
'''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
'''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
'''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
'''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
'''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
'''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
A = {
'''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
'''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
'''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
'''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
'''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
'''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
'''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
'''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
'''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
'''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
'''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
'''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
'''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
'''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
'''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
'''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
'''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
'''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
A = {
'''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
'''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
'''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
'''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
'''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
'''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
'''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
'''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
'''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
'''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
'''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
'''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
'''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
'''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
'''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
'''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
'''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
'''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
'''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
A = {
'''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
'''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
'''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
'''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
'''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
'''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
'''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
'''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
'''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
'''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
'''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
'''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
'''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
'''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
'''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
'''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
'''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
'''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
A = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
A = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
A = []
A = []
def __A ( a_ :Optional[int] , a_ :str , a_ :Optional[Any] , a_ :Optional[Any] , a_ :Tuple) -> str:
for attribute in key.split('''.'''):
__a : Union[str, Any] = getattr(a_ , a_)
if weight_type is not None:
__a : Optional[Any] = getattr(a_ , a_).shape
else:
__a : Optional[int] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""")
if weight_type == "weight":
__a : Tuple = value
elif weight_type == "weight_g":
__a : List[str] = value
elif weight_type == "weight_v":
__a : Optional[int] = value
elif weight_type == "bias":
__a : List[str] = value
elif weight_type == "running_mean":
__a : List[str] = value
elif weight_type == "running_var":
__a : List[Any] = value
elif weight_type == "num_batches_tracked":
__a : List[Any] = value
elif weight_type == "weight_ih_l0":
__a : Optional[int] = value
elif weight_type == "weight_hh_l0":
__a : Any = value
elif weight_type == "bias_ih_l0":
__a : Union[str, Any] = value
elif weight_type == "bias_hh_l0":
__a : Optional[Any] = value
elif weight_type == "weight_ih_l1":
__a : Dict = value
elif weight_type == "weight_hh_l1":
__a : str = value
elif weight_type == "bias_ih_l1":
__a : Union[str, Any] = value
elif weight_type == "bias_hh_l1":
__a : Union[str, Any] = value
else:
__a : str = value
logger.info(F"""{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.""")
def __A ( a_ :Dict , a_ :Any) -> Tuple:
for key in ignore_keys:
if key.endswith('''.*'''):
if name.startswith(key[:-1]):
return True
elif ".*." in key:
__a , __a : Union[str, Any] = key.split('''.*.''')
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __A ( a_ :Optional[Any] , a_ :Tuple , a_ :List[str]) -> Any:
__a : Tuple = []
if model_name == "encodec_24khz" or "encodec_32khz":
__a : int = MAPPING_24K
elif model_name == "encodec_48khz":
__a : List[str] = MAPPING_48K
else:
raise ValueError(F"""Unsupported model: {model_name}""")
for name, value in orig_dict.items():
if should_ignore(a_ , a_):
logger.info(F"""{name} was ignored""")
continue
__a : Tuple = False
for key, mapped_key in MAPPING.items():
if "*" in key:
__a , __a : Optional[Any] = key.split('''.*.''')
if prefix in name and suffix in name:
__a : int = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('''embed''') and name.endswith('''embed_avg'''):
continue
__a : List[str] = True
if "*" in mapped_key:
__a : Optional[Any] = name.split(a_)[0].split('''.''')[-2]
__a : str = mapped_key.replace('''*''' , a_)
if "weight_g" in name:
__a : Optional[Any] = '''weight_g'''
elif "weight_v" in name:
__a : Optional[Any] = '''weight_v'''
elif "weight_ih_l0" in name:
__a : Tuple = '''weight_ih_l0'''
elif "weight_hh_l0" in name:
__a : Tuple = '''weight_hh_l0'''
elif "bias_ih_l0" in name:
__a : List[str] = '''bias_ih_l0'''
elif "bias_hh_l0" in name:
__a : int = '''bias_hh_l0'''
elif "weight_ih_l1" in name:
__a : Optional[int] = '''weight_ih_l1'''
elif "weight_hh_l1" in name:
__a : List[str] = '''weight_hh_l1'''
elif "bias_ih_l1" in name:
__a : List[str] = '''bias_ih_l1'''
elif "bias_hh_l1" in name:
__a : str = '''bias_hh_l1'''
elif "bias" in name:
__a : Union[str, Any] = '''bias'''
elif "weight" in name:
__a : Any = '''weight'''
elif "running_mean" in name:
__a : List[Any] = '''running_mean'''
elif "running_var" in name:
__a : int = '''running_var'''
elif "num_batches_tracked" in name:
__a : int = '''num_batches_tracked'''
else:
__a : List[str] = None
set_recursively(a_ , a_ , a_ , a_ , a_)
continue
if not is_used:
unused_weights.append(a_)
logger.warning(F"""Unused weights: {unused_weights}""")
@torch.no_grad()
def __A ( a_ :Dict , a_ :Optional[int] , a_ :Union[str, Any] , a_ :Any=None , a_ :Tuple=None , ) -> List[Any]:
if config_path is not None:
__a : List[str] = EncodecConfig.from_pretrained(a_)
else:
__a : List[Any] = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
__a : List[Any] = [8, 5, 4, 4]
__a : int = [2.2]
__a : int = 64
__a : List[Any] = 3_20_00
__a : Union[str, Any] = 20_48
__a : Optional[int] = False
__a : str = False
__a : Dict = False
elif model_name == "encodec_48khz":
__a : Any = [8, 5, 4, 2]
__a : Dict = [3.0, 6.0, 1_2.0, 2_4.0]
__a : List[Any] = 4_80_00
__a : Dict = 2
__a : int = False
__a : List[str] = '''time_group_norm'''
__a : str = True
__a : Dict = 1.0
__a : Optional[Any] = 0.0_1
else:
raise ValueError(F"""Unknown model name: {model_name}""")
__a : Union[str, Any] = EncodecModel(a_)
__a : List[Any] = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(a_)
__a : List[Any] = torch.load(a_)
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
__a : Optional[Any] = original_checkpoint['''best_state''']
recursively_load_weights(a_ , a_ , a_)
model.save_pretrained(a_)
if repo_id:
print('''Pushing to the hub...''')
feature_extractor.push_to_hub(a_)
model.push_to_hub(a_)
if __name__ == "__main__":
A = argparse.ArgumentParser()
parser.add_argument(
'''--model''',
default='''encodec_24khz''',
type=str,
help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
)
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
A = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 160
| 1
|
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__SCREAMING_SNAKE_CASE : Dict = get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
__SCREAMING_SNAKE_CASE : str = json.load(f)
@require_torch
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
def snake_case__ ( self , lowerCamelCase__ ):
return FSMTTokenizer.from_pretrained(snake_case__ )
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = FSMTForConditionalGeneration.from_pretrained(snake_case__ ).to(snake_case__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
['''en-ru''', 2_6.0],
['''ru-en''', 2_2.0],
['''en-de''', 2_2.0],
['''de-en''', 2_9.0],
] )
@slow
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = F"""facebook/wmt19-{pair}"""
_lowerCamelCase = self.get_tokenizer(snake_case__ )
_lowerCamelCase = self.get_model(snake_case__ )
_lowerCamelCase = bleu_data[pair]["src"]
_lowerCamelCase = bleu_data[pair]["tgt"]
_lowerCamelCase = tokenizer(snake_case__ , return_tensors='''pt''' , truncation=snake_case__ , padding='''longest''' ).to(snake_case__ )
_lowerCamelCase = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
_lowerCamelCase = tokenizer.batch_decode(
snake_case__ , skip_special_tokens=snake_case__ , clean_up_tokenization_spaces=snake_case__ )
_lowerCamelCase = calculate_bleu(snake_case__ , snake_case__ )
print(snake_case__ )
self.assertGreaterEqual(scores['''bleu'''] , snake_case__ )
| 360
|
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_( lowercase_ : list , lowercase_ : int ) -> Tuple:
# Checks if the entire collection has been sorted
if len(lowercase_ ) <= 1 or n <= 1:
return
insert_next(lowercase_ , n - 1 )
rec_insertion_sort(lowercase_ , n - 1 )
def lowerCAmelCase_( lowercase_ : list , lowercase_ : int ) -> Any:
# Checks order between adjacent elements
if index >= len(lowercase_ ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
_lowerCamelCase , _lowerCamelCase = (
collection[index],
collection[index - 1],
)
insert_next(lowercase_ , index + 1 )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = input('''Enter integers separated by spaces: ''')
__SCREAMING_SNAKE_CASE : list[int] = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| 73
| 0
|
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 10**-10 ):
'''simple docstring'''
_lowerCAmelCase : Dict = a
while True:
_lowerCAmelCase : List[Any] = Decimal(_lowerCamelCase ) - (
Decimal(eval(_lowerCamelCase ) ) / Decimal(eval(str(diff(_lowerCamelCase ) ) ) ) # noqa: S307
)
# This number dictates the accuracy of the answer
if abs(eval(_lowerCamelCase ) ) < precision: # noqa: S307
return float(_lowerCamelCase )
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
# Find root of polynomial
print(f'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
# Find Square Root of 5
print(f'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
# Exponential Roots
print(f'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')
| 36
|
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __UpperCamelCase :
@staticmethod
def lowercase__ ( *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
lowercase : int =MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =pipeline(
'''zero-shot-object-detection''', model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase_ =[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =object_detector(examples[0], threshold=0.0 )
lowerCamelCase_ =len(lowerCAmelCase )
self.assertGreater(lowerCAmelCase, 0 )
self.assertEqual(
lowerCAmelCase, [
{
'''score''': ANY(lowerCAmelCase ),
'''label''': ANY(lowerCAmelCase ),
'''box''': {'''xmin''': ANY(lowerCAmelCase ), '''ymin''': ANY(lowerCAmelCase ), '''xmax''': ANY(lowerCAmelCase ), '''ymax''': ANY(lowerCAmelCase )},
}
for i in range(lowerCAmelCase )
], )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =pipeline(
'''zero-shot-object-detection''', model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase_ =object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''', candidate_labels=['''cat''', '''remote''', '''couch'''], threshold=0.6_4, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
], )
lowerCamelCase_ =object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
], threshold=0.6_4, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
[
{'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
]
], )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
], )
lowerCamelCase_ =object_detector(
[
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
], )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
[
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
[
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
], )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =0.2
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], threshold=lowerCAmelCase, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
], )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =2
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], top_k=lowerCAmelCase, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
], )
| 75
| 0
|
def _A ( __magic_name__ ):
return "".join([hex(__magic_name__ )[2:].zfill(2 ).upper() for byte in list(__magic_name__ )] )
def _A ( __magic_name__ ):
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(__magic_name__ ) % 2) != 0:
raise ValueError(
"Base16 encoded data is invalid:\nData does not have an even number of hex digits." )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(__magic_name__ ) <= set("0123456789ABCDEF" ):
raise ValueError(
"Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters." )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(__magic_name__ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AutoformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"""AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AutoformerForPrediction""",
"""AutoformerModel""",
"""AutoformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 201
| 0
|
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowercase_ = XGLMConfig
lowercase_ = {}
lowercase_ = "gelu"
def __init__(self : Optional[Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Optional[int]=14 , UpperCAmelCase_ : int=7 , UpperCAmelCase_ : str=True , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : int=True , UpperCAmelCase_ : Dict=99 , UpperCAmelCase_ : Optional[int]=32 , UpperCAmelCase_ : str=2 , UpperCAmelCase_ : List[str]=4 , UpperCAmelCase_ : List[str]=37 , UpperCAmelCase_ : int="gelu" , UpperCAmelCase_ : Tuple=0.1 , UpperCAmelCase_ : Union[str, Any]=0.1 , UpperCAmelCase_ : Any=512 , UpperCAmelCase_ : List[str]=0.02 , ) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: str =parent
lowerCamelCase__: int =batch_size
lowerCamelCase__: List[str] =seq_length
lowerCamelCase__: int =is_training
lowerCamelCase__: int =use_input_mask
lowerCamelCase__: Tuple =use_labels
lowerCamelCase__: Any =vocab_size
lowerCamelCase__: Dict =d_model
lowerCamelCase__: Optional[int] =num_hidden_layers
lowerCamelCase__: Tuple =num_attention_heads
lowerCamelCase__: Dict =ffn_dim
lowerCamelCase__: Optional[int] =activation_function
lowerCamelCase__: str =activation_dropout
lowerCamelCase__: int =attention_dropout
lowerCamelCase__: Optional[Any] =max_position_embeddings
lowerCamelCase__: Optional[int] =initializer_range
lowerCamelCase__: str =None
lowerCamelCase__: List[Any] =0
lowerCamelCase__: str =2
lowerCamelCase__: Union[str, Any] =1
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->Union[str, Any]:
'''simple docstring'''
return XGLMConfig.from_pretrained("facebook/xglm-564M")
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->int:
'''simple docstring'''
lowerCamelCase__: Dict =tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size) , clip_value_min=0 , clip_value_max=3)
lowerCamelCase__: str =None
if self.use_input_mask:
lowerCamelCase__: Tuple =random_attention_mask([self.batch_size, self.seq_length])
lowerCamelCase__: Dict =self.get_config()
lowerCamelCase__: Optional[Any] =floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2)
return (
config,
input_ids,
input_mask,
head_mask,
)
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->int:
'''simple docstring'''
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=UpperCAmelCase_ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=UpperCAmelCase_ , )
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->Any:
'''simple docstring'''
lowerCamelCase__: str =self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
): Union[str, Any] =config_and_inputs
lowerCamelCase__: str ={
"input_ids": input_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_tf
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
lowercase_ = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
lowercase_ = (TFXGLMForCausalLM,) if is_tf_available() else ()
lowercase_ = (
{"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->Tuple:
'''simple docstring'''
lowerCamelCase__: Tuple =TFXGLMModelTester(self)
lowerCamelCase__: str =ConfigTester(self , config_class=UpperCAmelCase_ , n_embd=37)
def SCREAMING_SNAKE_CASE_ (self : str) ->str:
'''simple docstring'''
self.config_tester.run_common_tests()
@slow
def SCREAMING_SNAKE_CASE_ (self : Optional[int]) ->Dict:
'''simple docstring'''
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase__: Dict =TFXGLMModel.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor.")
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->Dict:
'''simple docstring'''
super().test_resize_token_embeddings()
@require_tf
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE_ (self : Any , UpperCAmelCase_ : str=True) ->Any:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
lowerCamelCase__: Optional[int] =tf.convert_to_tensor([[2, 268, 9_865]] , dtype=tf.intaa) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
lowerCamelCase__: Union[str, Any] =[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
# fmt: on
lowerCamelCase__: List[str] =model.generate(UpperCAmelCase_ , do_sample=UpperCAmelCase_ , num_beams=1)
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , UpperCAmelCase_)
@slow
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: Optional[int] =XGLMTokenizer.from_pretrained("facebook/xglm-564M")
lowerCamelCase__: Tuple =TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
tf.random.set_seed(0)
lowerCamelCase__: Union[str, Any] =tokenizer("Today is a nice day and" , return_tensors="tf")
lowerCamelCase__: Dict =tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(":/CPU:0"):
lowerCamelCase__: str =model.generate(UpperCAmelCase_ , do_sample=UpperCAmelCase_ , seed=[7, 0])
lowerCamelCase__: str =tokenizer.decode(output_ids[0] , skip_special_tokens=UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =(
"Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
)
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_)
@slow
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->List[str]:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
lowerCamelCase__: Tuple =XGLMTokenizer.from_pretrained("facebook/xglm-564M")
lowerCamelCase__: str ="left"
# use different length sentences to test batching
lowerCamelCase__: List[Any] =[
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When",
"Hello, my dog is a little",
]
lowerCamelCase__: Optional[Any] =tokenizer(UpperCAmelCase_ , return_tensors="tf" , padding=UpperCAmelCase_)
lowerCamelCase__: Optional[int] =inputs["input_ids"]
lowerCamelCase__: str =model.generate(input_ids=UpperCAmelCase_ , attention_mask=inputs["attention_mask"] , max_new_tokens=12)
lowerCamelCase__: Tuple =tokenizer(sentences[0] , return_tensors="tf").input_ids
lowerCamelCase__: Union[str, Any] =model.generate(input_ids=UpperCAmelCase_ , max_new_tokens=12)
lowerCamelCase__: Optional[Any] =tokenizer(sentences[1] , return_tensors="tf").input_ids
lowerCamelCase__: Union[str, Any] =model.generate(input_ids=UpperCAmelCase_ , max_new_tokens=12)
lowerCamelCase__: Optional[Any] =tokenizer.batch_decode(UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_)
lowerCamelCase__: Any =tokenizer.decode(output_non_padded[0] , skip_special_tokens=UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =tokenizer.decode(output_padded[0] , skip_special_tokens=UpperCAmelCase_)
lowerCamelCase__: int =[
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
"a single",
"Hello, my dog is a little bit of a shy one, but he is very friendly",
]
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , [non_padded_sentence, padded_sentence])
| 10
|
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _UpperCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
__lowercase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCAmelCase__ , '''hidden_sizes''' ) )
self.parent.assertTrue(hasattr(lowerCAmelCase__ , '''num_attention_heads''' ) )
class _UpperCamelCase :
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=13 , lowerCAmelCase__=64 , lowerCAmelCase__=3 , lowerCAmelCase__=3 , lowerCAmelCase__=2 , lowerCAmelCase__=1 , lowerCAmelCase__=16 , lowerCAmelCase__=[1_28, 2_56, 3_84] , lowerCAmelCase__=[4, 6, 8] , lowerCAmelCase__=[2, 3, 4] , lowerCAmelCase__=[16, 16, 16] , lowerCAmelCase__=0 , lowerCAmelCase__=[2, 2, 2] , lowerCAmelCase__=[2, 2, 2] , lowerCAmelCase__=0.02 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=2 , ) -> Tuple:
'''simple docstring'''
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = num_channels
__lowercase = kernel_size
__lowercase = stride
__lowercase = padding
__lowercase = hidden_sizes
__lowercase = num_attention_heads
__lowercase = depths
__lowercase = key_dim
__lowercase = drop_path_rate
__lowercase = patch_size
__lowercase = attention_ratio
__lowercase = mlp_ratio
__lowercase = initializer_range
__lowercase = [
['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
__lowercase = is_training
__lowercase = use_labels
__lowercase = num_labels
__lowercase = initializer_range
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.num_labels )
__lowercase = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
return LevitConfig(
image_size=self.image_size , num_channels=self.num_channels , kernel_size=self.kernel_size , stride=self.stride , padding=self.padding , patch_size=self.patch_size , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , depths=self.depths , key_dim=self.key_dim , drop_path_rate=self.drop_path_rate , mlp_ratio=self.mlp_ratio , attention_ratio=self.attention_ratio , initializer_range=self.initializer_range , down_ops=self.down_ops , )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
__lowercase = LevitModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
__lowercase = model(lowerCAmelCase__ )
__lowercase = (self.image_size, self.image_size)
__lowercase , __lowercase = image_size[0], image_size[1]
for _ in range(4 ):
__lowercase = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
__lowercase = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) , )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> str:
'''simple docstring'''
__lowercase = self.num_labels
__lowercase = LevitForImageClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
__lowercase = model(lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( _UpperCAmelCase ,_UpperCAmelCase ,unittest.TestCase ):
"""simple docstring"""
__a : int = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
__a : List[str] = (
{
'''feature-extraction''': LevitModel,
'''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
__a : int = False
__a : Dict = False
__a : Optional[Any] = False
__a : Optional[int] = False
__a : Dict = False
def _SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
__lowercase = LevitModelTester(self )
__lowercase = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
return
@unittest.skip(reason='''Levit does not use inputs_embeds''' )
def _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''Levit does not support input and output embeddings''' )
def _SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip(reason='''Levit does not output attentions''' )
def _SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowerCAmelCase__ )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
def check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
__lowercase = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
__lowercase = outputs.hidden_states
__lowercase = len(self.model_tester.depths ) + 1
self.assertEqual(len(lowerCAmelCase__ ) , lowerCAmelCase__ )
__lowercase = (self.model_tester.image_size, self.model_tester.image_size)
__lowercase , __lowercase = image_size[0], image_size[1]
for _ in range(4 ):
__lowercase = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
__lowercase = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [
height * width,
self.model_tester.hidden_sizes[0],
] , )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def _SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=False ) -> str:
'''simple docstring'''
__lowercase = super()._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ , return_labels=lowerCAmelCase__ )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
if not self.model_tester.is_training:
return
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(lowerCAmelCase__ )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
__lowercase = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.train()
__lowercase = self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ , return_labels=lowerCAmelCase__ )
__lowercase = model(**lowerCAmelCase__ ).loss
loss.backward()
def _SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
__lowercase = False
__lowercase = True
for model_class in self.all_model_classes:
if model_class in get_values(lowerCAmelCase__ ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
__lowercase = model_class(lowerCAmelCase__ )
model.gradient_checkpointing_enable()
model.to(lowerCAmelCase__ )
model.train()
__lowercase = self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ , return_labels=lowerCAmelCase__ )
__lowercase = model(**lowerCAmelCase__ ).loss
loss.backward()
def _SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = [
{'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
{'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
{'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(lowerCAmelCase__ ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}" ):
__lowercase = problem_type['''title''']
__lowercase = problem_type['''num_labels''']
__lowercase = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.train()
__lowercase = self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ , return_labels=lowerCAmelCase__ )
if problem_type["num_labels"] > 1:
__lowercase = inputs['''labels'''].unsqueeze(1 ).repeat(1 , problem_type['''num_labels'''] )
__lowercase = inputs['''labels'''].to(problem_type['''dtype'''] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=lowerCAmelCase__ ) as warning_list:
__lowercase = model(**lowerCAmelCase__ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F"Something is going wrong in the regression problem: intercepted {w.message}" )
loss.backward()
@slow
def _SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = LevitModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def UpperCAmelCase ( ):
"""simple docstring"""
__lowercase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def _SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
__lowercase = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
lowerCAmelCase__ )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=lowerCAmelCase__ , return_tensors='''pt''' ).to(lowerCAmelCase__ )
# forward pass
with torch.no_grad():
__lowercase = model(**lowerCAmelCase__ )
# verify the logits
__lowercase = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase__ )
__lowercase = torch.tensor([1.0448, -0.3745, -1.8317] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
| 210
| 0
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
UpperCamelCase__ = 16
UpperCamelCase__ = 32
def _UpperCamelCase (a__ :Accelerator , a__ :int = 16 ):
"""simple docstring"""
UpperCamelCase__ = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase__ = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(a__ :Any ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase__ = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=a__ , max_length=a__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase__ = datasets.map(
a__ , batched=a__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase__ = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(a__ :List[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase__ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase__ = 16
elif accelerator.mixed_precision != "no":
UpperCamelCase__ = 8
else:
UpperCamelCase__ = None
return tokenizer.pad(
a__ , padding="""longest""" , max_length=a__ , pad_to_multiple_of=a__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
UpperCamelCase__ = DataLoader(
tokenized_datasets["""train"""] , shuffle=a__ , collate_fn=a__ , batch_size=a__ )
UpperCamelCase__ = DataLoader(
tokenized_datasets["""validation"""] , shuffle=a__ , collate_fn=a__ , batch_size=a__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
UpperCamelCase__ = mocked_dataloaders # noqa: F811
def _UpperCamelCase (a__ :List[Any] , a__ :Optional[Any] ):
"""simple docstring"""
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , a__ ) == "1":
UpperCamelCase__ = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
UpperCamelCase__ = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="""all""" , project_dir=args.project_dir )
else:
UpperCamelCase__ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase__ = config["""lr"""]
UpperCamelCase__ = int(config["""num_epochs"""] )
UpperCamelCase__ = int(config["""seed"""] )
UpperCamelCase__ = int(config["""batch_size"""] )
set_seed(a__ )
UpperCamelCase__ , UpperCamelCase__ = get_dataloaders(a__ , a__ )
UpperCamelCase__ = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
UpperCamelCase__ = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
UpperCamelCase__ = batch_size // MAX_GPU_BATCH_SIZE
UpperCamelCase__ = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase__ = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=a__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase__ = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase__ = AdamW(params=model.parameters() , lr=a__ )
# Instantiate scheduler
UpperCamelCase__ = get_linear_schedule_with_warmup(
optimizer=a__ , num_warmup_steps=100 , num_training_steps=(len(a__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = accelerator.prepare(
a__ , a__ , a__ , a__ , a__ )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
UpperCamelCase__ = os.path.split(a__ )[-1].split(""".""" )[0]
accelerator.init_trackers(a__ , a__ )
# Now we train the model
for epoch in range(a__ ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
UpperCamelCase__ = 0
for step, batch in enumerate(a__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
UpperCamelCase__ = model(**a__ )
UpperCamelCase__ = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
UpperCamelCase__ = loss / gradient_accumulation_steps
accelerator.backward(a__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(a__ ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase__ = model(**a__ )
UpperCamelCase__ = outputs.logits.argmax(dim=-1 )
UpperCamelCase__ , UpperCamelCase__ = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=a__ , references=a__ , )
UpperCamelCase__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , a__ )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
"""accuracy""": eval_metric["""accuracy"""],
"""f1""": eval_metric["""f1"""],
"""train_loss""": total_loss.item() / len(a__ ),
"""epoch""": epoch,
} , step=a__ , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def _UpperCamelCase ():
"""simple docstring"""
UpperCamelCase__ = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=a__ , default=a__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
parser.add_argument(
"""--with_tracking""" , action="""store_true""" , help="""Whether to load in all available experiment trackers from the environment and use them for logging.""" , )
parser.add_argument(
"""--project_dir""" , type=a__ , default="""logs""" , help="""Location on where to store experiment tracking logs` and relevent project information""" , )
UpperCamelCase__ = parser.parse_args()
UpperCamelCase__ = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(a__ , a__ )
if __name__ == "__main__":
main()
| 87
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
UpperCamelCase__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : List[Any] = """upernet"""
def __init__( self , __lowerCAmelCase=None , __lowerCAmelCase=512 , __lowerCAmelCase=0.02 , __lowerCAmelCase=[1, 2, 3, 6] , __lowerCAmelCase=True , __lowerCAmelCase=0.4 , __lowerCAmelCase=384 , __lowerCAmelCase=256 , __lowerCAmelCase=1 , __lowerCAmelCase=False , __lowerCAmelCase=255 , **__lowerCAmelCase , ):
super().__init__(**__lowerCAmelCase )
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCamelCase__ = CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
UpperCamelCase__ = backbone_config.get("""model_type""" )
UpperCamelCase__ = CONFIG_MAPPING[backbone_model_type]
UpperCamelCase__ = config_class.from_dict(__lowerCAmelCase )
UpperCamelCase__ = backbone_config
UpperCamelCase__ = hidden_size
UpperCamelCase__ = initializer_range
UpperCamelCase__ = pool_scales
UpperCamelCase__ = use_auxiliary_head
UpperCamelCase__ = auxiliary_loss_weight
UpperCamelCase__ = auxiliary_in_channels
UpperCamelCase__ = auxiliary_channels
UpperCamelCase__ = auxiliary_num_convs
UpperCamelCase__ = auxiliary_concat_input
UpperCamelCase__ = loss_ignore_index
def _lowerCamelCase ( self ):
UpperCamelCase__ = copy.deepcopy(self.__dict__ )
UpperCamelCase__ = self.backbone_config.to_dict()
UpperCamelCase__ = self.__class__.model_type
return output
| 87
| 1
|
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
"""The RoBERTa Model transformer with early exiting (DeeRoBERTa). """ , UpperCAmelCase , )
class a__ ( UpperCAmelCase ):
"""simple docstring"""
UpperCAmelCase__ : Any =RobertaConfig
UpperCAmelCase__ : List[Any] ="""roberta"""
def __init__( self : Any , UpperCAmelCase__ : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
super().__init__(__lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = RobertaEmbeddings(__lowercase )
self.init_weights()
@add_start_docstrings(
"""RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
also takes care of multi-layer training. """ , UpperCAmelCase , )
class a__ ( UpperCAmelCase ):
"""simple docstring"""
UpperCAmelCase__ : Tuple =RobertaConfig
UpperCAmelCase__ : Tuple ="""roberta"""
def __init__( self : Tuple , UpperCAmelCase__ : Dict ) ->Dict:
"""simple docstring"""
super().__init__(__lowercase )
SCREAMING_SNAKE_CASE : List[str] = config.num_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = config.num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = DeeRobertaModel(__lowercase )
SCREAMING_SNAKE_CASE : int = nn.Dropout(config.hidden_dropout_prob )
SCREAMING_SNAKE_CASE : Dict = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(__lowercase )
def _lowercase ( self : str , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Tuple=None , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Optional[Any]=None , UpperCAmelCase__ : Optional[Any]=None , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Optional[int]=-1 , UpperCAmelCase__ : str=False , ) ->str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.num_layers
try:
SCREAMING_SNAKE_CASE : List[str] = self.roberta(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , position_ids=__lowercase , head_mask=__lowercase , inputs_embeds=__lowercase , )
SCREAMING_SNAKE_CASE : Optional[Any] = outputs[1]
SCREAMING_SNAKE_CASE : Optional[int] = self.dropout(__lowercase )
SCREAMING_SNAKE_CASE : Tuple = self.classifier(__lowercase )
SCREAMING_SNAKE_CASE : Any = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
SCREAMING_SNAKE_CASE : Union[str, Any] = e.message
SCREAMING_SNAKE_CASE : Any = e.exit_layer
SCREAMING_SNAKE_CASE : List[Any] = outputs[0]
if not self.training:
SCREAMING_SNAKE_CASE : Union[str, Any] = entropy(__lowercase )
SCREAMING_SNAKE_CASE : Dict = []
SCREAMING_SNAKE_CASE : str = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
SCREAMING_SNAKE_CASE : Optional[int] = MSELoss()
SCREAMING_SNAKE_CASE : str = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
SCREAMING_SNAKE_CASE : List[Any] = CrossEntropyLoss()
SCREAMING_SNAKE_CASE : List[str] = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
SCREAMING_SNAKE_CASE : Any = []
for highway_exit in outputs[-1]:
SCREAMING_SNAKE_CASE : Optional[Any] = highway_exit[0]
if not self.training:
highway_logits_all.append(__lowercase )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
SCREAMING_SNAKE_CASE : List[str] = MSELoss()
SCREAMING_SNAKE_CASE : int = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
SCREAMING_SNAKE_CASE : Dict = CrossEntropyLoss()
SCREAMING_SNAKE_CASE : Optional[int] = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(__lowercase )
if train_highway:
SCREAMING_SNAKE_CASE : str = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
SCREAMING_SNAKE_CASE : List[str] = (loss,) + outputs
if not self.training:
SCREAMING_SNAKE_CASE : Tuple = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
SCREAMING_SNAKE_CASE : str = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 245
|
'''simple docstring'''
from PIL import Image
def _a( UpperCamelCase__ : Image, UpperCamelCase__ : float ):
'''simple docstring'''
def brightness(UpperCamelCase__ : int ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''' )
return img.point(UpperCamelCase__ )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
a_ = change_brightness(img, 1_0_0)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 152
| 0
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = ["sentencepiece"]
def __init__( self : Tuple , *snake_case : Optional[Any] , **snake_case : Optional[int] ) -> Dict:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = ["sentencepiece"]
def __init__( self : Tuple , *snake_case : str , **snake_case : Optional[Any] ) -> Any:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = ["sentencepiece"]
def __init__( self : str , *snake_case : Optional[Any] , **snake_case : Optional[int] ) -> Tuple:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = ["sentencepiece"]
def __init__( self : Dict , *snake_case : Optional[int] , **snake_case : str ) -> Dict:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ["sentencepiece"]
def __init__( self : Optional[Any] , *snake_case : int , **snake_case : Any ) -> Tuple:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = ["sentencepiece"]
def __init__( self : List[Any] , *snake_case : Dict , **snake_case : Tuple ) -> Optional[Any]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ["sentencepiece"]
def __init__( self : Dict , *snake_case : Any , **snake_case : Any ) -> List[str]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = ["sentencepiece"]
def __init__( self : int , *snake_case : Tuple , **snake_case : Union[str, Any] ) -> str:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = ["sentencepiece"]
def __init__( self : List[str] , *snake_case : Dict , **snake_case : Tuple ) -> str:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ["sentencepiece"]
def __init__( self : Union[str, Any] , *snake_case : Tuple , **snake_case : Optional[Any] ) -> List[Any]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ["sentencepiece"]
def __init__( self : Optional[int] , *snake_case : Optional[Any] , **snake_case : Optional[int] ) -> str:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ["sentencepiece"]
def __init__( self : Dict , *snake_case : Optional[int] , **snake_case : Any ) -> Optional[Any]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = ["sentencepiece"]
def __init__( self : int , *snake_case : Any , **snake_case : List[Any] ) -> List[Any]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = ["sentencepiece"]
def __init__( self : Tuple , *snake_case : str , **snake_case : Tuple ) -> List[Any]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = ["sentencepiece"]
def __init__( self : Dict , *snake_case : List[Any] , **snake_case : int ) -> Union[str, Any]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = ["sentencepiece"]
def __init__( self : Optional[int] , *snake_case : List[Any] , **snake_case : List[Any] ) -> List[Any]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ["sentencepiece"]
def __init__( self : Any , *snake_case : str , **snake_case : List[str] ) -> Union[str, Any]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ["sentencepiece"]
def __init__( self : Union[str, Any] , *snake_case : Any , **snake_case : Any ) -> List[str]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ["sentencepiece"]
def __init__( self : Dict , *snake_case : Any , **snake_case : Dict ) -> Tuple:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ["sentencepiece"]
def __init__( self : Dict , *snake_case : Any , **snake_case : Optional[Any] ) -> Tuple:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = ["sentencepiece"]
def __init__( self : Dict , *snake_case : List[Any] , **snake_case : List[str] ) -> Any:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = ["sentencepiece"]
def __init__( self : List[str] , *snake_case : int , **snake_case : Optional[int] ) -> Union[str, Any]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = ["sentencepiece"]
def __init__( self : Optional[Any] , *snake_case : Tuple , **snake_case : Optional[int] ) -> Any:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ["sentencepiece"]
def __init__( self : Tuple , *snake_case : List[Any] , **snake_case : Union[str, Any] ) -> int:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = ["sentencepiece"]
def __init__( self : Tuple , *snake_case : Tuple , **snake_case : Any ) -> int:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = ["sentencepiece"]
def __init__( self : Optional[int] , *snake_case : Optional[Any] , **snake_case : List[Any] ) -> Tuple:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ["sentencepiece"]
def __init__( self : Any , *snake_case : Any , **snake_case : Any ) -> Optional[Any]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = ["sentencepiece"]
def __init__( self : int , *snake_case : List[str] , **snake_case : Dict ) -> int:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ["sentencepiece"]
def __init__( self : List[str] , *snake_case : int , **snake_case : int ) -> str:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ["sentencepiece"]
def __init__( self : Dict , *snake_case : Tuple , **snake_case : str ) -> List[Any]:
requires_backends(self , ['''sentencepiece'''] )
class a ( metaclass=_a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = ["sentencepiece"]
def __init__( self : Tuple , *snake_case : Union[str, Any] , **snake_case : Optional[int] ) -> Any:
requires_backends(self , ['''sentencepiece'''] )
| 240
|
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase :Tuple = "Muhammad Umer Farooq"
__UpperCAmelCase :Tuple = "MIT"
__UpperCAmelCase :Union[str, Any] = "1.0.0"
__UpperCAmelCase :Optional[int] = "Muhammad Umer Farooq"
__UpperCAmelCase :Optional[Any] = "contact@muhammadumerfarooq.me"
__UpperCAmelCase :Any = "Alpha"
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class a ( _a ):
"""simple docstring"""
def __init__( self : Tuple , snake_case : str ) -> None:
super().__init__()
__UpperCAmelCase : list[str] = []
__UpperCAmelCase : Optional[int] = domain
def lowerCamelCase__ ( self : Union[str, Any] , snake_case : str , snake_case : list[tuple[str, str | None]] ) -> None:
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__UpperCAmelCase : Optional[Any] = parse.urljoin(self.domain , snake_case )
self.urls.append(snake_case )
def _a ( _lowercase : str ):
'''simple docstring'''
return ".".join(get_sub_domain_name(_lowercase ).split('''.''' )[-2:] )
def _a ( _lowercase : str ):
'''simple docstring'''
return parse.urlparse(_lowercase ).netloc
def _a ( _lowercase : str = "https://github.com" ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = get_domain_name(_lowercase )
# Initialize the parser
__UpperCAmelCase : Dict = Parser(_lowercase )
try:
# Open URL
__UpperCAmelCase : Dict = requests.get(_lowercase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__UpperCAmelCase : str = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__UpperCAmelCase : Tuple = requests.get(_lowercase )
# Get the valid email.
__UpperCAmelCase : Dict = re.findall('''[a-zA-Z0-9]+@''' + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_lowercase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_lowercase )
if __name__ == "__main__":
__UpperCAmelCase :List[str] = emails_from_url("https://github.com")
print(f"""{len(emails)} emails found:""")
print("\n".join(sorted(emails)))
| 240
| 1
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = DPTConfig()
if "large" in checkpoint_url:
snake_case_ = 1024
snake_case_ = 4096
snake_case_ = 24
snake_case_ = 16
snake_case_ = [5, 11, 17, 23]
snake_case_ = [256, 512, 1024, 1024]
snake_case_ = (1, 384, 384)
if "ade" in checkpoint_url:
snake_case_ = True
snake_case_ = 150
snake_case_ = '''huggingface/label-files'''
snake_case_ = '''ade20k-id2label.json'''
snake_case_ = json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type='''dataset''' ) ) , '''r''' ) )
snake_case_ = {int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
snake_case_ = [1, 150, 480, 480]
return config, expected_shape
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
snake_case_ = name.replace('''pretrained.model''' , '''dpt.encoder''' )
if "pretrained.model" in name:
snake_case_ = name.replace('''pretrained.model''' , '''dpt.embeddings''' )
if "patch_embed" in name:
snake_case_ = name.replace('''patch_embed''' , '''patch_embeddings''' )
if "pos_embed" in name:
snake_case_ = name.replace('''pos_embed''' , '''position_embeddings''' )
if "attn.proj" in name:
snake_case_ = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "proj" in name and "project" not in name:
snake_case_ = name.replace('''proj''' , '''projection''' )
if "blocks" in name:
snake_case_ = name.replace('''blocks''' , '''layer''' )
if "mlp.fc1" in name:
snake_case_ = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
snake_case_ = name.replace('''mlp.fc2''' , '''output.dense''' )
if "norm1" in name:
snake_case_ = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
snake_case_ = name.replace('''norm2''' , '''layernorm_after''' )
if "scratch.output_conv" in name:
snake_case_ = name.replace('''scratch.output_conv''' , '''head''' )
if "scratch" in name:
snake_case_ = name.replace('''scratch''' , '''neck''' )
if "layer1_rn" in name:
snake_case_ = name.replace('''layer1_rn''' , '''convs.0''' )
if "layer2_rn" in name:
snake_case_ = name.replace('''layer2_rn''' , '''convs.1''' )
if "layer3_rn" in name:
snake_case_ = name.replace('''layer3_rn''' , '''convs.2''' )
if "layer4_rn" in name:
snake_case_ = name.replace('''layer4_rn''' , '''convs.3''' )
if "refinenet" in name:
snake_case_ = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
snake_case_ = name.replace(F'''refinenet{layer_idx}''' , F'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
if "out_conv" in name:
snake_case_ = name.replace('''out_conv''' , '''projection''' )
if "resConfUnit1" in name:
snake_case_ = name.replace('''resConfUnit1''' , '''residual_layer1''' )
if "resConfUnit2" in name:
snake_case_ = name.replace('''resConfUnit2''' , '''residual_layer2''' )
if "conv1" in name:
snake_case_ = name.replace('''conv1''' , '''convolution1''' )
if "conv2" in name:
snake_case_ = name.replace('''conv2''' , '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
snake_case_ = name.replace('''pretrained''' , '''dpt''' )
if "bn" in name:
snake_case_ = name.replace('''bn''' , '''batch_norm''' )
if "head" in name:
snake_case_ = name.replace('''head''' , '''head.head''' )
if "encoder.norm" in name:
snake_case_ = name.replace('''encoder.norm''' , '''layernorm''' )
if "auxlayer" in name:
snake_case_ = name.replace('''auxlayer''' , '''auxiliary_head.head''' )
return name
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
snake_case_ = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case_ = in_proj_weight[: config.hidden_size, :]
snake_case_ = in_proj_bias[: config.hidden_size]
snake_case_ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case_ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case_ = in_proj_weight[
-config.hidden_size :, :
]
snake_case_ = in_proj_bias[-config.hidden_size :]
def __SCREAMING_SNAKE_CASE ():
snake_case_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case_ = Image.open(requests.get(SCREAMING_SNAKE_CASE__ , stream=SCREAMING_SNAKE_CASE__ ).raw )
return im
@torch.no_grad()
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_, snake_case_ = get_dpt_config(SCREAMING_SNAKE_CASE__ )
# load original state_dict from URL
snake_case_ = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__ , map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(SCREAMING_SNAKE_CASE__ )
# rename keys
for key in state_dict.copy().keys():
snake_case_ = state_dict.pop(SCREAMING_SNAKE_CASE__ )
snake_case_ = val
# read in qkv matrices
read_in_q_k_v(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# load HuggingFace model
snake_case_ = DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE__ ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE__ )
model.load_state_dict(SCREAMING_SNAKE_CASE__ )
model.eval()
# Check outputs on an image
snake_case_ = 480 if '''ade''' in checkpoint_url else 384
snake_case_ = DPTImageProcessor(size=SCREAMING_SNAKE_CASE__ )
snake_case_ = prepare_img()
snake_case_ = image_processor(SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' )
# forward pass
snake_case_ = model(**SCREAMING_SNAKE_CASE__ ).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE__ ).predicted_depth
# Assert logits
snake_case_ = torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
if "ade" in checkpoint_url:
snake_case_ = torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
assert outputs.shape == torch.Size(SCREAMING_SNAKE_CASE__ )
assert (
torch.allclose(outputs[0, 0, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1E-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] , SCREAMING_SNAKE_CASE__ )
)
Path(SCREAMING_SNAKE_CASE__ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE__ )
if push_to_hub:
print('''Pushing model to hub...''' )
model.push_to_hub(
repo_path_or_name=Path(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=SCREAMING_SNAKE_CASE__ , )
image_processor.push_to_hub(
repo_path_or_name=Path(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=SCREAMING_SNAKE_CASE__ , )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
type=str,
help='''URL of the original DPT checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
)
parser.add_argument(
'''--model_name''',
default='''dpt-large''',
type=str,
help='''Name of the model, in case you\'re pushing to the hub.''',
)
lowerCAmelCase_ = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 8
|
import csv
import tweepy
# Twitter API credentials
a =""""""
a =""""""
a =""""""
a =""""""
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> None:
# authorize twitter, initialize tweepy
__lowerCamelCase : Tuple = tweepy.OAuthHandler(lowerCamelCase__ , lowerCamelCase__ )
auth.set_access_token(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : Optional[int] = tweepy.API(lowerCamelCase__ )
# initialize a list to hold all the tweepy Tweets
__lowerCamelCase : str = []
# make initial request for most recent tweets (200 is the maximum allowed count)
__lowerCamelCase : Union[str, Any] = api.user_timeline(screen_name=lowerCamelCase__ , count=2_0_0 )
# save most recent tweets
alltweets.extend(lowerCamelCase__ )
# save the id of the oldest tweet less one
__lowerCamelCase : Any = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(lowerCamelCase__ ) > 0:
print(F"getting tweets before {oldest}" )
# all subsequent requests use the max_id param to prevent duplicates
__lowerCamelCase : str = api.user_timeline(
screen_name=lowerCamelCase__ , count=2_0_0 , max_id=lowerCamelCase__ )
# save most recent tweets
alltweets.extend(lowerCamelCase__ )
# update the id of the oldest tweet less one
__lowerCamelCase : Optional[int] = alltweets[-1].id - 1
print(F"...{len(lowerCamelCase__ )} tweets downloaded so far" )
# transform the tweepy tweets into a 2D array that will populate the csv
__lowerCamelCase : str = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F"new_{screen_name}_tweets.csv" , 'w' ) as f:
__lowerCamelCase : Any = csv.writer(lowerCamelCase__ )
writer.writerow(['id', 'created_at', 'text'] )
writer.writerows(lowerCamelCase__ )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets("""FirePing32""")
| 73
| 0
|
from math import factorial
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase , __lowercase ) -> List[str]:
"""simple docstring"""
a__ : str = real
if isinstance(__lowercase , __lowercase ):
a__ : Optional[int] = [1] * rank
else:
a__ : Dict = rank
def __repr__( self ) -> Tuple:
"""simple docstring"""
return (
F'''{self.real}+'''
F'''{'+'.join(str(__lowercase )+'E'+str(n+1 )for n,dual in enumerate(self.duals ) )}'''
)
def SCREAMING_SNAKE_CASE__( self ) -> int:
"""simple docstring"""
a__ : int = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1 )
return Dual(self.real , __lowercase )
def __add__( self , __lowercase ) -> str:
"""simple docstring"""
if not isinstance(__lowercase , __lowercase ):
return Dual(self.real + other , self.duals )
a__ : Any = self.duals.copy()
a__ : str = other.duals.copy()
if len(__lowercase ) > len(__lowercase ):
o_dual.extend([1] * (len(__lowercase ) - len(__lowercase )) )
elif len(__lowercase ) < len(__lowercase ):
s_dual.extend([1] * (len(__lowercase ) - len(__lowercase )) )
a__ : Any = []
for i in range(len(__lowercase ) ):
new_duals.append(s_dual[i] + o_dual[i] )
return Dual(self.real + other.real , __lowercase )
__lowerCAmelCase :Union[str, Any] = __add__
def __sub__( self , __lowercase ) -> Dict:
"""simple docstring"""
return self + other * -1
def __mul__( self , __lowercase ) -> str:
"""simple docstring"""
if not isinstance(__lowercase , __lowercase ):
a__ : Union[str, Any] = []
for i in self.duals:
new_duals.append(i * other )
return Dual(self.real * other , __lowercase )
a__ : List[Any] = [0] * (len(self.duals ) + len(other.duals ) + 1)
for i, item in enumerate(self.duals ):
for j, jtem in enumerate(other.duals ):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals ) ):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals ) ):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real , __lowercase )
__lowerCAmelCase :Union[str, Any] = __mul__
def __truediv__( self , __lowercase ) -> int:
"""simple docstring"""
if not isinstance(__lowercase , __lowercase ):
a__ : List[Any] = []
for i in self.duals:
new_duals.append(i / other )
return Dual(self.real / other , __lowercase )
raise ValueError
def __floordiv__( self , __lowercase ) -> Optional[int]:
"""simple docstring"""
if not isinstance(__lowercase , __lowercase ):
a__ : int = []
for i in self.duals:
new_duals.append(i // other )
return Dual(self.real // other , __lowercase )
raise ValueError
def __pow__( self , __lowercase ) -> int:
"""simple docstring"""
if n < 0 or isinstance(__lowercase , __lowercase ):
raise ValueError("""power must be a positive integer""" )
if n == 0:
return 1
if n == 1:
return self
a__ : int = self
for _ in range(n - 1 ):
x *= self
return x
def lowerCAmelCase_ ( _lowercase : int , _lowercase : Dict , _lowercase : Optional[int]) -> Any:
"""simple docstring"""
if not callable(_lowercase):
raise ValueError("""differentiate() requires a function as input for func""")
if not isinstance(_lowercase , (float, int)):
raise ValueError("""differentiate() requires a float as input for position""")
if not isinstance(_lowercase , _lowercase):
raise ValueError("""differentiate() requires an int as input for order""")
a__ : List[Any] = Dual(_lowercase , 1)
a__ : Dict = func(_lowercase)
if order == 0:
return result.real
return result.duals[order - 1] * factorial(_lowercase)
if __name__ == "__main__":
import doctest
doctest.testmod()
def lowerCAmelCase_ ( _lowercase : str) -> int:
"""simple docstring"""
return y**2 * y**4
print(differentiate(f, 9, 2))
| 354
|
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
_lowercase : int =(
"https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
)
_lowercase : Dict =logging.get_logger(__name__) # pylint: disable=invalid-name
def lowerCAmelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
a__ : List[Any] = """https://pypi.org/pypi/diffusers/json"""
a__ : Tuple = json.loads(request.urlopen(_lowercase).read())["""releases"""].keys()
return sorted(_lowercase , key=lambda _lowercase: version.Version(_lowercase))
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(_lowercase)
os.makedirs(_lowercase , exist_ok=_lowercase)
a__ : Tuple = Path(_lowercase) / """__init__.py"""
if not init_path.exists():
init_path.touch()
def lowerCAmelCase_ ( _lowercase : Union[str, os.PathLike]) -> Optional[Any]:
"""simple docstring"""
init_hf_modules()
a__ : Optional[int] = Path(_lowercase) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent)
os.makedirs(_lowercase , exist_ok=_lowercase)
a__ : Any = dynamic_module_path / """__init__.py"""
if not init_path.exists():
init_path.touch()
def lowerCAmelCase_ ( _lowercase : Union[str, Any]) -> Optional[int]:
"""simple docstring"""
with open(_lowercase , """r""" , encoding="""utf-8""") as f:
a__ : Union[str, Any] = f.read()
# Imports of the form `import .xxx`
a__ : Optional[Any] = re.findall("""^\s*import\s+\.(\S+)\s*$""" , _lowercase , flags=re.MULTILINE)
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall("""^\s*from\s+\.(\S+)\s+import""" , _lowercase , flags=re.MULTILINE)
# Unique-ify
return list(set(_lowercase))
def lowerCAmelCase_ ( _lowercase : List[Any]) -> Dict:
"""simple docstring"""
a__ : Dict = False
a__ : str = [module_file]
a__ : List[Any] = []
# Let's recurse through all relative imports
while not no_change:
a__ : Optional[Any] = []
for f in files_to_check:
new_imports.extend(get_relative_imports(_lowercase))
a__ : Dict = Path(_lowercase).parent
a__ : Any = [str(module_path / m) for m in new_imports]
a__ : Any = [f for f in new_import_files if f not in all_relative_imports]
a__ : List[Any] = [F'''{f}.py''' for f in new_import_files]
a__ : List[Any] = len(_lowercase) == 0
all_relative_imports.extend(_lowercase)
return all_relative_imports
def lowerCAmelCase_ ( _lowercase : Optional[int]) -> Any:
"""simple docstring"""
with open(_lowercase , """r""" , encoding="""utf-8""") as f:
a__ : Optional[Any] = f.read()
# Imports of the form `import xxx`
a__ : Optional[int] = re.findall("""^\s*import\s+(\S+)\s*$""" , _lowercase , flags=re.MULTILINE)
# Imports of the form `from xxx import yyy`
imports += re.findall("""^\s*from\s+(\S+)\s+import""" , _lowercase , flags=re.MULTILINE)
# Only keep the top-level module
a__ : Any = [imp.split(""".""")[0] for imp in imports if not imp.startswith(""".""")]
# Unique-ify and test we got them all
a__ : Optional[Any] = list(set(_lowercase))
a__ : Union[str, Any] = []
for imp in imports:
try:
importlib.import_module(_lowercase)
except ImportError:
missing_packages.append(_lowercase)
if len(_lowercase) > 0:
raise ImportError(
"""This modeling file requires the following packages that were not found in your environment: """
F'''{', '.join(_lowercase)}. Run `pip install {' '.join(_lowercase)}`''')
return get_relative_imports(_lowercase)
def lowerCAmelCase_ ( _lowercase : int , _lowercase : Tuple) -> Union[str, Any]:
"""simple docstring"""
a__ : Dict = module_path.replace(os.path.sep , """.""")
a__ : List[Any] = importlib.import_module(_lowercase)
if class_name is None:
return find_pipeline_class(_lowercase)
return getattr(_lowercase , _lowercase)
def lowerCAmelCase_ ( _lowercase : Union[str, Any]) -> Optional[int]:
"""simple docstring"""
from ..pipelines import DiffusionPipeline
a__ : Any = dict(inspect.getmembers(_lowercase , inspect.isclass))
a__ : str = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , _lowercase)
and cls.__module__.split(""".""")[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
F''' {loaded_module}.''')
a__ : str = cls
return pipeline_class
def lowerCAmelCase_ ( _lowercase : Union[str, os.PathLike] , _lowercase : str , _lowercase : Optional[Union[str, os.PathLike]] = None , _lowercase : bool = False , _lowercase : bool = False , _lowercase : Optional[Dict[str, str]] = None , _lowercase : Optional[Union[bool, str]] = None , _lowercase : Optional[str] = None , _lowercase : bool = False , ) -> Optional[int]:
"""simple docstring"""
a__ : Union[str, Any] = str(_lowercase)
a__ : Union[str, Any] = os.path.join(_lowercase , _lowercase)
if os.path.isfile(_lowercase):
a__ : Dict = module_file_or_url
a__ : List[str] = """local"""
elif pretrained_model_name_or_path.count("""/""") == 0:
a__ : Optional[Any] = get_diffusers_versions()
# cut ".dev0"
a__ : Union[str, Any] = """v""" + """.""".join(__version__.split(""".""")[:3])
# retrieve github version that matches
if revision is None:
a__ : List[str] = latest_version if latest_version[1:] in available_versions else """main"""
logger.info(F'''Defaulting to latest_version: {revision}.''')
elif revision in available_versions:
a__ : str = F'''v{revision}'''
elif revision == "main":
a__ : List[str] = revision
else:
raise ValueError(
F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
F''' {', '.join(available_versions + ['main'])}.''')
# community pipeline on GitHub
a__ : Any = COMMUNITY_PIPELINES_URL.format(revision=_lowercase , pipeline=_lowercase)
try:
a__ : Optional[int] = cached_download(
_lowercase , cache_dir=_lowercase , force_download=_lowercase , proxies=_lowercase , resume_download=_lowercase , local_files_only=_lowercase , use_auth_token=_lowercase , )
a__ : Any = """git"""
a__ : int = pretrained_model_name_or_path + """.py"""
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''')
raise
else:
try:
# Load from URL or cache if already cached
a__ : Any = hf_hub_download(
_lowercase , _lowercase , cache_dir=_lowercase , force_download=_lowercase , proxies=_lowercase , resume_download=_lowercase , local_files_only=_lowercase , use_auth_token=_lowercase , )
a__ : Optional[Any] = os.path.join("""local""" , """--""".join(pretrained_model_name_or_path.split("""/""")))
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''')
raise
# Check we have all the requirements in our environment
a__ : List[str] = check_imports(_lowercase)
# Now we move the module inside our cached dynamic modules.
a__ : int = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(_lowercase)
a__ : Tuple = Path(_lowercase) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(_lowercase , submodule_path / module_file)
for module_needed in modules_needed:
a__ : Dict = F'''{module_needed}.py'''
shutil.copy(os.path.join(_lowercase , _lowercase) , submodule_path / module_needed)
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(_lowercase , _lowercase):
a__ : Optional[Any] = use_auth_token
elif use_auth_token is True:
a__ : Dict = HfFolder.get_token()
else:
a__ : str = None
a__ : Any = model_info(_lowercase , revision=_lowercase , token=_lowercase).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
a__ : Optional[int] = submodule_path / commit_hash
a__ : Tuple = full_submodule + os.path.sep + commit_hash
create_dynamic_module(_lowercase)
if not (submodule_path / module_file).exists():
shutil.copy(_lowercase , submodule_path / module_file)
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
_lowercase , F'''{module_needed}.py''' , cache_dir=_lowercase , force_download=_lowercase , resume_download=_lowercase , proxies=_lowercase , use_auth_token=_lowercase , revision=_lowercase , local_files_only=_lowercase , )
return os.path.join(_lowercase , _lowercase)
def lowerCAmelCase_ ( _lowercase : Union[str, os.PathLike] , _lowercase : str , _lowercase : Optional[str] = None , _lowercase : Optional[Union[str, os.PathLike]] = None , _lowercase : bool = False , _lowercase : bool = False , _lowercase : Optional[Dict[str, str]] = None , _lowercase : Optional[Union[bool, str]] = None , _lowercase : Optional[str] = None , _lowercase : bool = False , **_lowercase : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
a__ : int = get_cached_module_file(
_lowercase , _lowercase , cache_dir=_lowercase , force_download=_lowercase , resume_download=_lowercase , proxies=_lowercase , use_auth_token=_lowercase , revision=_lowercase , local_files_only=_lowercase , )
return get_class_in_module(_lowercase , final_module.replace(""".py""" , """"""))
| 266
| 0
|
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Any ):
UpperCAmelCase__ = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) ,1 )
self.assertEqual(x.component(2 ) ,3 )
UpperCAmelCase__ = Vector()
def __lowerCAmelCase ( self : Dict ):
UpperCAmelCase__ = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(lowerCamelCase__ ) ,'(0,0,0,0,0,1)' )
def __lowerCAmelCase ( self : Optional[int] ):
UpperCAmelCase__ = Vector([1, 2, 3, 4] )
self.assertEqual(len(lowerCamelCase__ ) ,4 )
def __lowerCAmelCase ( self : Dict ):
UpperCAmelCase__ = Vector([1, 2] )
UpperCAmelCase__ = Vector([1, 2, 3, 4, 5] )
UpperCAmelCase__ = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
UpperCAmelCase__ = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() ,2.2_3_6 ,3 )
self.assertAlmostEqual(y.euclidean_length() ,7.4_1_6 ,3 )
self.assertEqual(z.euclidean_length() ,0 )
self.assertAlmostEqual(w.euclidean_length() ,7.6_1_6 ,3 )
def __lowerCAmelCase ( self : Any ):
UpperCAmelCase__ = Vector([1, 2, 3] )
UpperCAmelCase__ = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) ,2 )
self.assertEqual((x + y).component(1 ) ,3 )
self.assertEqual((x + y).component(2 ) ,4 )
def __lowerCAmelCase ( self : Union[str, Any] ):
UpperCAmelCase__ = Vector([1, 2, 3] )
UpperCAmelCase__ = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) ,0 )
self.assertEqual((x - y).component(1 ) ,1 )
self.assertEqual((x - y).component(2 ) ,2 )
def __lowerCAmelCase ( self : int ):
UpperCAmelCase__ = Vector([1, 2, 3] )
UpperCAmelCase__ = Vector([2, -1, 4] ) # for test of dot product
UpperCAmelCase__ = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) ,'(3.0,6.0,9.0)' )
self.assertEqual((a * b) ,0 )
def __lowerCAmelCase ( self : str ):
self.assertEqual(str(zero_vector(10 ) ).count('0' ) ,10 )
def __lowerCAmelCase ( self : str ):
self.assertEqual(str(unit_basis_vector(3 ,1 ) ) ,'(0,1,0)' )
def __lowerCAmelCase ( self : List[str] ):
UpperCAmelCase__ = Vector([1, 2, 3] )
UpperCAmelCase__ = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 ,lowerCamelCase__ ,lowerCamelCase__ ) ) ,'(3,4,7)' )
def __lowerCAmelCase ( self : Tuple ):
UpperCAmelCase__ = Vector([1, 0, 0, 0, 0, 0] )
UpperCAmelCase__ = x.copy()
self.assertEqual(str(lowerCamelCase__ ) ,str(lowerCamelCase__ ) )
def __lowerCAmelCase ( self : str ):
UpperCAmelCase__ = Vector([1, 0, 0] )
x.change_component(0 ,0 )
x.change_component(1 ,1 )
self.assertEqual(str(lowerCamelCase__ ) ,'(0,1,0)' )
def __lowerCAmelCase ( self : int ):
UpperCAmelCase__ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] ,3 ,3 )
self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n' ,str(lowerCamelCase__ ) )
def __lowerCAmelCase ( self : Dict ):
UpperCAmelCase__ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] ,3 ,3 )
UpperCAmelCase__ = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] ,a.minor(lowerCamelCase__ ,lowerCamelCase__ ) )
def __lowerCAmelCase ( self : Any ):
UpperCAmelCase__ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] ,3 ,3 )
UpperCAmelCase__ = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] ,a.cofactor(lowerCamelCase__ ,lowerCamelCase__ ) )
def __lowerCAmelCase ( self : List[Any] ):
UpperCAmelCase__ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] ,3 ,3 )
self.assertEqual(-5 ,a.determinant() )
def __lowerCAmelCase ( self : Dict ):
UpperCAmelCase__ = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] ,3 ,3 )
UpperCAmelCase__ = Vector([1, 2, 3] )
self.assertEqual('(14,32,50)' ,str(a * x ) )
self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n' ,str(a * 2 ) )
def __lowerCAmelCase ( self : Any ):
UpperCAmelCase__ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] ,3 ,3 )
a.change_component(0 ,2 ,5 )
self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n' ,str(lowerCamelCase__ ) )
def __lowerCAmelCase ( self : int ):
UpperCAmelCase__ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] ,3 ,3 )
self.assertEqual(7 ,a.component(2 ,1 ) ,0.0_1 )
def __lowerCAmelCase ( self : Optional[Any] ):
UpperCAmelCase__ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] ,3 ,3 )
UpperCAmelCase__ = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] ,3 ,3 )
self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n' ,str(a + b ) )
def __lowerCAmelCase ( self : Tuple ):
UpperCAmelCase__ = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] ,3 ,3 )
UpperCAmelCase__ = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] ,3 ,3 )
self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n' ,str(a - b ) )
def __lowerCAmelCase ( self : Optional[int] ):
self.assertEqual(
'|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n' ,str(square_zero_matrix(5 ) ) ,)
if __name__ == "__main__":
unittest.main()
| 98
|
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
UpperCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase_ = 256
class lowercase__ ( __lowerCamelCase ):
'''simple docstring'''
a : List[str] = ["melgan"]
def __init__( self, __magic_name__, __magic_name__, __magic_name__, __magic_name__, __magic_name__, ) -> None:
"""simple docstring"""
super().__init__()
# From MELGAN
UpperCamelCase__ : Optional[int] = math.log(1E-5 ) # Matches MelGAN training.
UpperCamelCase__ : int = 4.0 # Largest value for most examples
UpperCamelCase__ : Optional[int] = 128
self.register_modules(
notes_encoder=__magic_name__, continuous_encoder=__magic_name__, decoder=__magic_name__, scheduler=__magic_name__, melgan=__magic_name__, )
def UpperCamelCase__ ( self, __magic_name__, __magic_name__=(-1.0, 1.0), __magic_name__=False ) -> Any:
"""simple docstring"""
UpperCamelCase__ ,UpperCamelCase__ : str = output_range
if clip:
UpperCamelCase__ : Union[str, Any] = torch.clip(__magic_name__, self.min_value, self.max_value )
# Scale to [0, 1].
UpperCamelCase__ : List[str] = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def UpperCamelCase__ ( self, __magic_name__, __magic_name__=(-1.0, 1.0), __magic_name__=False ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase__ ,UpperCamelCase__ : List[str] = input_range
UpperCamelCase__ : Any = torch.clip(__magic_name__, __magic_name__, __magic_name__ ) if clip else outputs
# Scale to [0, 1].
UpperCamelCase__ : Any = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__ ) -> List[str]:
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] = input_tokens > 0
UpperCamelCase__ ,UpperCamelCase__ : Any = self.notes_encoder(
encoder_input_tokens=__magic_name__, encoder_inputs_mask=__magic_name__ )
UpperCamelCase__ ,UpperCamelCase__ : Optional[Any] = self.continuous_encoder(
encoder_inputs=__magic_name__, encoder_inputs_mask=__magic_name__ )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__ ) -> str:
"""simple docstring"""
UpperCamelCase__ : Any = noise_time
if not torch.is_tensor(__magic_name__ ):
UpperCamelCase__ : Tuple = torch.tensor([timesteps], dtype=torch.long, device=input_tokens.device )
elif torch.is_tensor(__magic_name__ ) and len(timesteps.shape ) == 0:
UpperCamelCase__ : Union[str, Any] = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCamelCase__ : Dict = timesteps * torch.ones(input_tokens.shape[0], dtype=timesteps.dtype, device=timesteps.device )
UpperCamelCase__ : List[str] = self.decoder(
encodings_and_masks=__magic_name__, decoder_input_tokens=__magic_name__, decoder_noise_time=__magic_name__ )
return logits
@torch.no_grad()
def __call__( self, __magic_name__, __magic_name__ = None, __magic_name__ = 100, __magic_name__ = True, __magic_name__ = "numpy", __magic_name__ = None, __magic_name__ = 1, ) -> Union[AudioPipelineOutput, Tuple]:
"""simple docstring"""
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__magic_name__, __magic_name__ ) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(__magic_name__ )}." )
UpperCamelCase__ : Dict = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims], dtype=np.floataa )
UpperCamelCase__ : Tuple = np.zeros([1, 0, self.n_dims], np.floataa )
UpperCamelCase__ : List[Any] = torch.ones((1, TARGET_FEATURE_LENGTH), dtype=__magic_name__, device=self.device )
for i, encoder_input_tokens in enumerate(__magic_name__ ):
if i == 0:
UpperCamelCase__ : str = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device, dtype=self.decoder.dtype )
# The first chunk has no previous context.
UpperCamelCase__ : Any = torch.zeros((1, TARGET_FEATURE_LENGTH), dtype=__magic_name__, device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
UpperCamelCase__ : List[str] = ones
UpperCamelCase__ : int = self.scale_features(
__magic_name__, output_range=[-1.0, 1.0], clip=__magic_name__ )
UpperCamelCase__ : Union[str, Any] = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ), continuous_inputs=__magic_name__, continuous_mask=__magic_name__, )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
UpperCamelCase__ : Optional[int] = randn_tensor(
shape=encoder_continuous_inputs.shape, generator=__magic_name__, device=self.device, dtype=self.decoder.dtype, )
# set step values
self.scheduler.set_timesteps(__magic_name__ )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCamelCase__ : Union[str, Any] = self.decode(
encodings_and_masks=__magic_name__, input_tokens=__magic_name__, noise_time=t / self.scheduler.config.num_train_timesteps, )
# Compute previous output: x_t -> x_t-1
UpperCamelCase__ : List[Any] = self.scheduler.step(__magic_name__, __magic_name__, __magic_name__, generator=__magic_name__ ).prev_sample
UpperCamelCase__ : List[Any] = self.scale_to_features(__magic_name__, input_range=[-1.0, 1.0] )
UpperCamelCase__ : List[Any] = mel[:1]
UpperCamelCase__ : int = mel.cpu().float().numpy()
UpperCamelCase__ : Union[str, Any] = np.concatenate([full_pred_mel, pred_mel[:1]], axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__magic_name__, __magic_name__ )
logger.info('''Generated segment''', __magic_name__ )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
'''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''' )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
'''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''' )
if output_type == "numpy":
UpperCamelCase__ : Optional[int] = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
UpperCamelCase__ : Any = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=__magic_name__ )
| 201
| 0
|
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 154
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class UpperCamelCase__( unittest.TestCase ):
lowerCAmelCase__ : Dict = StableDiffusionLDMaDPipeline
lowerCAmelCase__ : List[Any] = TEXT_TO_IMAGE_PARAMS
lowerCAmelCase__ : Optional[int] = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCAmelCase__ : Tuple = TEXT_TO_IMAGE_IMAGE_PARAMS
def snake_case__ ( self ) -> str:
torch.manual_seed(0 )
A__ = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,cross_attention_dim=32 ,)
A__ = DDIMScheduler(
beta_start=0.0_0_0_8_5 ,beta_end=0.0_1_2 ,beta_schedule='scaled_linear' ,clip_sample=__UpperCAmelCase ,set_alpha_to_one=__UpperCAmelCase ,)
torch.manual_seed(0 )
A__ = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=6 ,out_channels=6 ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,latent_channels=4 ,)
torch.manual_seed(0 )
A__ = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1e-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=10_00 ,)
A__ = CLIPTextModel(__UpperCAmelCase )
A__ = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
A__ = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase=0 ) -> Dict:
if str(__UpperCAmelCase ).startswith('mps' ):
A__ = torch.manual_seed(__UpperCAmelCase )
else:
A__ = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
A__ = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def snake_case__ ( self ) -> str:
A__ = 'cpu' # ensure determinism for the device-dependent torch.Generator
A__ = self.get_dummy_components()
A__ = StableDiffusionLDMaDPipeline(**__UpperCAmelCase )
A__ = ldmad_pipe.to(__UpperCAmelCase )
ldmad_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
A__ = self.get_dummy_inputs(__UpperCAmelCase )
A__ = ldmad_pipe(**__UpperCAmelCase )
A__ , A__ = output.rgb, output.depth
A__ = rgb[0, -3:, -3:, -1]
A__ = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
A__ = np.array(
[0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2] )
A__ = np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
def snake_case__ ( self ) -> List[str]:
A__ = self.get_dummy_components()
A__ = StableDiffusionLDMaDPipeline(**__UpperCAmelCase )
A__ = ldmad_pipe.to(__UpperCAmelCase )
ldmad_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
A__ = self.get_dummy_inputs(__UpperCAmelCase )
A__ = 3 * [inputs['prompt']]
# forward
A__ = ldmad_pipe(**__UpperCAmelCase )
A__ , A__ = output.rgb, output.depth
A__ = rgb_slice_a[0, -3:, -3:, -1]
A__ = depth_slice_a[0, -3:, -1]
A__ = self.get_dummy_inputs(__UpperCAmelCase )
A__ = 3 * [inputs.pop('prompt' )]
A__ = ldmad_pipe.tokenizer(
__UpperCAmelCase ,padding='max_length' ,max_length=ldmad_pipe.tokenizer.model_max_length ,truncation=__UpperCAmelCase ,return_tensors='pt' ,)
A__ = text_inputs['input_ids'].to(__UpperCAmelCase )
A__ = ldmad_pipe.text_encoder(__UpperCAmelCase )[0]
A__ = prompt_embeds
# forward
A__ = ldmad_pipe(**__UpperCAmelCase )
A__ , A__ = output.rgb, output.depth
A__ = rgb_slice_a[0, -3:, -3:, -1]
A__ = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4
def snake_case__ ( self ) -> int:
A__ = 'cpu' # ensure determinism for the device-dependent torch.Generator
A__ = self.get_dummy_components()
A__ = PNDMScheduler(skip_prk_steps=__UpperCAmelCase )
A__ = StableDiffusionLDMaDPipeline(**__UpperCAmelCase )
A__ = ldmad_pipe.to(__UpperCAmelCase )
ldmad_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
A__ = self.get_dummy_inputs(__UpperCAmelCase )
A__ = 'french fries'
A__ = ldmad_pipe(**__UpperCAmelCase ,negative_prompt=__UpperCAmelCase )
A__ , A__ = output.rgb, output.depth
A__ = rgb[0, -3:, -3:, -1]
A__ = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
A__ = np.array(
[0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7] )
A__ = np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2
@slow
@require_torch_gpu
class UpperCamelCase__( unittest.TestCase ):
def snake_case__ ( self ) -> List[str]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase="cpu" ,__UpperCAmelCase=torch.floataa ,__UpperCAmelCase=0 ) -> Optional[int]:
A__ = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
A__ = np.random.RandomState(__UpperCAmelCase ).standard_normal((1, 4, 64, 64) )
A__ = torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase ,dtype=__UpperCAmelCase )
A__ = {
'prompt': 'a photograph of an astronaut riding a horse',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def snake_case__ ( self ) -> Optional[Any]:
A__ = StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d' )
A__ = ldmad_pipe.to(__UpperCAmelCase )
ldmad_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
A__ = self.get_inputs(__UpperCAmelCase )
A__ = ldmad_pipe(**__UpperCAmelCase )
A__ , A__ = output.rgb, output.depth
A__ = rgb[0, -3:, -3:, -1].flatten()
A__ = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12)
A__ = np.array(
[0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6] )
A__ = np.array(
[0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3
@nightly
@require_torch_gpu
class UpperCamelCase__( unittest.TestCase ):
def snake_case__ ( self ) -> Tuple:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase="cpu" ,__UpperCAmelCase=torch.floataa ,__UpperCAmelCase=0 ) -> int:
A__ = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
A__ = np.random.RandomState(__UpperCAmelCase ).standard_normal((1, 4, 64, 64) )
A__ = torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase ,dtype=__UpperCAmelCase )
A__ = {
'prompt': 'a photograph of an astronaut riding a horse',
'latents': latents,
'generator': generator,
'num_inference_steps': 50,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def snake_case__ ( self ) -> str:
A__ = StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d' ).to(__UpperCAmelCase )
ldmad_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
A__ = self.get_inputs(__UpperCAmelCase )
A__ = ldmad_pipe(**__UpperCAmelCase )
A__ , A__ = output.rgb, output.depth
A__ = 0.4_9_5_5_8_6
A__ = 0.3_3_7_9_5_5_1_5
A__ = 1_1_2.4_8_5_1_8
A__ = 9_8.4_8_9_7_4_6
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
def snake_case__ ( self ) -> Optional[int]:
A__ = StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c' ).to(__UpperCAmelCase )
ldmad_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
A__ = self.get_inputs(__UpperCAmelCase )
A__ = ldmad_pipe(**__UpperCAmelCase )
A__ , A__ = output.rgb, output.depth
A__ = 0.4_1_9_4_1_2_7
A__ = 0.3_5_3_7_5_5_8_6
A__ = 0.5_6_3_8_5_0_2
A__ = 0.3_4_6_8_6_1_0_3
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
| 154
| 1
|
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'''vocab_file''': '''vocab.txt'''}
UpperCamelCase = {
'''vocab_file''': {
'''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
},
}
UpperCamelCase = {
'''openbmb/cpm-ant-10b''': 1024,
}
def lowercase_ ( _lowerCamelCase : int):
lowercase__ : Tuple = collections.OrderedDict()
with open(_lowerCamelCase , "r" , encoding="utf-8") as reader:
lowercase__ : Tuple = reader.readlines()
for index, token in enumerate(_lowerCamelCase):
lowercase__ : Optional[int] = token.rstrip("\n")
lowercase__ : Optional[int] = index
return vocab
class snake_case_ ( __A ):
def __init__( self : str , lowercase_ : int , lowercase_ : str="<unk>" , lowercase_ : List[Any]=2_00 ) -> Union[str, Any]:
lowercase__ : Any = vocab
lowercase__ : int = unk_token
lowercase__ : Union[str, Any] = max_input_chars_per_word
def __UpperCamelCase ( self : Dict , lowercase_ : Any ) -> List[Any]:
lowercase__ : List[str] = list(lowercase_ )
if len(lowercase_ ) > self.max_input_chars_per_word:
return [self.unk_token]
lowercase__ : int = 0
lowercase__ : Union[str, Any] = []
while start < len(lowercase_ ):
lowercase__ : int = len(lowercase_ )
lowercase__ : List[Any] = None
while start < end:
lowercase__ : int = "".join(chars[start:end] )
if substr in self.vocab:
lowercase__ : Tuple = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(lowercase_ )
lowercase__ : Dict = end
return sub_tokens
class snake_case_ ( __A ):
__A : List[str] = VOCAB_FILES_NAMES
__A : str = PRETRAINED_VOCAB_FILES_MAP
__A : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A : int = ["input_ids", "attention_mask"]
__A : Optional[Any] = False
def __init__( self : Optional[Any] , lowercase_ : str , lowercase_ : str="<d>" , lowercase_ : Dict="</d>" , lowercase_ : str="<s>" , lowercase_ : List[Any]="</s>" , lowercase_ : Tuple="<pad>" , lowercase_ : Optional[Any]="<unk>" , lowercase_ : Optional[Any]="</n>" , lowercase_ : int="</_>" , lowercase_ : List[Any]="left" , **lowercase_ : Union[str, Any] , ) -> Dict:
requires_backends(self , ["jieba"] )
super().__init__(
bod_token=lowercase_ , eod_token=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , pad_token=lowercase_ , unk_token=lowercase_ , line_token=lowercase_ , space_token=lowercase_ , padding_side=lowercase_ , **lowercase_ , )
lowercase__ : Dict = bod_token
lowercase__ : List[Any] = eod_token
lowercase__ : List[str] = load_vocab(lowercase_ )
lowercase__ : Tuple = self.encoder[space_token]
lowercase__ : Union[str, Any] = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
lowercase__ : Union[str, Any] = collections.OrderedDict(sorted(self.encoder.items() , key=lambda lowercase_ : x[1] ) )
lowercase__ : Optional[Any] = {v: k for k, v in self.encoder.items()}
lowercase__ : Dict = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
return self.encoder[self.bod_token]
@property
def __UpperCamelCase ( self : List[str] ) -> List[Any]:
return self.encoder[self.eod_token]
@property
def __UpperCamelCase ( self : Optional[Any] ) -> int:
return self.encoder["\n"]
@property
def __UpperCamelCase ( self : Any ) -> int:
return len(self.encoder )
def __UpperCamelCase ( self : List[str] ) -> List[str]:
return dict(self.encoder , **self.added_tokens_encoder )
def __UpperCamelCase ( self : Union[str, Any] , lowercase_ : int ) -> Tuple:
lowercase__ : Any = []
for x in jieba.cut(lowercase_ , cut_all=lowercase_ ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowercase_ ) )
return output_tokens
def __UpperCamelCase ( self : Any , lowercase_ : Union[str, Any] , **lowercase_ : str ) -> Dict:
lowercase__ : str = [i for i in token_ids if i >= 0]
lowercase__ : List[str] = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(lowercase_ , **lowercase_ )
def __UpperCamelCase ( self : List[str] , lowercase_ : Dict ) -> int:
return token in self.encoder
def __UpperCamelCase ( self : str , lowercase_ : List[str] ) -> str:
return "".join(lowercase_ )
def __UpperCamelCase ( self : Optional[Any] , lowercase_ : Union[str, Any] ) -> List[str]:
return self.encoder.get(lowercase_ , self.encoder.get(self.unk_token ) )
def __UpperCamelCase ( self : Any , lowercase_ : List[str] ) -> Optional[int]:
return self.decoder.get(lowercase_ , self.unk_token )
def __UpperCamelCase ( self : Union[str, Any] , lowercase_ : str , lowercase_ : Optional[str] = None ) -> Tuple[str]:
if os.path.isdir(lowercase_ ):
lowercase__ : Dict = os.path.join(
lowercase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
lowercase__ : Optional[Any] = (filename_prefix + "-" if filename_prefix else "") + save_directory
lowercase__ : int = 0
if " " in self.encoder:
lowercase__ : Tuple = self.encoder[" "]
del self.encoder[" "]
if "\n" in self.encoder:
lowercase__ : List[str] = self.encoder["\n"]
del self.encoder["\n"]
lowercase__ : Optional[Any] = collections.OrderedDict(sorted(self.encoder.items() , key=lambda lowercase_ : x[1] ) )
with open(lowercase_ , "w" , encoding="utf-8" ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
" Please check that the vocabulary is not corrupted!" )
lowercase__ : Union[str, Any] = token_index
writer.write(token + "\n" )
index += 1
return (vocab_file,)
def __UpperCamelCase ( self : Union[str, Any] , lowercase_ : List[int] , lowercase_ : List[int] = None ) -> List[int]:
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def __UpperCamelCase ( self : List[Any] , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None , lowercase_ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase_ , token_ids_a=lowercase_ , already_has_special_tokens=lowercase_ )
if token_ids_a is not None:
return [1] + ([0] * len(lowercase_ )) + [1] + ([0] * len(lowercase_ ))
return [1] + ([0] * len(lowercase_ ))
| 87
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCamelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ['''GPTSw3Tokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_swa import GPTSwaTokenizer
else:
import sys
UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 87
| 1
|
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder):
"""simple docstring"""
def lowercase__ ( self : Union[str, Any] )->Dict:
return datasets.DatasetInfo(
features=datasets.Features({'''content''': datasets.Value('''string''' )} ) , supervised_keys=__UpperCamelCase , )
def lowercase__ ( self : str , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[Any] )->Tuple:
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_dummy_examples()} )]
def lowercase__ ( self : List[Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : List[str] )->int:
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(__UpperCamelCase )
class _a ( datasets.BeamBasedBuilder):
"""simple docstring"""
def lowercase__ ( self : Optional[int] )->Optional[Any]:
return datasets.DatasetInfo(
features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) , supervised_keys=__UpperCamelCase , )
def lowercase__ ( self : int , __UpperCamelCase : Dict , __UpperCamelCase : str )->Tuple:
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_nested_examples()} )
]
def lowercase__ ( self : Any , __UpperCamelCase : Any , __UpperCamelCase : str )->str:
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(__UpperCamelCase )
def lowercase ( ):
'''simple docstring'''
return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''] )]
def lowercase ( ):
'''simple docstring'''
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''] )]
class _a ( lowerCAmelCase):
"""simple docstring"""
@require_beam
def lowercase__ ( self : int )->str:
_UpperCAmelCase = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_UpperCAmelCase = DummyBeamDataset(cache_dir=__UpperCamelCase , beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(__UpperCamelCase , builder.name , '''default''' , '''0.0.0''' , F'{builder.name}-train.arrow' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''' )} ) )
_UpperCAmelCase = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , __UpperCamelCase )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __UpperCamelCase )
self.assertDictEqual(dset['''train'''][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(__UpperCamelCase , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
@require_beam
def lowercase__ ( self : Any )->int:
import apache_beam as beam
_UpperCAmelCase = beam.io.parquetio.WriteToParquet
_UpperCAmelCase = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_UpperCAmelCase = DummyBeamDataset(cache_dir=__UpperCamelCase , beam_runner='''DirectRunner''' )
with patch('''apache_beam.io.parquetio.WriteToParquet''' ) as write_parquet_mock:
_UpperCAmelCase = partial(__UpperCamelCase , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
__UpperCamelCase , builder.name , '''default''' , '''0.0.0''' , F'{builder.name}-train-00000-of-00002.arrow' ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
__UpperCamelCase , builder.name , '''default''' , '''0.0.0''' , F'{builder.name}-train-00000-of-00002.arrow' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''' )} ) )
_UpperCAmelCase = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , __UpperCamelCase )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __UpperCamelCase )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset['''train''']['''content'''] ) , sorted(['''foo''', '''bar''', '''foobar'''] ) )
self.assertTrue(
os.path.exists(os.path.join(__UpperCamelCase , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
@require_beam
def lowercase__ ( self : Dict )->Dict:
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_UpperCAmelCase = DummyBeamDataset(cache_dir=__UpperCamelCase )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def lowercase__ ( self : Optional[Any] )->int:
_UpperCAmelCase = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_UpperCAmelCase = NestedBeamDataset(cache_dir=__UpperCamelCase , beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(__UpperCamelCase , builder.name , '''default''' , '''0.0.0''' , F'{builder.name}-train.arrow' ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) )
_UpperCAmelCase = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , __UpperCamelCase )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __UpperCamelCase )
self.assertDictEqual(dset['''train'''][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(__UpperCamelCase , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
| 326
|
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
with open(_SCREAMING_SNAKE_CASE ) as metadata_file:
_UpperCAmelCase = json.load(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = LukeConfig(use_entity_aware_attention=_SCREAMING_SNAKE_CASE , **metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
_UpperCAmelCase = torch.load(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )['''module''']
# Load the entity vocab file
_UpperCAmelCase = load_original_entity_vocab(_SCREAMING_SNAKE_CASE )
# add an entry for [MASK2]
_UpperCAmelCase = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
_UpperCAmelCase = XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
_UpperCAmelCase = AddedToken('''<ent>''' , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = AddedToken('''<ent2>''' , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
with open(os.path.join(_SCREAMING_SNAKE_CASE , '''tokenizer_config.json''' ) , '''r''' ) as f:
_UpperCAmelCase = json.load(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = '''MLukeTokenizer'''
with open(os.path.join(_SCREAMING_SNAKE_CASE , '''tokenizer_config.json''' ) , '''w''' ) as f:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
with open(os.path.join(_SCREAMING_SNAKE_CASE , MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ) , '''w''' ) as f:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
# Initialize the embeddings of the special tokens
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(['''@'''] )[0]
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(['''#'''] )[0]
_UpperCAmelCase = state_dict['''embeddings.word_embeddings.weight''']
_UpperCAmelCase = word_emb[ent_init_index].unsqueeze(0 )
_UpperCAmelCase = word_emb[enta_init_index].unsqueeze(0 )
_UpperCAmelCase = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
_UpperCAmelCase = state_dict[bias_name]
_UpperCAmelCase = decoder_bias[ent_init_index].unsqueeze(0 )
_UpperCAmelCase = decoder_bias[enta_init_index].unsqueeze(0 )
_UpperCAmelCase = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
_UpperCAmelCase = f'encoder.layer.{layer_index}.attention.self.'
_UpperCAmelCase = state_dict[prefix + matrix_name]
_UpperCAmelCase = state_dict[prefix + matrix_name]
_UpperCAmelCase = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
_UpperCAmelCase = state_dict['''entity_embeddings.entity_embeddings.weight''']
_UpperCAmelCase = entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
_UpperCAmelCase = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
_UpperCAmelCase = state_dict['''entity_predictions.bias''']
_UpperCAmelCase = entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
_UpperCAmelCase = torch.cat([entity_prediction_bias, entity_mask_bias] )
_UpperCAmelCase = LukeForMaskedLM(config=_SCREAMING_SNAKE_CASE ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
_UpperCAmelCase = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
_UpperCAmelCase = state_dict[key]
else:
_UpperCAmelCase = state_dict[key]
_UpperCAmelCase , _UpperCAmelCase = model.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
if set(_SCREAMING_SNAKE_CASE ) != {"luke.embeddings.position_ids"}:
raise ValueError(f'Unexpected unexpected_keys: {unexpected_keys}' )
if set(_SCREAMING_SNAKE_CASE ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(f'Unexpected missing_keys: {missing_keys}' )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
_UpperCAmelCase = MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE , task='''entity_classification''' )
_UpperCAmelCase = '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
_UpperCAmelCase = (0, 9)
_UpperCAmelCase = tokenizer(_SCREAMING_SNAKE_CASE , entity_spans=[span] , return_tensors='''pt''' )
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_UpperCAmelCase = torch.Size((1, 33, 768) )
_UpperCAmelCase = torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_UpperCAmelCase = torch.Size((1, 1, 768) )
_UpperCAmelCase = torch.tensor([[-0.1482, 0.0609, 0.0322]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
f' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ):
raise ValueError
# Verify masked word/entity prediction
_UpperCAmelCase = MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = '''Tokyo is the capital of <mask>.'''
_UpperCAmelCase = (24, 30)
_UpperCAmelCase = tokenizer(_SCREAMING_SNAKE_CASE , entity_spans=[span] , return_tensors='''pt''' )
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = encoding['''input_ids'''][0].tolist()
_UpperCAmelCase = input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
_UpperCAmelCase = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.entity_logits[0][0].argmax().item()
_UpperCAmelCase = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(_SCREAMING_SNAKE_CASE ) )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
_UpperCAmelCase = ['''[MASK]''', '''[PAD]''', '''[UNK]''']
_UpperCAmelCase = [json.loads(_SCREAMING_SNAKE_CASE ) for line in open(_SCREAMING_SNAKE_CASE )]
_UpperCAmelCase = {}
for entry in data:
_UpperCAmelCase = entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
_UpperCAmelCase = entity_id
break
_UpperCAmelCase = f'{language}:{entity_name}'
_UpperCAmelCase = entity_id
return new_mapping
if __name__ == "__main__":
__A : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
parser.add_argument(
"--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
)
parser.add_argument(
"--entity_vocab_path",
default=None,
type=str,
help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
)
parser.add_argument(
"--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
)
__A : List[str] = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
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from queue import PriorityQueue
from typing import Any
import numpy as np
def __lowercase ( __lowerCAmelCase : dict , __lowerCAmelCase : str , __lowerCAmelCase : set , __lowerCAmelCase : set , __lowerCAmelCase : dict , __lowerCAmelCase : dict , __lowerCAmelCase : PriorityQueue , __lowerCAmelCase : dict , __lowerCAmelCase : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
a__ = cst_fwd.get(__lowerCAmelCase , np.inf )
a__ = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
a__ = new_cost_f
a__ = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
a__ = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def __lowercase ( __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : dict , __lowerCAmelCase : dict ):
a__ = -1
a__ = set()
a__ = set()
a__ = {source: 0}
a__ = {destination: 0}
a__ = {source: None}
a__ = {destination: None}
a__ = PriorityQueue()
a__ = PriorityQueue()
a__ = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
a__ , a__ = queue_forward.get()
visited_forward.add(__lowerCAmelCase )
a__ , a__ = queue_backward.get()
visited_backward.add(__lowerCAmelCase )
a__ = pass_and_relaxation(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )
a__ = pass_and_relaxation(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
a__ = shortest_distance
return shortest_path_distance
snake_case : Union[str, Any] = {
'''B''': [['''C''', 1]],
'''C''': [['''D''', 1]],
'''D''': [['''F''', 1]],
'''E''': [['''B''', 1], ['''G''', 2]],
'''F''': [],
'''G''': [['''F''', 1]],
}
snake_case : Optional[int] = {
'''B''': [['''E''', 1]],
'''C''': [['''B''', 1]],
'''D''': [['''C''', 1]],
'''F''': [['''D''', 1], ['''G''', 1]],
'''E''': [[None, np.inf]],
'''G''': [['''E''', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
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|
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class snake_case_ (unittest.TestCase ):
def lowerCamelCase__( self :Any ,__snake_case :List[Any] ) -> Union[str, Any]:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] ,model_result['ss'] ):
a__ = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(__snake_case )
def lowerCamelCase__( self :List[str] ) -> Union[str, Any]:
a__ = 'sshleifer/tiny-gpt2'
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__( self :List[Any] ) -> Union[str, Any]:
a__ = 'sgugger/tiny-distilbert-classification'
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,only_pretrain_model=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__( self :Optional[Any] ) -> Optional[Any]:
a__ = 'sshleifer/tiny-gpt2'
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,torchscript=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == 'cpu' ,'Cant do half precision' )
def lowerCamelCase__( self :int ) -> str:
a__ = 'sshleifer/tiny-gpt2'
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,fpaa=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__( self :Optional[int] ) -> Union[str, Any]:
a__ = 'sshleifer/tiny-gpt2'
a__ = AutoConfig.from_pretrained(__snake_case )
# set architectures equal to `None`
a__ = None
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case ,configs=[config] )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__( self :Dict ) -> int:
a__ = 'sshleifer/tiny-gpt2'
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == 'cpu' ,'Can\'t do half precision' )
def lowerCamelCase__( self :int ) -> List[str]:
a__ = 'sshleifer/tiny-gpt2'
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,fpaa=__snake_case ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowerCamelCase__( self :str ) -> Union[str, Any]:
a__ = 'sshleifer/tiny-gpt2'
a__ = AutoConfig.from_pretrained(__snake_case )
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case ,configs=[config] )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__( self :List[Any] ) -> Any:
a__ = 'sshleifer/tinier_bart'
a__ = AutoConfig.from_pretrained(__snake_case )
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case ,configs=[config] )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__( self :Tuple ) -> Dict:
a__ = 'sshleifer/tiny-gpt2'
a__ = AutoConfig.from_pretrained(__snake_case )
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case ,configs=[config] )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowerCamelCase__( self :Union[str, Any] ) -> Optional[int]:
a__ = 'sshleifer/tinier_bart'
a__ = AutoConfig.from_pretrained(__snake_case )
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case ,configs=[config] )
a__ = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowerCamelCase__( self :Optional[int] ) -> List[Any]:
a__ = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,save_to_csv=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,inference_time_csv_file=os.path.join(__snake_case ,'inf_time.csv' ) ,train_memory_csv_file=os.path.join(__snake_case ,'train_mem.csv' ) ,inference_memory_csv_file=os.path.join(__snake_case ,'inf_mem.csv' ) ,train_time_csv_file=os.path.join(__snake_case ,'train_time.csv' ) ,env_info_csv_file=os.path.join(__snake_case ,'env.csv' ) ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
benchmark.run()
self.assertTrue(Path(os.path.join(__snake_case ,'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__snake_case ,'train_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__snake_case ,'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__snake_case ,'train_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__snake_case ,'env.csv' ) ).exists() )
def lowerCamelCase__( self :Optional[int] ) -> List[str]:
a__ = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(__snake_case :List[str] ):
self.assertTrue(hasattr(__snake_case ,'sequential' ) )
self.assertTrue(hasattr(__snake_case ,'cumulative' ) )
self.assertTrue(hasattr(__snake_case ,'current' ) )
self.assertTrue(hasattr(__snake_case ,'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
a__ = PyTorchBenchmarkArguments(
models=[MODEL_ID] ,training=__snake_case ,inference=__snake_case ,sequence_lengths=[8] ,batch_sizes=[1] ,log_filename=os.path.join(__snake_case ,'log.txt' ) ,log_print=__snake_case ,trace_memory_line_by_line=__snake_case ,multi_process=__snake_case ,)
a__ = PyTorchBenchmark(__snake_case )
a__ = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(__snake_case ,'log.txt' ) ).exists() )
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|
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
__A = {"LayoutLMv2Config", "LayoutLMv3Config"}
@is_pipeline_test
class UpperCAmelCase (unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase :Tuple = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_UpperCAmelCase :Union[str, Any] = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
_UpperCAmelCase :Any = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
_UpperCAmelCase :Optional[Any] = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Optional[int] = ZeroShotClassificationPipeline(
model=_UpperCAmelCase , tokenizer=_UpperCAmelCase , candidate_labels=['''polics''', '''health'''] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Any = classifier('''Who are you voting for in 2020?''' , candidate_labels='''politics''' )
self.assertEqual(_UpperCAmelCase , {'''sequence''': ANY(_UpperCAmelCase ), '''labels''': [ANY(_UpperCAmelCase )], '''scores''': [ANY(_UpperCAmelCase )]} )
# No kwarg
lowercase__: int = classifier('''Who are you voting for in 2020?''' , ['''politics'''] )
self.assertEqual(_UpperCAmelCase , {'''sequence''': ANY(_UpperCAmelCase ), '''labels''': [ANY(_UpperCAmelCase )], '''scores''': [ANY(_UpperCAmelCase )]} )
lowercase__: Dict = classifier('''Who are you voting for in 2020?''' , candidate_labels=['''politics'''] )
self.assertEqual(_UpperCAmelCase , {'''sequence''': ANY(_UpperCAmelCase ), '''labels''': [ANY(_UpperCAmelCase )], '''scores''': [ANY(_UpperCAmelCase )]} )
lowercase__: List[Any] = classifier('''Who are you voting for in 2020?''' , candidate_labels='''politics, public health''' )
self.assertEqual(
_UpperCAmelCase , {'''sequence''': ANY(_UpperCAmelCase ), '''labels''': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )], '''scores''': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['''scores'''] ) ) , 1.0 )
lowercase__: Optional[Any] = classifier('''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health'''] )
self.assertEqual(
_UpperCAmelCase , {'''sequence''': ANY(_UpperCAmelCase ), '''labels''': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )], '''scores''': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['''scores'''] ) ) , 1.0 )
lowercase__: Tuple = classifier(
'''Who are you voting for in 2020?''' , candidate_labels='''politics''' , hypothesis_template='''This text is about {}''' )
self.assertEqual(_UpperCAmelCase , {'''sequence''': ANY(_UpperCAmelCase ), '''labels''': [ANY(_UpperCAmelCase )], '''scores''': [ANY(_UpperCAmelCase )]} )
# https://github.com/huggingface/transformers/issues/13846
lowercase__: Union[str, Any] = classifier(['''I am happy'''] , ['''positive''', '''negative'''] )
self.assertEqual(
_UpperCAmelCase , [
{'''sequence''': ANY(_UpperCAmelCase ), '''labels''': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )], '''scores''': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )]}
for i in range(1 )
] , )
lowercase__: Any = classifier(['''I am happy''', '''I am sad'''] , ['''positive''', '''negative'''] )
self.assertEqual(
_UpperCAmelCase , [
{'''sequence''': ANY(_UpperCAmelCase ), '''labels''': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )], '''scores''': [ANY(_UpperCAmelCase ), ANY(_UpperCAmelCase )]}
for i in range(2 )
] , )
with self.assertRaises(_UpperCAmelCase ):
classifier('''''' , candidate_labels='''politics''' )
with self.assertRaises(_UpperCAmelCase ):
classifier(_UpperCAmelCase , candidate_labels='''politics''' )
with self.assertRaises(_UpperCAmelCase ):
classifier('''Who are you voting for in 2020?''' , candidate_labels='''''' )
with self.assertRaises(_UpperCAmelCase ):
classifier('''Who are you voting for in 2020?''' , candidate_labels=_UpperCAmelCase )
with self.assertRaises(_UpperCAmelCase ):
classifier(
'''Who are you voting for in 2020?''' , candidate_labels='''politics''' , hypothesis_template='''Not formatting template''' , )
with self.assertRaises(_UpperCAmelCase ):
classifier(
'''Who are you voting for in 2020?''' , candidate_labels='''politics''' , hypothesis_template=_UpperCAmelCase , )
self.run_entailment_id(_UpperCAmelCase )
def _snake_case ( self , _UpperCAmelCase ):
lowercase__: List[Any] = zero_shot_classifier.model.config
lowercase__: Optional[Any] = config.labelaid
lowercase__: Dict = zero_shot_classifier.entailment_id
lowercase__: Optional[int] = {'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
lowercase__: Union[str, Any] = {'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
lowercase__: Union[str, Any] = {'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
lowercase__: Optional[int] = {'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
lowercase__: List[str] = original_labelaid
self.assertEqual(_UpperCAmelCase , zero_shot_classifier.entailment_id )
@require_torch
def _snake_case ( self ):
lowercase__: Tuple = pipeline(
'''zero-shot-classification''' , model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''' , framework='''pt''' , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
'''Who are you voting for in 2020?''' * 100 , candidate_labels=['''politics''', '''public health''', '''science'''] )
@require_torch
def _snake_case ( self ):
lowercase__: Tuple = pipeline(
'''zero-shot-classification''' , model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''' , framework='''pt''' , )
lowercase__: List[Any] = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''science''', '''public health''', '''politics'''],
'''scores''': [0.333, 0.333, 0.333],
} , )
@require_tf
def _snake_case ( self ):
lowercase__: List[Any] = pipeline(
'''zero-shot-classification''' , model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''' , framework='''tf''' , )
lowercase__: List[Any] = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''science''', '''public health''', '''politics'''],
'''scores''': [0.333, 0.333, 0.333],
} , )
@slow
@require_torch
def _snake_case ( self ):
lowercase__: List[Any] = pipeline('''zero-shot-classification''' , model='''roberta-large-mnli''' , framework='''pt''' )
lowercase__: Optional[int] = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''politics''', '''public health''', '''science'''],
'''scores''': [0.976, 0.015, 0.009],
} , )
lowercase__: Optional[int] = zero_shot_classifier(
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
''' English-to-German translation task, improving over the existing best results, including ensembles by'''
''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
''' large and limited training data.''' , candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision'''] , multi_label=_UpperCAmelCase , )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
'''sequence''': (
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
''' networks in an encoder-decoder configuration. The best performing models also connect the'''
''' encoder and decoder through an attention mechanism. We propose a new simple network'''
''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
''' superior in quality while being more parallelizable and requiring significantly less time to'''
''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
''' other tasks by applying it successfully to English constituency parsing both with large and'''
''' limited training data.'''
),
'''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
'''scores''': [0.817, 0.713, 0.018, 0.018],
} , )
@slow
@require_tf
def _snake_case ( self ):
lowercase__: Union[str, Any] = pipeline('''zero-shot-classification''' , model='''roberta-large-mnli''' , framework='''tf''' )
lowercase__: List[Any] = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''politics''', '''public health''', '''science'''],
'''scores''': [0.976, 0.015, 0.009],
} , )
lowercase__: Tuple = zero_shot_classifier(
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
''' English-to-German translation task, improving over the existing best results, including ensembles by'''
''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
''' large and limited training data.''' , candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision'''] , multi_label=_UpperCAmelCase , )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) , {
'''sequence''': (
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
''' networks in an encoder-decoder configuration. The best performing models also connect the'''
''' encoder and decoder through an attention mechanism. We propose a new simple network'''
''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
''' superior in quality while being more parallelizable and requiring significantly less time to'''
''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
''' other tasks by applying it successfully to English constituency parsing both with large and'''
''' limited training data.'''
),
'''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
'''scores''': [0.817, 0.713, 0.018, 0.018],
} , )
| 2
|
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
__A = "hf-internal-testing/tiny-random-bert"
__A = os.path.join(TRANSFORMERS_CACHE, "models--hf-internal-testing--tiny-random-bert")
__A = "9b8c223d42b2188cb49d29af482996f9d0f3e5a6"
class UpperCAmelCase (unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ):
lowercase__: Union[str, Any] = cached_file(_UpperCAmelCase , _UpperCAmelCase )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(_UpperCAmelCase ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) ) )
with open(os.path.join(_UpperCAmelCase , '''refs''' , '''main''' ) ) as f:
lowercase__: Dict = f.read()
self.assertEqual(_UpperCAmelCase , os.path.join(_UpperCAmelCase , '''snapshots''' , _UpperCAmelCase , _UpperCAmelCase ) )
self.assertTrue(os.path.isfile(_UpperCAmelCase ) )
# File is cached at the same place the second time.
lowercase__: Any = cached_file(_UpperCAmelCase , _UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
# Using a specific revision to test the full commit hash.
lowercase__: Dict = cached_file(_UpperCAmelCase , _UpperCAmelCase , revision='''9b8c223''' )
self.assertEqual(_UpperCAmelCase , os.path.join(_UpperCAmelCase , '''snapshots''' , _UpperCAmelCase , _UpperCAmelCase ) )
def _snake_case ( self ):
with self.assertRaisesRegex(_UpperCAmelCase , '''is not a valid model identifier''' ):
lowercase__: int = cached_file('''tiny-random-bert''' , _UpperCAmelCase )
with self.assertRaisesRegex(_UpperCAmelCase , '''is not a valid git identifier''' ):
lowercase__: List[Any] = cached_file(_UpperCAmelCase , _UpperCAmelCase , revision='''aaaa''' )
with self.assertRaisesRegex(_UpperCAmelCase , '''does not appear to have a file named''' ):
lowercase__: Dict = cached_file(_UpperCAmelCase , '''conf''' )
def _snake_case ( self ):
with self.assertRaisesRegex(_UpperCAmelCase , '''does not appear to have a file named''' ):
lowercase__: Optional[Any] = cached_file(_UpperCAmelCase , '''conf''' )
with open(os.path.join(_UpperCAmelCase , '''refs''' , '''main''' ) ) as f:
lowercase__: int = f.read()
self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase , '''.no_exist''' , _UpperCAmelCase , '''conf''' ) ) )
lowercase__: Dict = cached_file(_UpperCAmelCase , '''conf''' , _raise_exceptions_for_missing_entries=_UpperCAmelCase )
self.assertIsNone(_UpperCAmelCase )
lowercase__: List[str] = cached_file(_UpperCAmelCase , '''conf''' , local_files_only=_UpperCAmelCase , _raise_exceptions_for_missing_entries=_UpperCAmelCase )
self.assertIsNone(_UpperCAmelCase )
lowercase__: Union[str, Any] = mock.Mock()
lowercase__: str = 500
lowercase__: Union[str, Any] = {}
lowercase__: List[str] = HTTPError
lowercase__: int = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('''requests.Session.request''' , return_value=_UpperCAmelCase ) as mock_head:
lowercase__: Any = cached_file(_UpperCAmelCase , '''conf''' , _raise_exceptions_for_connection_errors=_UpperCAmelCase )
self.assertIsNone(_UpperCAmelCase )
# This check we did call the fake head request
mock_head.assert_called()
def _snake_case ( self ):
self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only''' , _UpperCAmelCase ) )
self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' , _UpperCAmelCase ) )
self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' , _UpperCAmelCase ) )
def _snake_case ( self ):
# `get_file_from_repo` returns None if the file does not exist
self.assertIsNone(get_file_from_repo('''bert-base-cased''' , '''ahah.txt''' ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(_UpperCAmelCase , '''is not a valid model identifier''' ):
get_file_from_repo('''bert-base-case''' , _UpperCAmelCase )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(_UpperCAmelCase , '''is not a valid git identifier''' ):
get_file_from_repo('''bert-base-cased''' , _UpperCAmelCase , revision='''ahaha''' )
lowercase__: Optional[Any] = get_file_from_repo('''bert-base-cased''' , _UpperCAmelCase )
# The name is the cached name which is not very easy to test, so instead we load the content.
lowercase__: Optional[Any] = json.loads(open(_UpperCAmelCase , '''r''' ).read() )
self.assertEqual(config['''hidden_size'''] , 768 )
def _snake_case ( self ):
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase__: Any = Path(_UpperCAmelCase ) / '''a.txt'''
filename.touch()
self.assertEqual(get_file_from_repo(_UpperCAmelCase , '''a.txt''' ) , str(_UpperCAmelCase ) )
self.assertIsNone(get_file_from_repo(_UpperCAmelCase , '''b.txt''' ) )
| 2
| 1
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
'umberto-commoncrawl-cased-v1': (
'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
),
'umberto-wikipedia-uncased-v1': (
'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
),
}
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = """camembert"""
def __init__( self : Any , __lowerCamelCase : Tuple=3_05_22 , __lowerCamelCase : Optional[Any]=7_68 , __lowerCamelCase : Tuple=12 , __lowerCamelCase : Any=12 , __lowerCamelCase : Any=30_72 , __lowerCamelCase : str="gelu" , __lowerCamelCase : int=0.1 , __lowerCamelCase : Dict=0.1 , __lowerCamelCase : Optional[Any]=5_12 , __lowerCamelCase : Any=2 , __lowerCamelCase : str=0.02 , __lowerCamelCase : Optional[Any]=1e-12 , __lowerCamelCase : int=1 , __lowerCamelCase : Union[str, Any]=0 , __lowerCamelCase : List[str]=2 , __lowerCamelCase : Tuple="absolute" , __lowerCamelCase : List[str]=True , __lowerCamelCase : Dict=None , **__lowerCamelCase : Dict , ) -> Tuple:
super().__init__(pad_token_id=__lowerCamelCase , bos_token_id=__lowerCamelCase , eos_token_id=__lowerCamelCase , **__lowerCamelCase )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = position_embedding_type
a = use_cache
a = classifier_dropout
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
@property
def __UpperCAmelCase ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
a = {0: "batch", 1: "choice", 2: "sequence"}
else:
a = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 107
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class snake_case ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[Any], _lowerCamelCase : Tuple, _lowerCamelCase : List[str]=13, _lowerCamelCase : Optional[Any]=7, _lowerCamelCase : Optional[int]=True, _lowerCamelCase : int=True, _lowerCamelCase : List[str]=True, _lowerCamelCase : Optional[Any]=True, _lowerCamelCase : int=99, _lowerCamelCase : Optional[int]=32, _lowerCamelCase : Tuple=5, _lowerCamelCase : Tuple=4, _lowerCamelCase : str=37, _lowerCamelCase : Union[str, Any]="gelu", _lowerCamelCase : int=0.1, _lowerCamelCase : List[Any]=0.1, _lowerCamelCase : Dict=5_12, _lowerCamelCase : List[Any]=16, _lowerCamelCase : Any=2, _lowerCamelCase : Any=0.02, _lowerCamelCase : Dict=4, ):
'''simple docstring'''
__A = parent
__A = batch_size
__A = seq_length
__A = is_training
__A = use_attention_mask
__A = use_token_type_ids
__A = use_labels
__A = vocab_size
__A = hidden_size
__A = num_hidden_layers
__A = num_attention_heads
__A = intermediate_size
__A = hidden_act
__A = hidden_dropout_prob
__A = attention_probs_dropout_prob
__A = max_position_embeddings
__A = type_vocab_size
__A = type_sequence_label_size
__A = initializer_range
__A = num_choices
def _SCREAMING_SNAKE_CASE ( self : Any ):
'''simple docstring'''
__A = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
__A = None
if self.use_attention_mask:
__A = random_attention_mask([self.batch_size, self.seq_length] )
__A = None
if self.use_token_type_ids:
__A = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
__A = RoFormerConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=_lowerCamelCase, initializer_range=self.initializer_range, )
return config, input_ids, token_type_ids, attention_mask
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
'''simple docstring'''
__A = self.prepare_config_and_inputs()
__A , __A , __A , __A = config_and_inputs
__A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class snake_case ( _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
A_ : Dict = True
A_ : Tuple = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
'''simple docstring'''
__A = FlaxRoFormerModelTester(self )
@slow
def _SCREAMING_SNAKE_CASE ( self : Any ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
__A = model_class_name.from_pretrained('''junnyu/roformer_chinese_small''', from_pt=_lowerCamelCase )
__A = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowerCamelCase )
@require_flax
class snake_case ( unittest.TestCase ):
'''simple docstring'''
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict ):
'''simple docstring'''
__A = FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
__A = jnp.array([[0, 1, 2, 3, 4, 5]] )
__A = model(_lowerCamelCase )[0]
__A = 5_00_00
__A = (1, 6, vocab_size)
self.assertEqual(output.shape, _lowerCamelCase )
__A = jnp.array(
[[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3], _lowerCamelCase, atol=1e-4 ) )
| 266
| 0
|
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
SCREAMING_SNAKE_CASE :Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Tuple = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
SCREAMING_SNAKE_CASE :Optional[int] = {
'vocab_file': {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
'roberta-large-openai-detector': (
'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
),
},
'merges_file': {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
'roberta-large-openai-detector': (
'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
),
},
'tokenizer_file': {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
'roberta-base-openai-detector': (
'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
),
'roberta-large-openai-detector': (
'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
),
},
}
SCREAMING_SNAKE_CASE :List[str] = {
'roberta-base': 512,
'roberta-large': 512,
'roberta-large-mnli': 512,
'distilroberta-base': 512,
'roberta-base-openai-detector': 512,
'roberta-large-openai-detector': 512,
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["input_ids", "attention_mask"]
snake_case_ = RobertaTokenizer
def __init__( self : Tuple ,A : int=None ,A : Dict=None ,A : Optional[int]=None ,A : Tuple="replace" ,A : str="<s>" ,A : str="</s>" ,A : Union[str, Any]="</s>" ,A : int="<s>" ,A : str="<unk>" ,A : int="<pad>" ,A : Union[str, Any]="<mask>" ,A : List[Any]=False ,A : Tuple=True ,**A : Dict ,):
super().__init__(
A ,A ,tokenizer_file=A ,errors=A ,bos_token=A ,eos_token=A ,sep_token=A ,cls_token=A ,unk_token=A ,pad_token=A ,mask_token=A ,add_prefix_space=A ,trim_offsets=A ,**A ,)
__A = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" ,A ) != add_prefix_space:
__A = getattr(A ,pre_tok_state.pop("type" ) )
__A = add_prefix_space
__A = pre_tok_class(**A )
__A = add_prefix_space
__A = "post_processor"
__A = getattr(self.backend_tokenizer ,A ,A )
if tokenizer_component_instance:
__A = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__A = tuple(state["sep"] )
if "cls" in state:
__A = tuple(state["cls"] )
__A = False
if state.get("add_prefix_space" ,A ) != add_prefix_space:
__A = add_prefix_space
__A = True
if state.get("trim_offsets" ,A ) != trim_offsets:
__A = trim_offsets
__A = True
if changes_to_apply:
__A = getattr(A ,state.pop("type" ) )
__A = component_class(**A )
setattr(self.backend_tokenizer ,A ,A )
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCamelCase_ ( self : Tuple ,A : List[Any] ):
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else value
__A = value
def UpperCamelCase_ ( self : Dict ,*A : List[str] ,**A : List[str] ):
__A = kwargs.get("is_split_into_words" ,A )
assert self.add_prefix_space or not is_split_into_words, (
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*A ,**A )
def UpperCamelCase_ ( self : Union[str, Any] ,*A : str ,**A : int ):
__A = kwargs.get("is_split_into_words" ,A )
assert self.add_prefix_space or not is_split_into_words, (
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*A ,**A )
def UpperCamelCase_ ( self : str ,A : str ,A : Optional[str] = None ):
__A = self._tokenizer.model.save(A ,name=A )
return tuple(A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : Tuple ,A : Tuple=None ):
__A = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCamelCase_ ( self : Any ,A : List[int] ,A : Optional[List[int]] = None ):
__A = [self.sep_token_id]
__A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 363
|
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "bloom"
snake_case_ = ["past_key_values"]
snake_case_ = {
"num_hidden_layers": "n_layer",
"num_attention_heads": "n_head",
}
def __init__( self : Optional[Any] ,A : List[Any]=25_08_80 ,A : Optional[int]=64 ,A : List[Any]=2 ,A : Optional[int]=8 ,A : str=1E-5 ,A : str=0.02 ,A : int=True ,A : Optional[Any]=1 ,A : int=2 ,A : str=False ,A : Dict=0.0 ,A : List[Any]=0.0 ,A : str=1 ,A : List[Any]=False ,**A : List[Any] ,):
__A = vocab_size
# Backward compatibility with n_embed kwarg
__A = kwargs.pop("n_embed" ,A )
__A = hidden_size if n_embed is None else n_embed
__A = n_layer
__A = n_head
__A = layer_norm_epsilon
__A = initializer_range
__A = use_cache
__A = pretraining_tp
__A = apply_residual_connection_post_layernorm
__A = hidden_dropout
__A = attention_dropout
__A = bos_token_id
__A = eos_token_id
__A = slow_but_exact
super().__init__(bos_token_id=A ,eos_token_id=A ,**A )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = version.parse("1.12" )
def __init__( self : str ,A : PretrainedConfig ,A : str = "default" ,A : List[PatchingSpec] = None ,A : bool = False ,):
super().__init__(A ,task=A ,patching_specs=A ,use_past=A )
if not getattr(self._config ,"pad_token_id" ,A ):
# TODO: how to do that better?
__A = 0
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
self.fill_with_past_key_values_(A ,direction="inputs" ,inverted_values_shape=A )
__A = {0: "batch", 1: "past_sequence + sequence"}
else:
__A = {0: "batch", 1: "sequence"}
return common_inputs
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return self._config.n_layer
@property
def UpperCamelCase_ ( self : List[Any] ):
return self._config.n_head
@property
def UpperCamelCase_ ( self : Optional[int] ):
return 1E-3
def UpperCamelCase_ ( self : Any ,A : "PreTrainedTokenizer" ,A : int = -1 ,A : int = -1 ,A : bool = False ,A : Optional["TensorType"] = None ,):
__A = super(A ,self ).generate_dummy_inputs(
A ,batch_size=A ,seq_length=A ,is_pair=A ,framework=A )
# We need to order the input in the way they appears in the forward()
__A = OrderedDict({"input_ids": common_inputs["input_ids"]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
__A , __A = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
__A = seqlen + 2
__A = self._config.hidden_size // self.num_attention_heads
__A = (
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
__A = (
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
__A = [
(torch.zeros(A ), torch.zeros(A )) for _ in range(self.num_layers )
]
__A = common_inputs["attention_mask"]
if self.use_past:
__A = ordered_inputs["attention_mask"].dtype
__A = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(A ,A ,dtype=A )] ,dim=1 )
return ordered_inputs
@property
def UpperCamelCase_ ( self : int ):
return 13
| 124
| 0
|
def __UpperCamelCase ( _A : int ) ->int:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicative_persistence() does not accept negative values""" )
lowerCamelCase_ =0
lowerCamelCase_ =str(_A )
while len(_A ) != 1:
lowerCamelCase_ =[int(_A ) for i in num_string]
lowerCamelCase_ =1
for i in range(0 , len(_A ) ):
total *= numbers[i]
lowerCamelCase_ =str(_A )
steps += 1
return steps
def __UpperCamelCase ( _A : int ) ->int:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError("""additive_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""additive_persistence() does not accept negative values""" )
lowerCamelCase_ =0
lowerCamelCase_ =str(_A )
while len(_A ) != 1:
lowerCamelCase_ =[int(_A ) for i in num_string]
lowerCamelCase_ =0
for i in range(0 , len(_A ) ):
total += numbers[i]
lowerCamelCase_ =str(_A )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 154
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__A : List[Any] = {
'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
'GPTNeoXJapaneseForCausalLM',
'GPTNeoXJapaneseLayer',
'GPTNeoXJapaneseModel',
'GPTNeoXJapanesePreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
__A : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 154
| 1
|
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class __snake_case ( _lowercase):
snake_case__ : torch.FloatTensor
class __snake_case ( _lowercase , _lowercase):
@register_to_config
def __init__( self : int , __lowerCAmelCase : int = 3_2 , __lowerCAmelCase : int = 6_4 , __lowerCAmelCase : int = 2_0 , __lowerCAmelCase : int = 7_6_8 , __lowerCAmelCase : Tuple=7_7 , __lowerCAmelCase : Tuple=4 , __lowerCAmelCase : float = 0.0 , __lowerCAmelCase : str = "silu" , __lowerCAmelCase : Optional[str] = None , __lowerCAmelCase : Optional[str] = None , __lowerCAmelCase : Optional[str] = "linear" , __lowerCAmelCase : Optional[str] = "prd" , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : Optional[int] = None , ):
"""simple docstring"""
super().__init__()
_lowerCamelCase : Any = num_attention_heads
_lowerCamelCase : Optional[int] = attention_head_dim
_lowerCamelCase : Dict = num_attention_heads * attention_head_dim
_lowerCamelCase : Optional[Any] = additional_embeddings
_lowerCamelCase : List[Any] = time_embed_dim or inner_dim
_lowerCamelCase : Optional[Any] = embedding_proj_dim or embedding_dim
_lowerCamelCase : Tuple = clip_embed_dim or embedding_dim
_lowerCamelCase : int = Timesteps(__lowerCAmelCase , __lowerCAmelCase , 0 )
_lowerCamelCase : str = TimestepEmbedding(__lowerCAmelCase , __lowerCAmelCase , out_dim=__lowerCAmelCase , act_fn=__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = nn.Linear(__lowerCAmelCase , __lowerCAmelCase )
if embedding_proj_norm_type is None:
_lowerCamelCase : Dict = None
elif embedding_proj_norm_type == "layer":
_lowerCamelCase : Any = nn.LayerNorm(__lowerCAmelCase )
else:
raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}''' )
_lowerCamelCase : List[str] = nn.Linear(__lowerCAmelCase , __lowerCAmelCase )
if encoder_hid_proj_type is None:
_lowerCamelCase : Any = None
elif encoder_hid_proj_type == "linear":
_lowerCamelCase : List[str] = nn.Linear(__lowerCAmelCase , __lowerCAmelCase )
else:
raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}''' )
_lowerCamelCase : Optional[int] = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , __lowerCAmelCase ) )
if added_emb_type == "prd":
_lowerCamelCase : Optional[Any] = nn.Parameter(torch.zeros(1 , 1 , __lowerCAmelCase ) )
elif added_emb_type is None:
_lowerCamelCase : Union[str, Any] = None
else:
raise ValueError(
f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.''' )
_lowerCamelCase : Tuple = nn.ModuleList(
[
BasicTransformerBlock(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , dropout=__lowerCAmelCase , activation_fn='''gelu''' , attention_bias=__lowerCAmelCase , )
for d in range(__lowerCAmelCase )
] )
if norm_in_type == "layer":
_lowerCamelCase : Any = nn.LayerNorm(__lowerCAmelCase )
elif norm_in_type is None:
_lowerCamelCase : Any = None
else:
raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.''' )
_lowerCamelCase : str = nn.LayerNorm(__lowerCAmelCase )
_lowerCamelCase : List[Any] = nn.Linear(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : Optional[int] = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -1_0_0_0_0.0 )
causal_attention_mask.triu_(1 )
_lowerCamelCase : Dict = causal_attention_mask[None, ...]
self.register_buffer('''causal_attention_mask''' , __lowerCAmelCase , persistent=__lowerCAmelCase )
_lowerCamelCase : int = nn.Parameter(torch.zeros(1 , __lowerCAmelCase ) )
_lowerCamelCase : Tuple = nn.Parameter(torch.zeros(1 , __lowerCAmelCase ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
_lowerCamelCase : Any = {}
def fn_recursive_add_processors(__lowerCAmelCase : str , __lowerCAmelCase : torch.nn.Module , __lowerCAmelCase : Dict[str, AttentionProcessor] ):
if hasattr(__lowerCAmelCase , '''set_processor''' ):
_lowerCamelCase : str = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f'''{name}.{sub_name}''' , __lowerCAmelCase , __lowerCAmelCase )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return processors
def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : Union[AttentionProcessor, Dict[str, AttentionProcessor]] ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = len(self.attn_processors.keys() )
if isinstance(__lowerCAmelCase , __lowerCAmelCase ) and len(__lowerCAmelCase ) != count:
raise ValueError(
f'''A dict of processors was passed, but the number of processors {len(__lowerCAmelCase )} does not match the'''
f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''' )
def fn_recursive_attn_processor(__lowerCAmelCase : str , __lowerCAmelCase : torch.nn.Module , __lowerCAmelCase : Tuple ):
if hasattr(__lowerCAmelCase , '''set_processor''' ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
module.set_processor(__lowerCAmelCase )
else:
module.set_processor(processor.pop(f'''{name}.processor''' ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f'''{name}.{sub_name}''' , __lowerCAmelCase , __lowerCAmelCase )
for name, module in self.named_children():
fn_recursive_attn_processor(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
self.set_attn_processor(AttnProcessor() )
def SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[torch.Tensor, float, int] , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : Optional[torch.FloatTensor] = None , __lowerCAmelCase : Optional[torch.BoolTensor] = None , __lowerCAmelCase : bool = True , ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = hidden_states.shape[0]
_lowerCamelCase : Union[str, Any] = timestep
if not torch.is_tensor(__lowerCAmelCase ):
_lowerCamelCase : Dict = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(__lowerCAmelCase ) and len(timesteps.shape ) == 0:
_lowerCamelCase : str = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
_lowerCamelCase : Tuple = timesteps * torch.ones(__lowerCAmelCase , dtype=timesteps.dtype , device=timesteps.device )
_lowerCamelCase : Tuple = self.time_proj(__lowerCAmelCase )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
_lowerCamelCase : Union[str, Any] = timesteps_projected.to(dtype=self.dtype )
_lowerCamelCase : Any = self.time_embedding(__lowerCAmelCase )
if self.embedding_proj_norm is not None:
_lowerCamelCase : str = self.embedding_proj_norm(__lowerCAmelCase )
_lowerCamelCase : Optional[Any] = self.embedding_proj(__lowerCAmelCase )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
_lowerCamelCase : Optional[Any] = self.encoder_hidden_states_proj(__lowerCAmelCase )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set''' )
_lowerCamelCase : str = self.proj_in(__lowerCAmelCase )
_lowerCamelCase : Tuple = self.positional_embedding.to(hidden_states.dtype )
_lowerCamelCase : Tuple = []
_lowerCamelCase : Dict = 0
if encoder_hidden_states is not None:
additional_embeds.append(__lowerCAmelCase )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
_lowerCamelCase : str = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
_lowerCamelCase : Dict = hidden_states[:, None, :]
_lowerCamelCase : int = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
_lowerCamelCase : int = self.prd_embedding.to(hidden_states.dtype ).expand(__lowerCAmelCase , -1 , -1 )
additional_embeds.append(__lowerCAmelCase )
_lowerCamelCase : str = torch.cat(
__lowerCAmelCase , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
_lowerCamelCase : Dict = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
_lowerCamelCase : List[Any] = F.pad(
__lowerCAmelCase , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
_lowerCamelCase : Any = hidden_states + positional_embeddings
if attention_mask is not None:
_lowerCamelCase : Union[str, Any] = (1 - attention_mask.to(hidden_states.dtype )) * -1_0_0_0_0.0
_lowerCamelCase : Tuple = F.pad(__lowerCAmelCase , (0, self.additional_embeddings) , value=0.0 )
_lowerCamelCase : Any = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
_lowerCamelCase : int = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
_lowerCamelCase : List[str] = self.norm_in(__lowerCAmelCase )
for block in self.transformer_blocks:
_lowerCamelCase : List[str] = block(__lowerCAmelCase , attention_mask=__lowerCAmelCase )
_lowerCamelCase : Optional[Any] = self.norm_out(__lowerCAmelCase )
if self.prd_embedding is not None:
_lowerCamelCase : Any = hidden_states[:, -1]
else:
_lowerCamelCase : Dict = hidden_states[:, additional_embeddings_len:]
_lowerCamelCase : str = self.proj_to_clip_embeddings(__lowerCAmelCase )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : Dict = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 350
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"""simple docstring"""
def snake_case_ ( A_ : float ):
'''simple docstring'''
if edge <= 0 or not isinstance(A_, A_ ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def snake_case_ ( A_ : float ):
'''simple docstring'''
if edge <= 0 or not isinstance(A_, A_ ):
raise ValueError('''Length must be a positive.''' )
return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)
if __name__ == "__main__":
import doctest
doctest.testmod()
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def __lowercase ( a__ ) -> Any:
__SCREAMING_SNAKE_CASE = 0
while len(lowerCamelCase_ ) > 1:
__SCREAMING_SNAKE_CASE = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
__SCREAMING_SNAKE_CASE = files.index(min(lowerCamelCase_ ) )
temp += files[min_index]
files.pop(lowerCamelCase_ )
files.append(lowerCamelCase_ )
optimal_merge_cost += temp
return optimal_merge_cost
if __name__ == "__main__":
import doctest
doctest.testmod()
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'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''git_vision_model'''
def __init__( self : int , lowerCamelCase_ : Dict=7_68 , lowerCamelCase_ : Tuple=30_72 , lowerCamelCase_ : List[Any]=12 , lowerCamelCase_ : Tuple=12 , lowerCamelCase_ : int=3 , lowerCamelCase_ : List[str]=2_24 , lowerCamelCase_ : Optional[Any]=16 , lowerCamelCase_ : Optional[Any]="quick_gelu" , lowerCamelCase_ : List[Any]=1e-5 , lowerCamelCase_ : List[Any]=0.0 , lowerCamelCase_ : Optional[Any]=0.02 , **lowerCamelCase_ : str , ):
'''simple docstring'''
super().__init__(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : List[str] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : int = num_channels
SCREAMING_SNAKE_CASE : Optional[Any] = patch_size
SCREAMING_SNAKE_CASE : Optional[int] = image_size
SCREAMING_SNAKE_CASE : List[str] = initializer_range
SCREAMING_SNAKE_CASE : str = attention_dropout
SCREAMING_SNAKE_CASE : Any = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = hidden_act
@classmethod
def lowerCamelCase_ ( cls : List[str] , lowerCamelCase_ : Union[str, os.PathLike] , **lowerCamelCase_ : int ):
'''simple docstring'''
cls._set_token_in_kwargs(lowerCamelCase_ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Any = cls.get_config_dict(lowerCamelCase_ , **lowerCamelCase_ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("""model_type""" ) == "git":
SCREAMING_SNAKE_CASE : Optional[Any] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(lowerCamelCase_ , **lowerCamelCase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''git'''
def __init__( self : List[str] , lowerCamelCase_ : Dict=None , lowerCamelCase_ : Tuple=3_05_22 , lowerCamelCase_ : Optional[Any]=7_68 , lowerCamelCase_ : Any=6 , lowerCamelCase_ : List[str]=12 , lowerCamelCase_ : List[str]=30_72 , lowerCamelCase_ : Union[str, Any]="gelu" , lowerCamelCase_ : List[str]=0.1 , lowerCamelCase_ : Dict=0.1 , lowerCamelCase_ : Optional[int]=10_24 , lowerCamelCase_ : int=0.02 , lowerCamelCase_ : Optional[int]=1e-12 , lowerCamelCase_ : Union[str, Any]=0 , lowerCamelCase_ : Optional[Any]="absolute" , lowerCamelCase_ : Dict=True , lowerCamelCase_ : Optional[Any]=False , lowerCamelCase_ : Optional[int]=1_01 , lowerCamelCase_ : Optional[Any]=1_02 , lowerCamelCase_ : List[str]=None , **lowerCamelCase_ : str , ):
'''simple docstring'''
super().__init__(bos_token_id=lowerCamelCase_ , eos_token_id=lowerCamelCase_ , pad_token_id=lowerCamelCase_ , **lowerCamelCase_ )
if vision_config is None:
SCREAMING_SNAKE_CASE : Any = {}
logger.info("""vision_config is None. initializing the GitVisionConfig with default values.""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = GitVisionConfig(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = vocab_size
SCREAMING_SNAKE_CASE : Tuple = hidden_size
SCREAMING_SNAKE_CASE : int = num_hidden_layers
SCREAMING_SNAKE_CASE : List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : str = hidden_act
SCREAMING_SNAKE_CASE : Dict = intermediate_size
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : List[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : Dict = initializer_range
SCREAMING_SNAKE_CASE : Optional[int] = layer_norm_eps
SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
SCREAMING_SNAKE_CASE : Union[str, Any] = use_cache
SCREAMING_SNAKE_CASE : List[str] = tie_word_embeddings
SCREAMING_SNAKE_CASE : int = num_image_with_embedding
SCREAMING_SNAKE_CASE : Optional[Any] = bos_token_id
SCREAMING_SNAKE_CASE : str = eos_token_id
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE : Tuple = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE : Any = self.__class__.model_type
return output
| 323
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|
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase_ ( lowerCAmelCase: Optional[int] , lowerCAmelCase: List[str] , lowerCAmelCase: Dict )-> Optional[int]:
return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]
def lowerCamelCase_ ( lowerCAmelCase: Optional[int] , lowerCAmelCase: Optional[Any] , lowerCAmelCase: int , lowerCAmelCase: int="attention" )-> Optional[Any]:
_snake_case : str = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :] )
_snake_case : Any = k_tmp.reshape(k_tmp.shape[0] , k_tmp.shape[1] * k_tmp.shape[2] )
_snake_case : List[str] = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :] )
_snake_case : Union[str, Any] = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] , o_tmp.shape[2] )
_snake_case : Union[str, Any] = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :] )
_snake_case : int = q_tmp.reshape(q_tmp.shape[0] , q_tmp.shape[1] * q_tmp.shape[2] )
_snake_case : Union[str, Any] = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :] )
_snake_case : Any = v_tmp.reshape(v_tmp.shape[0] , v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def lowerCamelCase_ ( lowerCAmelCase: Union[str, Any] , lowerCAmelCase: Tuple , lowerCAmelCase: Optional[int] , lowerCAmelCase: Union[str, Any]=False )-> Dict:
if split_mlp_wi:
_snake_case : Optional[Any] = params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
_snake_case : int = params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
_snake_case : int = (wi_a, wi_a)
else:
_snake_case : Any = params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]
_snake_case : Dict = params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
return wi, wo
def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: Tuple , lowerCAmelCase: Tuple , lowerCAmelCase: Optional[int] )-> Optional[Any]:
return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]
def lowerCamelCase_ ( lowerCAmelCase: dict , *, lowerCAmelCase: int , lowerCAmelCase: bool , lowerCAmelCase: bool = False )-> Union[str, Any]:
_snake_case : Optional[int] = traverse_util.flatten_dict(variables['target'] )
_snake_case : int = {'/'.join(lowerCAmelCase ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
_snake_case : List[Any] = 'encoder/encoder/mlp/wi_0/kernel' in old
print('Split MLP:' , lowerCAmelCase )
_snake_case : Union[str, Any] = collections.OrderedDict()
# Shared embeddings.
_snake_case : List[str] = old['token_embedder/embedding']
# Encoder.
for i in range(lowerCAmelCase ):
# Block i, layer 0 (Self Attention).
_snake_case : Optional[Any] = tax_layer_norm_lookup(lowerCAmelCase , lowerCAmelCase , 'encoder' , 'pre_attention_layer_norm' )
_snake_case : Dict = tax_attention_lookup(lowerCAmelCase , lowerCAmelCase , 'encoder' , 'attention' )
_snake_case : Optional[Any] = layer_norm
_snake_case : List[Any] = k.T
_snake_case : Optional[Any] = o.T
_snake_case : Tuple = q.T
_snake_case : List[Any] = v.T
# Block i, layer 1 (MLP).
_snake_case : Tuple = tax_layer_norm_lookup(lowerCAmelCase , lowerCAmelCase , 'encoder' , 'pre_mlp_layer_norm' )
_snake_case : Any = tax_mlp_lookup(lowerCAmelCase , lowerCAmelCase , 'encoder' , lowerCAmelCase )
_snake_case : Dict = layer_norm
if split_mlp_wi:
_snake_case : Union[str, Any] = wi[0].T
_snake_case : Union[str, Any] = wi[1].T
else:
_snake_case : List[str] = wi.T
_snake_case : List[Any] = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
_snake_case : int = tax_relpos_bias_lookup(
lowerCAmelCase , lowerCAmelCase , 'encoder' ).T
_snake_case : List[str] = old['encoder/encoder_norm/scale']
if not scalable_attention:
_snake_case : Optional[Any] = tax_relpos_bias_lookup(
lowerCAmelCase , 0 , 'encoder' ).T
_snake_case : int = tax_relpos_bias_lookup(
lowerCAmelCase , 0 , 'decoder' ).T
if not is_encoder_only:
# Decoder.
for i in range(lowerCAmelCase ):
# Block i, layer 0 (Self Attention).
_snake_case : Optional[Any] = tax_layer_norm_lookup(lowerCAmelCase , lowerCAmelCase , 'decoder' , 'pre_self_attention_layer_norm' )
_snake_case : Tuple = tax_attention_lookup(lowerCAmelCase , lowerCAmelCase , 'decoder' , 'self_attention' )
_snake_case : Optional[Any] = layer_norm
_snake_case : str = k.T
_snake_case : List[str] = o.T
_snake_case : int = q.T
_snake_case : Union[str, Any] = v.T
# Block i, layer 1 (Cross Attention).
_snake_case : Optional[Any] = tax_layer_norm_lookup(lowerCAmelCase , lowerCAmelCase , 'decoder' , 'pre_cross_attention_layer_norm' )
_snake_case : Optional[int] = tax_attention_lookup(lowerCAmelCase , lowerCAmelCase , 'decoder' , 'encoder_decoder_attention' )
_snake_case : Dict = layer_norm
_snake_case : Optional[Any] = k.T
_snake_case : List[Any] = o.T
_snake_case : List[str] = q.T
_snake_case : str = v.T
# Block i, layer 2 (MLP).
_snake_case : Optional[int] = tax_layer_norm_lookup(lowerCAmelCase , lowerCAmelCase , 'decoder' , 'pre_mlp_layer_norm' )
_snake_case : Dict = tax_mlp_lookup(lowerCAmelCase , lowerCAmelCase , 'decoder' , lowerCAmelCase )
_snake_case : List[Any] = layer_norm
if split_mlp_wi:
_snake_case : Optional[Any] = wi[0].T
_snake_case : str = wi[1].T
else:
_snake_case : Optional[int] = wi.T
_snake_case : Any = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
_snake_case : Optional[Any] = tax_relpos_bias_lookup(lowerCAmelCase , lowerCAmelCase , 'decoder' ).T
_snake_case : List[Any] = old['decoder/decoder_norm/scale']
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
_snake_case : List[str] = old['decoder/logits_dense/kernel'].T
return new
def lowerCamelCase_ ( lowerCAmelCase: Tuple , lowerCAmelCase: bool )-> Optional[int]:
_snake_case : Optional[int] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
_snake_case : Tuple = state_dict['shared.weight']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
_snake_case : int = state_dict['shared.weight']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('Using shared word embeddings as lm_head.' )
_snake_case : List[Any] = state_dict['shared.weight']
return state_dict
def lowerCamelCase_ ( lowerCAmelCase: Any , lowerCAmelCase: Optional[int] , lowerCAmelCase: str , lowerCAmelCase: Optional[Any] , lowerCAmelCase: Optional[int] )-> List[str]:
_snake_case : int = checkpoints.load_tax_checkpoint(lowerCAmelCase )
_snake_case : int = convert_tax_to_pytorch(
lowerCAmelCase , num_layers=config.num_layers , is_encoder_only=lowerCAmelCase , scalable_attention=lowerCAmelCase )
_snake_case : Tuple = make_state_dict(lowerCAmelCase , lowerCAmelCase )
model.load_state_dict(lowerCAmelCase , strict=lowerCAmelCase )
def lowerCamelCase_ ( lowerCAmelCase: Tuple , lowerCAmelCase: Optional[Any] , lowerCAmelCase: int , lowerCAmelCase: bool = False , lowerCAmelCase: bool = False , )-> Union[str, Any]:
_snake_case : Dict = MTaConfig.from_json_file(lowerCAmelCase )
print(F"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
_snake_case : Dict = UMTaEncoderModel(lowerCAmelCase )
else:
_snake_case : Optional[Any] = UMTaForConditionalGeneration(lowerCAmelCase )
# Load weights from tf checkpoint
load_tax_weights_in_ta(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowerCAmelCase )
# Verify that we can load the checkpoint.
model.from_pretrained(lowerCAmelCase )
print('Done' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
# Required parameters
parser.add_argument(
"""--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
)
parser.add_argument(
"""--scalable_attention""",
action="""store_true""",
help="""Whether the model uses scaled attention (umt5 model)""",
default=False,
)
lowerCAmelCase_ = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 371
|
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase_ ( lowerCAmelCase: List[str] , lowerCAmelCase: Dict , lowerCAmelCase: str )-> List[str]:
# Initialise PyTorch model
_snake_case : Optional[Any] = MobileBertConfig.from_json_file(lowerCAmelCase )
print(F"""Building PyTorch model from configuration: {config}""" )
_snake_case : Optional[int] = MobileBertForPreTraining(lowerCAmelCase )
# Load weights from tf checkpoint
_snake_case : Optional[int] = load_tf_weights_in_mobilebert(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCAmelCase )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--mobilebert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained MobileBERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
lowerCAmelCase_ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 260
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|
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
lowerCamelCase : Dict = {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class __lowerCAmelCase (unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ : str = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowerCAmelCase__ : Dict = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
lowerCAmelCase__ : List[str] = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
lowerCAmelCase__ : Union[str, Any] = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def UpperCamelCase__ (self : Optional[int] , UpperCamelCase : str , UpperCamelCase : Optional[Any] , UpperCamelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ = ZeroShotClassificationPipeline(
model=UpperCamelCase , tokenizer=UpperCamelCase , candidate_labels=['''polics''', '''health'''] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def UpperCamelCase__ (self : Any , UpperCamelCase : List[Any] , UpperCamelCase : Optional[int] ):
'''simple docstring'''
lowercase__ = classifier('''Who are you voting for in 2020?''' , candidate_labels='''politics''' )
self.assertEqual(UpperCamelCase , {'''sequence''': ANY(UpperCamelCase ), '''labels''': [ANY(UpperCamelCase )], '''scores''': [ANY(UpperCamelCase )]} )
# No kwarg
lowercase__ = classifier('''Who are you voting for in 2020?''' , ['''politics'''] )
self.assertEqual(UpperCamelCase , {'''sequence''': ANY(UpperCamelCase ), '''labels''': [ANY(UpperCamelCase )], '''scores''': [ANY(UpperCamelCase )]} )
lowercase__ = classifier('''Who are you voting for in 2020?''' , candidate_labels=['''politics'''] )
self.assertEqual(UpperCamelCase , {'''sequence''': ANY(UpperCamelCase ), '''labels''': [ANY(UpperCamelCase )], '''scores''': [ANY(UpperCamelCase )]} )
lowercase__ = classifier('''Who are you voting for in 2020?''' , candidate_labels='''politics, public health''' )
self.assertEqual(
UpperCamelCase , {'''sequence''': ANY(UpperCamelCase ), '''labels''': [ANY(UpperCamelCase ), ANY(UpperCamelCase )], '''scores''': [ANY(UpperCamelCase ), ANY(UpperCamelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['''scores'''] ) ) , 1.0 )
lowercase__ = classifier('''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health'''] )
self.assertEqual(
UpperCamelCase , {'''sequence''': ANY(UpperCamelCase ), '''labels''': [ANY(UpperCamelCase ), ANY(UpperCamelCase )], '''scores''': [ANY(UpperCamelCase ), ANY(UpperCamelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['''scores'''] ) ) , 1.0 )
lowercase__ = classifier(
'''Who are you voting for in 2020?''' , candidate_labels='''politics''' , hypothesis_template='''This text is about {}''' )
self.assertEqual(UpperCamelCase , {'''sequence''': ANY(UpperCamelCase ), '''labels''': [ANY(UpperCamelCase )], '''scores''': [ANY(UpperCamelCase )]} )
# https://github.com/huggingface/transformers/issues/13846
lowercase__ = classifier(['''I am happy'''] , ['''positive''', '''negative'''] )
self.assertEqual(
UpperCamelCase , [
{'''sequence''': ANY(UpperCamelCase ), '''labels''': [ANY(UpperCamelCase ), ANY(UpperCamelCase )], '''scores''': [ANY(UpperCamelCase ), ANY(UpperCamelCase )]}
for i in range(1 )
] , )
lowercase__ = classifier(['''I am happy''', '''I am sad'''] , ['''positive''', '''negative'''] )
self.assertEqual(
UpperCamelCase , [
{'''sequence''': ANY(UpperCamelCase ), '''labels''': [ANY(UpperCamelCase ), ANY(UpperCamelCase )], '''scores''': [ANY(UpperCamelCase ), ANY(UpperCamelCase )]}
for i in range(2 )
] , )
with self.assertRaises(UpperCamelCase ):
classifier('''''' , candidate_labels='''politics''' )
with self.assertRaises(UpperCamelCase ):
classifier(UpperCamelCase , candidate_labels='''politics''' )
with self.assertRaises(UpperCamelCase ):
classifier('''Who are you voting for in 2020?''' , candidate_labels='''''' )
with self.assertRaises(UpperCamelCase ):
classifier('''Who are you voting for in 2020?''' , candidate_labels=UpperCamelCase )
with self.assertRaises(UpperCamelCase ):
classifier(
'''Who are you voting for in 2020?''' , candidate_labels='''politics''' , hypothesis_template='''Not formatting template''' , )
with self.assertRaises(UpperCamelCase ):
classifier(
'''Who are you voting for in 2020?''' , candidate_labels='''politics''' , hypothesis_template=UpperCamelCase , )
self.run_entailment_id(UpperCamelCase )
def UpperCamelCase__ (self : List[str] , UpperCamelCase : Pipeline ):
'''simple docstring'''
lowercase__ = zero_shot_classifier.model.config
lowercase__ = config.labelaid
lowercase__ = zero_shot_classifier.entailment_id
lowercase__ = {'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
lowercase__ = {'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
lowercase__ = {'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
lowercase__ = {'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
lowercase__ = original_labelaid
self.assertEqual(UpperCamelCase , zero_shot_classifier.entailment_id )
@require_torch
def UpperCamelCase__ (self : Tuple ):
'''simple docstring'''
lowercase__ = pipeline(
'''zero-shot-classification''' , model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''' , framework='''pt''' , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
'''Who are you voting for in 2020?''' * 100 , candidate_labels=['''politics''', '''public health''', '''science'''] )
@require_torch
def UpperCamelCase__ (self : List[str] ):
'''simple docstring'''
lowercase__ = pipeline(
'''zero-shot-classification''' , model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''' , framework='''pt''' , )
lowercase__ = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(UpperCamelCase ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''science''', '''public health''', '''politics'''],
'''scores''': [0.3_33, 0.3_33, 0.3_33],
} , )
@require_tf
def UpperCamelCase__ (self : Optional[int] ):
'''simple docstring'''
lowercase__ = pipeline(
'''zero-shot-classification''' , model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''' , framework='''tf''' , )
lowercase__ = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(UpperCamelCase ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''science''', '''public health''', '''politics'''],
'''scores''': [0.3_33, 0.3_33, 0.3_33],
} , )
@slow
@require_torch
def UpperCamelCase__ (self : Tuple ):
'''simple docstring'''
lowercase__ = pipeline('''zero-shot-classification''' , model='''roberta-large-mnli''' , framework='''pt''' )
lowercase__ = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(UpperCamelCase ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''politics''', '''public health''', '''science'''],
'''scores''': [0.9_76, 0.0_15, 0.0_09],
} , )
lowercase__ = zero_shot_classifier(
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
''' English-to-German translation task, improving over the existing best results, including ensembles by'''
''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
''' large and limited training data.''' , candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision'''] , multi_label=UpperCamelCase , )
self.assertEqual(
nested_simplify(UpperCamelCase ) , {
'''sequence''': (
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
''' networks in an encoder-decoder configuration. The best performing models also connect the'''
''' encoder and decoder through an attention mechanism. We propose a new simple network'''
''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
''' superior in quality while being more parallelizable and requiring significantly less time to'''
''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
''' other tasks by applying it successfully to English constituency parsing both with large and'''
''' limited training data.'''
),
'''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
'''scores''': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
} , )
@slow
@require_tf
def UpperCamelCase__ (self : Tuple ):
'''simple docstring'''
lowercase__ = pipeline('''zero-shot-classification''' , model='''roberta-large-mnli''' , framework='''tf''' )
lowercase__ = zero_shot_classifier(
'''Who are you voting for in 2020?''' , candidate_labels=['''politics''', '''public health''', '''science'''] )
self.assertEqual(
nested_simplify(UpperCamelCase ) , {
'''sequence''': '''Who are you voting for in 2020?''',
'''labels''': ['''politics''', '''public health''', '''science'''],
'''scores''': [0.9_76, 0.0_15, 0.0_09],
} , )
lowercase__ = zero_shot_classifier(
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
''' English-to-German translation task, improving over the existing best results, including ensembles by'''
''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
''' large and limited training data.''' , candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision'''] , multi_label=UpperCamelCase , )
self.assertEqual(
nested_simplify(UpperCamelCase ) , {
'''sequence''': (
'''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
''' networks in an encoder-decoder configuration. The best performing models also connect the'''
''' encoder and decoder through an attention mechanism. We propose a new simple network'''
''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
''' superior in quality while being more parallelizable and requiring significantly less time to'''
''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
''' other tasks by applying it successfully to English constituency parsing both with large and'''
''' limited training data.'''
),
'''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
'''scores''': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
} , )
| 2
|
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (A , A ) -> list[list[int]]:
"""simple docstring"""
lowercase__ = []
create_all_state(1 , A , A , [] , A )
return result
def _SCREAMING_SNAKE_CASE (A , A , A , A , A , ) -> None:
"""simple docstring"""
if level == 0:
total_list.append(current_list[:] )
return
for i in range(A , total_number - level + 2 ):
current_list.append(A )
create_all_state(i + 1 , A , level - 1 , A , A )
current_list.pop()
def _SCREAMING_SNAKE_CASE (A ) -> None:
"""simple docstring"""
for i in total_list:
print(*A )
if __name__ == "__main__":
lowerCamelCase : Tuple = 4
lowerCamelCase : Union[str, Any] = 2
lowerCamelCase : Dict = generate_all_combinations(n, k)
print_all_state(total_list)
| 2
| 1
|
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
lowerCamelCase : Tuple = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowerCamelCase : Any = 10
lowerCamelCase : Union[str, Any] = 256
def _SCREAMING_SNAKE_CASE ( lowercase : List[str] ):
'''simple docstring'''
if len(lowercase ) < MIN_NUM_TOKENS:
return None
lowerCamelCase_ = MinHash(num_perm=lowercase )
for token in set(lowercase ):
min_hash.update(token.encode() )
return min_hash
def _SCREAMING_SNAKE_CASE ( lowercase : str ):
'''simple docstring'''
return {t for t in NON_ALPHA.split(lowercase ) if len(t.strip() ) > 0}
class A:
'''simple docstring'''
def __init__( self : Optional[int] , *,
A_ : float = 0.85 , ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ = duplication_jaccard_threshold
lowerCamelCase_ = NUM_PERM
lowerCamelCase_ = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
lowerCamelCase_ = defaultdict(A_ )
def a__ ( self : Optional[int] , A_ : Tuple , A_ : MinHash ) -> None:
"""simple docstring"""
lowerCamelCase_ = self._index.query(A_ )
if code_key in self._index.keys:
print(f"""Duplicate key {code_key}""" )
return
self._index.insert(A_ , A_ )
if len(A_ ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(A_ )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(A_ )
def a__ ( self : Union[str, Any] ) -> List[List[Dict]]:
"""simple docstring"""
lowerCamelCase_ = []
for base, duplicates in self._duplicate_clusters.items():
lowerCamelCase_ = [base] + list(A_ )
# reformat the cluster to be a list of dict
lowerCamelCase_ = [{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
duplicate_clusters.append(A_ )
return duplicate_clusters
def a__ ( self : Optional[Any] , A_ : Optional[Any] ) -> None:
"""simple docstring"""
lowerCamelCase_ = self.get_duplicate_clusters()
with open(A_ , 'w' ) as f:
json.dump(A_ , A_ )
def _SCREAMING_SNAKE_CASE ( lowercase : Dict ):
'''simple docstring'''
lowerCamelCase_ , lowerCamelCase_ = element
lowerCamelCase_ = get_min_hash([t for t in NON_ALPHA.split(data['content'] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _SCREAMING_SNAKE_CASE ( lowercase : Type[Dataset] ):
'''simple docstring'''
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(lowercase , max_queue_size=1_00_00 ) , chunksize=1_00 , ):
if data is not None:
yield data
def _SCREAMING_SNAKE_CASE ( lowercase : Type[Dataset] , lowercase : float ):
'''simple docstring'''
lowerCamelCase_ = DuplicationIndex(duplication_jaccard_threshold=lowercase )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowercase ) ) , max_queue_size=1_00 ) ):
di.add(lowercase , lowercase )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _SCREAMING_SNAKE_CASE ( lowercase : str , lowercase : str ):
'''simple docstring'''
lowerCamelCase_ = get_tokens(lowercase )
lowerCamelCase_ = get_tokens(lowercase )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
lowerCamelCase : Optional[int] = None
def _SCREAMING_SNAKE_CASE ( lowercase : str , lowercase : Optional[int] ):
'''simple docstring'''
lowerCamelCase_ = []
for elementa in cluster:
lowerCamelCase_ = _shared_dataset[elementa['base_index']]['content']
for elementa in extremes:
lowerCamelCase_ = _shared_dataset[elementa['base_index']]['content']
if jaccard_similarity(lowercase , lowercase ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
lowerCamelCase_ = 1
extremes.append(lowercase )
return extremes
def _SCREAMING_SNAKE_CASE ( lowercase : int , lowercase : int , lowercase : List[str] ):
'''simple docstring'''
global _shared_dataset
lowerCamelCase_ = dataset
lowerCamelCase_ = []
lowerCamelCase_ = partial(_find_cluster_extremes_shared , jaccard_threshold=lowercase )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
lowercase , lowercase , ) , total=len(lowercase ) , ):
extremes_list.append(lowercase )
return extremes_list
def _SCREAMING_SNAKE_CASE ( lowercase : Type[Dataset] , lowercase : float = 0.85 ):
'''simple docstring'''
lowerCamelCase_ = make_duplicate_clusters(lowercase , lowercase )
lowerCamelCase_ = {x['base_index'] for cluster in duplicate_clusters for x in cluster}
lowerCamelCase_ = {}
lowerCamelCase_ = find_extremes(lowercase , lowercase , lowercase )
for extremes in extremes_clusters:
for element in extremes:
lowerCamelCase_ = element
lowerCamelCase_ = duplicate_indices - set(extreme_dict.keys() )
lowerCamelCase_ = dataset.filter(lambda lowercase , lowercase : idx not in remove_indices , with_indices=lowercase )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
lowerCamelCase_ = element['base_index'] in extreme_dict
if element["is_extreme"]:
lowerCamelCase_ = extreme_dict[element['base_index']]['copies']
print(f"""Original dataset size: {len(lowercase )}""" )
print(f"""Number of duplicate clusters: {len(lowercase )}""" )
print(f"""Files in duplicate cluster: {len(lowercase )}""" )
print(f"""Unique files in duplicate cluster: {len(lowercase )}""" )
print(f"""Filtered dataset size: {len(lowercase )}""" )
return ds_filter, duplicate_clusters
| 208
|
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( lowercase : dict , lowercase : str ):
'''simple docstring'''
lowerCamelCase_ , lowerCamelCase_ = set(lowercase ), [start]
while stack:
lowerCamelCase_ = stack.pop()
explored.add(lowercase )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(lowercase )
return explored
lowerCamelCase : int = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))
| 208
| 1
|
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A__ ( UpperCamelCase__ , unittest.TestCase ):
lowercase = LayoutLMTokenizer
lowercase = LayoutLMTokenizerFast
lowercase = True
lowercase = True
def snake_case_ ( self ) -> Any:
'''simple docstring'''
super().setUp()
A_ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
A_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def snake_case_ ( self , **UpperCamelCase__ ) -> int:
'''simple docstring'''
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
A_ = """UNwant\u00E9d,running"""
A_ = """unwanted, running"""
return input_text, output_text
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
A_ = self.tokenizer_class(self.vocab_file )
A_ = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(UpperCamelCase__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , [7, 4, 5, 10, 8, 9] )
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
| 162
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ) -> Union[str, Any]:
for attribute in key.split(""".""" ):
snake_case : Optional[Any] = getattr(lowercase ,lowercase )
if weight_type is not None:
snake_case : Any = getattr(lowercase ,lowercase ).shape
else:
snake_case : List[str] = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
snake_case : str = value
elif weight_type == "weight_g":
snake_case : Optional[int] = value
elif weight_type == "weight_v":
snake_case : List[str] = value
elif weight_type == "bias":
snake_case : int = value
else:
snake_case : str = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> List[Any]:
snake_case : Optional[Any] = []
snake_case : Optional[Any] = fairseq_model.state_dict()
snake_case : Tuple = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
snake_case : Optional[Any] = False
if "conv_layers" in name:
load_conv_layer(
lowercase ,lowercase ,lowercase ,lowercase ,hf_model.config.feat_extract_norm == """group""" ,)
snake_case : Any = True
else:
for key, mapped_key in MAPPING.items():
snake_case : Optional[int] = """hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or (key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0] and not is_finetuned):
snake_case : Union[str, Any] = True
if "*" in mapped_key:
snake_case : Dict = name.split(lowercase )[0].split(""".""" )[-2]
snake_case : str = mapped_key.replace("""*""" ,lowercase )
if "weight_g" in name:
snake_case : int = """weight_g"""
elif "weight_v" in name:
snake_case : Optional[int] = """weight_v"""
elif "weight" in name:
snake_case : Tuple = """weight"""
elif "bias" in name:
snake_case : List[Any] = """bias"""
else:
snake_case : List[str] = None
set_recursively(lowercase ,lowercase ,lowercase ,lowercase ,lowercase )
continue
if not is_used:
unused_weights.append(lowercase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ) -> Dict:
snake_case : str = full_name.split("""conv_layers.""" )[-1]
snake_case : Dict = name.split(""".""" )
snake_case : Any = int(items[0] )
snake_case : Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
snake_case : int = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
snake_case : List[Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
snake_case : Optional[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
snake_case : Tuple = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(lowercase )
@torch.no_grad()
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase=None ,lowercase=None ,lowercase=True ) -> Union[str, Any]:
if config_path is not None:
snake_case : Optional[int] = HubertConfig.from_pretrained(lowercase )
else:
snake_case : Tuple = HubertConfig()
if is_finetuned:
if dict_path:
snake_case : List[str] = Dictionary.load(lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case : Optional[int] = target_dict.pad_index
snake_case : Any = target_dict.bos_index
snake_case : Dict = target_dict.eos_index
snake_case : List[str] = len(target_dict.symbols )
snake_case : Union[str, Any] = os.path.join(lowercase ,"""vocab.json""" )
if not os.path.isdir(lowercase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase ) )
return
os.makedirs(lowercase ,exist_ok=lowercase )
with open(lowercase ,"""w""" ,encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices ,lowercase )
snake_case : Union[str, Any] = WavaVecaCTCTokenizer(
lowercase ,unk_token=target_dict.unk_word ,pad_token=target_dict.pad_word ,bos_token=target_dict.bos_word ,eos_token=target_dict.eos_word ,word_delimiter_token="""|""" ,do_lower_case=lowercase ,)
snake_case : Union[str, Any] = True if config.feat_extract_norm == """layer""" else False
snake_case : Union[str, Any] = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=16000 ,padding_value=0 ,do_normalize=lowercase ,return_attention_mask=lowercase ,)
snake_case : Dict = WavaVecaProcessor(feature_extractor=lowercase ,tokenizer=lowercase )
processor.save_pretrained(lowercase )
snake_case : List[Any] = HubertForCTC(lowercase )
else:
snake_case : Any = HubertModel(lowercase )
if is_finetuned:
snake_case , snake_case , snake_case : int = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
snake_case , snake_case , snake_case : Any = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
snake_case : Any = model[0].eval()
recursively_load_weights(lowercase ,lowercase ,lowercase )
hf_wavavec.save_pretrained(lowercase )
if __name__ == "__main__":
lowerCamelCase : Any = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
lowerCamelCase : List[str] = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 124
| 0
|
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
_snake_case = logging.get_logger(__name__)
# General docstring
_snake_case = """ResNetConfig"""
# Base docstring
_snake_case = """microsoft/resnet-50"""
_snake_case = [1, 2048, 7, 7]
# Image classification docstring
_snake_case = """microsoft/resnet-50"""
_snake_case = """tiger cat"""
_snake_case = [
"""microsoft/resnet-50""",
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class lowerCAmelCase ( nn.Module ):
def __init__( self :Optional[int] , _lowercase :int , _lowercase :int , _lowercase :int = 3 , _lowercase :int = 1 , _lowercase :str = "relu" ):
'''simple docstring'''
super().__init__()
lowercase__ = nn.Convad(
_lowercase , _lowercase , kernel_size=_lowercase , stride=_lowercase , padding=kernel_size // 2 , bias=_lowercase )
lowercase__ = nn.BatchNormad(_lowercase )
lowercase__ = ACTaFN[activation] if activation is not None else nn.Identity()
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Tensor ):
'''simple docstring'''
lowercase__ = self.convolution(_lowercase )
lowercase__ = self.normalization(_lowercase )
lowercase__ = self.activation(_lowercase )
return hidden_state
class lowerCAmelCase ( nn.Module ):
def __init__( self :Optional[Any] , _lowercase :ResNetConfig ):
'''simple docstring'''
super().__init__()
lowercase__ = ResNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=7 , stride=2 , activation=config.hidden_act )
lowercase__ = nn.MaxPoolad(kernel_size=3 , stride=2 , padding=1 )
lowercase__ = config.num_channels
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Tensor ):
'''simple docstring'''
lowercase__ = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
lowercase__ = self.embedder(_lowercase )
lowercase__ = self.pooler(_lowercase )
return embedding
class lowerCAmelCase ( nn.Module ):
def __init__( self :List[Any] , _lowercase :int , _lowercase :int , _lowercase :int = 2 ):
'''simple docstring'''
super().__init__()
lowercase__ = nn.Convad(_lowercase , _lowercase , kernel_size=1 , stride=_lowercase , bias=_lowercase )
lowercase__ = nn.BatchNormad(_lowercase )
def UpperCAmelCase ( self :List[str] , _lowercase :Tensor ):
'''simple docstring'''
lowercase__ = self.convolution(_lowercase )
lowercase__ = self.normalization(_lowercase )
return hidden_state
class lowerCAmelCase ( nn.Module ):
def __init__( self :List[str] , _lowercase :int , _lowercase :int , _lowercase :int = 1 , _lowercase :str = "relu" ):
'''simple docstring'''
super().__init__()
lowercase__ = in_channels != out_channels or stride != 1
lowercase__ = (
ResNetShortCut(_lowercase , _lowercase , stride=_lowercase ) if should_apply_shortcut else nn.Identity()
)
lowercase__ = nn.Sequential(
ResNetConvLayer(_lowercase , _lowercase , stride=_lowercase ) , ResNetConvLayer(_lowercase , _lowercase , activation=_lowercase ) , )
lowercase__ = ACTaFN[activation]
def UpperCAmelCase ( self :int , _lowercase :Tuple ):
'''simple docstring'''
lowercase__ = hidden_state
lowercase__ = self.layer(_lowercase )
lowercase__ = self.shortcut(_lowercase )
hidden_state += residual
lowercase__ = self.activation(_lowercase )
return hidden_state
class lowerCAmelCase ( nn.Module ):
def __init__( self :Optional[int] , _lowercase :int , _lowercase :int , _lowercase :int = 1 , _lowercase :str = "relu" , _lowercase :int = 4 ):
'''simple docstring'''
super().__init__()
lowercase__ = in_channels != out_channels or stride != 1
lowercase__ = out_channels // reduction
lowercase__ = (
ResNetShortCut(_lowercase , _lowercase , stride=_lowercase ) if should_apply_shortcut else nn.Identity()
)
lowercase__ = nn.Sequential(
ResNetConvLayer(_lowercase , _lowercase , kernel_size=1 ) , ResNetConvLayer(_lowercase , _lowercase , stride=_lowercase ) , ResNetConvLayer(_lowercase , _lowercase , kernel_size=1 , activation=_lowercase ) , )
lowercase__ = ACTaFN[activation]
def UpperCAmelCase ( self :int , _lowercase :Tuple ):
'''simple docstring'''
lowercase__ = hidden_state
lowercase__ = self.layer(_lowercase )
lowercase__ = self.shortcut(_lowercase )
hidden_state += residual
lowercase__ = self.activation(_lowercase )
return hidden_state
class lowerCAmelCase ( nn.Module ):
def __init__( self :List[str] , _lowercase :ResNetConfig , _lowercase :int , _lowercase :int , _lowercase :int = 2 , _lowercase :int = 2 , ):
'''simple docstring'''
super().__init__()
lowercase__ = ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer
lowercase__ = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(_lowercase , _lowercase , stride=_lowercase , activation=config.hidden_act ) , *[layer(_lowercase , _lowercase , activation=config.hidden_act ) for _ in range(depth - 1 )] , )
def UpperCAmelCase ( self :int , _lowercase :Tensor ):
'''simple docstring'''
lowercase__ = input
for layer in self.layers:
lowercase__ = layer(_lowercase )
return hidden_state
class lowerCAmelCase ( nn.Module ):
def __init__( self :List[str] , _lowercase :ResNetConfig ):
'''simple docstring'''
super().__init__()
lowercase__ = nn.ModuleList([] )
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
_lowercase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
lowercase__ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(_lowercase , config.depths[1:] ):
self.stages.append(ResNetStage(_lowercase , _lowercase , _lowercase , depth=_lowercase ) )
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Tensor , _lowercase :bool = False , _lowercase :bool = True ):
'''simple docstring'''
lowercase__ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase__ = hidden_states + (hidden_state,)
lowercase__ = stage_module(_lowercase )
if output_hidden_states:
lowercase__ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(
last_hidden_state=_lowercase , hidden_states=_lowercase , )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = ResNetConfig
__lowerCamelCase = 'resnet'
__lowerCamelCase = 'pixel_values'
__lowerCamelCase = True
def UpperCAmelCase ( self :Optional[int] , _lowercase :Optional[Any] ):
'''simple docstring'''
if isinstance(_lowercase , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="fan_out" , nonlinearity="relu" )
elif isinstance(_lowercase , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def UpperCAmelCase ( self :Optional[Any] , _lowercase :List[Any] , _lowercase :str=False ):
'''simple docstring'''
if isinstance(_lowercase , _lowercase ):
lowercase__ = value
_snake_case = R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
_snake_case = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'The bare ResNet model outputting raw features without any specific head on top.' , lowercase_ , )
class lowerCAmelCase ( lowercase_ ):
def __init__( self :List[str] , _lowercase :str ):
'''simple docstring'''
super().__init__(_lowercase )
lowercase__ = config
lowercase__ = ResNetEmbeddings(_lowercase )
lowercase__ = ResNetEncoder(_lowercase )
lowercase__ = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_lowercase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_lowercase , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def UpperCAmelCase ( self :Tuple , _lowercase :Tensor , _lowercase :Optional[bool] = None , _lowercase :Optional[bool] = None ):
'''simple docstring'''
lowercase__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ = self.embedder(_lowercase )
lowercase__ = self.encoder(
_lowercase , output_hidden_states=_lowercase , return_dict=_lowercase )
lowercase__ = encoder_outputs[0]
lowercase__ = self.pooler(_lowercase )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_lowercase , pooler_output=_lowercase , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'\n ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , lowercase_ , )
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Optional[Any] , _lowercase :Tuple ):
'''simple docstring'''
super().__init__(_lowercase )
lowercase__ = config.num_labels
lowercase__ = ResNetModel(_lowercase )
# classification head
lowercase__ = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_lowercase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def UpperCAmelCase ( self :List[str] , _lowercase :Optional[torch.FloatTensor] = None , _lowercase :Optional[torch.LongTensor] = None , _lowercase :Optional[bool] = None , _lowercase :Optional[bool] = None , ):
'''simple docstring'''
lowercase__ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ = self.resnet(_lowercase , output_hidden_states=_lowercase , return_dict=_lowercase )
lowercase__ = outputs.pooler_output if return_dict else outputs[1]
lowercase__ = self.classifier(_lowercase )
lowercase__ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase__ = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase__ = "single_label_classification"
else:
lowercase__ = "multi_label_classification"
if self.config.problem_type == "regression":
lowercase__ = MSELoss()
if self.num_labels == 1:
lowercase__ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowercase__ = loss_fct(_lowercase , _lowercase )
elif self.config.problem_type == "single_label_classification":
lowercase__ = CrossEntropyLoss()
lowercase__ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase__ = BCEWithLogitsLoss()
lowercase__ = loss_fct(_lowercase , _lowercase )
if not return_dict:
lowercase__ = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_lowercase , logits=_lowercase , hidden_states=outputs.hidden_states )
@add_start_docstrings(
'\n ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n ' , lowercase_ , )
class lowerCAmelCase ( lowercase_ , lowercase_ ):
def __init__( self :List[Any] , _lowercase :List[str] ):
'''simple docstring'''
super().__init__(_lowercase )
super()._init_backbone(_lowercase )
lowercase__ = [config.embedding_size] + config.hidden_sizes
lowercase__ = ResNetEmbeddings(_lowercase )
lowercase__ = ResNetEncoder(_lowercase )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_lowercase )
@replace_return_docstrings(output_type=_lowercase , config_class=_CONFIG_FOR_DOC )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Tensor , _lowercase :Optional[bool] = None , _lowercase :Optional[bool] = None ):
'''simple docstring'''
lowercase__ = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ = self.embedder(_lowercase )
lowercase__ = self.encoder(_lowercase , output_hidden_states=_lowercase , return_dict=_lowercase )
lowercase__ = outputs.hidden_states
lowercase__ = ()
for idx, stage in enumerate(self.stage_names ):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
lowercase__ = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=_lowercase , hidden_states=outputs.hidden_states if output_hidden_states else None , attentions=_lowercase , )
| 355
|
from __future__ import annotations
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = []
create_all_state(1 , __magic_name__ , __magic_name__ , [] , __magic_name__ )
return result
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , ):
if level == 0:
total_list.append(current_list[:] )
return
for i in range(__magic_name__ , total_number - level + 2 ):
current_list.append(__magic_name__ )
create_all_state(i + 1 , __magic_name__ , level - 1 , __magic_name__ , __magic_name__ )
current_list.pop()
def _A ( __magic_name__ ):
for i in total_list:
print(*__magic_name__ )
if __name__ == "__main__":
_snake_case = 4
_snake_case = 2
_snake_case = generate_all_combinations(n, k)
print_all_state(total_list)
| 201
| 0
|
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1_337 , num_examples=42 , dataset_name="my_dataset")}),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1_337 , num_examples=42)}),
SplitDict({"train": SplitInfo()}),
] , )
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : SplitDict) -> str:
'''simple docstring'''
__UpperCamelCase : Tuple = split_dict._to_yaml_list()
assert len(_lowerCamelCase) == len(_lowerCamelCase)
__UpperCamelCase : Optional[int] = SplitDict._from_yaml_list(_lowerCamelCase)
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
__UpperCamelCase : Tuple = None
# the split name of split_dict takes over the name of the split info object
__UpperCamelCase : Any = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"split_info" , [SplitInfo(), SplitInfo(dataset_name=_lowerCamelCase), SplitInfo(dataset_name="my_dataset")])
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase : Dict = asdict(SplitDict({"train": split_info}))
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 232
|
from torch import nn
class _lowercase ( nn.Module ):
def __init__( self : Any , snake_case : Dict , snake_case : Union[str, Any] ) -> Dict:
"""simple docstring"""
super().__init__()
UpperCamelCase_ : List[Any] = class_size
UpperCamelCase_ : List[Any] = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
UpperCamelCase_ : int = nn.Linear(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , snake_case : Any ) -> str:
"""simple docstring"""
UpperCamelCase_ : Dict = self.mlp(snake_case )
return logits
| 175
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Dict = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
"Pix2StructVisionConfig",
],
"processing_pix2struct": ["Pix2StructProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Optional[int] = ["Pix2StructImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Any = [
"PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Pix2StructPreTrainedModel",
"Pix2StructForConditionalGeneration",
"Pix2StructVisionModel",
"Pix2StructTextModel",
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
_lowercase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 272
|
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("3.8"):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
_lowercase : List[str] = ""
if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):
class __SCREAMING_SNAKE_CASE ( tr.AbstractTransform ):
'''simple docstring'''
def __init__( self : List[Any], lowerCamelCase : str = " " )-> List[str]:
lowerCamelCase__ : List[str] =sentence_delimiter
def snake_case ( self : Any, lowerCamelCase : str )-> Optional[Any]:
return list(lowerCamelCase )
def snake_case ( self : Optional[Any], lowerCamelCase : List[str] )-> Tuple:
lowerCamelCase__ : Optional[int] =[]
for sent_idx, sentence in enumerate(lowerCamelCase ):
chars.extend(self.process_string(lowerCamelCase ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowerCamelCase ) - 1:
chars.append(self.sentence_delimiter )
return chars
_lowercase : Optional[int] = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
_lowercase : List[str] = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
_lowercase : Dict = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"
_lowercase : List[Any] = "\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"
_lowercase : Dict = "\nComputes CER score of transcribed segments against references.\nArgs:\n references: list of references for each speech input.\n predictions: list of transcribtions to score.\n concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n (float): the character error rate\n\nExamples:\n\n >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n >>> references = [\"this is the reference\", \"there is another one\"]\n >>> cer = datasets.load_metric(\"cer\")\n >>> cer_score = cer.compute(predictions=predictions, references=references)\n >>> print(cer_score)\n 0.34146341463414637\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
'''simple docstring'''
def snake_case ( self : Dict )-> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'''predictions''': datasets.Value('''string''', id='''sequence''' ),
'''references''': datasets.Value('''string''', id='''sequence''' ),
} ), codebase_urls=['''https://github.com/jitsi/jiwer/'''], reference_urls=[
'''https://en.wikipedia.org/wiki/Word_error_rate''',
'''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
], )
def snake_case ( self : Optional[Any], lowerCamelCase : Union[str, Any], lowerCamelCase : List[str], lowerCamelCase : Dict=False )-> List[Any]:
if concatenate_texts:
return jiwer.compute_measures(
lowerCamelCase, lowerCamelCase, truth_transform=lowerCamelCase, hypothesis_transform=lowerCamelCase, )["wer"]
lowerCamelCase__ : Optional[Any] =0
lowerCamelCase__ : Union[str, Any] =0
for prediction, reference in zip(lowerCamelCase, lowerCamelCase ):
lowerCamelCase__ : int =jiwer.compute_measures(
lowerCamelCase, lowerCamelCase, truth_transform=lowerCamelCase, hypothesis_transform=lowerCamelCase, )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 272
| 1
|
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43
|
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__A : int = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : int = 1_6000 ):
'''simple docstring'''
_UpperCAmelCase = int(round(sample_rate * max_length ) )
if len(_SCREAMING_SNAKE_CASE ) <= sample_length:
return wav
_UpperCAmelCase = randint(0 , len(_SCREAMING_SNAKE_CASE ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """Name of a dataset from the datasets package"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the training audio paths and labels."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the validation audio paths and labels."""})
UpperCamelCase__ = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
UpperCamelCase__ = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
UpperCamelCase__ = field(
default="""audio""" , metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""} , )
UpperCamelCase__ = field(
default="""label""" , metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=20 , metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""} , )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(
default="""facebook/wav2vec2-base""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""})
UpperCamelCase__ = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Name or path of preprocessor config."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def lowercase__ ( self : Optional[Any] )->int:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , __UpperCamelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_audio_classification''' , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_SCREAMING_SNAKE_CASE )
transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} '
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
'''Use --overwrite_output_dir to train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset and prepare it for the audio classification task.
_UpperCAmelCase = DatasetDict()
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--label_column_name` to the correct text column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_UpperCAmelCase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_UpperCAmelCase = feature_extractor.model_input_names[0]
def train_transforms(_SCREAMING_SNAKE_CASE : Tuple ):
_UpperCAmelCase = []
for audio in batch[data_args.audio_column_name]:
_UpperCAmelCase = random_subsample(
audio['''array'''] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(_SCREAMING_SNAKE_CASE : Optional[int] ):
_UpperCAmelCase = [audio['''array'''] for audio in batch[data_args.audio_column_name]]
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_UpperCAmelCase = raw_datasets['''train'''].features[data_args.label_column_name].names
_UpperCAmelCase , _UpperCAmelCase = {}, {}
for i, label in enumerate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = label
# Load the accuracy metric from the datasets package
_UpperCAmelCase = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(_SCREAMING_SNAKE_CASE : List[str] ):
_UpperCAmelCase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=_SCREAMING_SNAKE_CASE , references=eval_pred.label_ids )
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(_SCREAMING_SNAKE_CASE ) , labelaid=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , finetuning_task='''audio-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_UpperCAmelCase = (
raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_UpperCAmelCase = (
raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
# Initialize our trainer
_UpperCAmelCase = Trainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , train_dataset=raw_datasets['''train'''] if training_args.do_train else None , eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None , compute_metrics=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_UpperCAmelCase = trainer.evaluate()
trainer.log_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
trainer.save_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
# Write model card and (optionally) push to hub
_UpperCAmelCase = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''audio-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''audio-classification'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_SCREAMING_SNAKE_CASE )
else:
trainer.create_model_card(**_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260
| 0
|
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class a (unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : Optional[int] , lowerCamelCase : Optional[Any] ) -> Optional[Any]:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["bs"] , model_result["ss"] ):
__snake_case : Union[str, Any] = model_result["result"][batch_size][sequence_length]
self.assertIsNotNone(_a )
def __snake_case ( self : List[Any] ) -> str:
__snake_case : Optional[int] = "sshleifer/tiny-gpt2"
__snake_case : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_a , inference=_a , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=_a , multi_process=_a , )
__snake_case : List[str] = TensorFlowBenchmark(_a )
__snake_case : int = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __snake_case ( self : str ) -> Optional[Any]:
__snake_case : Optional[int] = "sgugger/tiny-distilbert-classification"
__snake_case : Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_a , inference=_a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_a , only_pretrain_model=_a , )
__snake_case : Tuple = TensorFlowBenchmark(_a )
__snake_case : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __snake_case ( self : str ) -> Any:
__snake_case : Optional[Any] = "sshleifer/tiny-gpt2"
__snake_case : Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_a , inference=_a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_a , )
__snake_case : List[str] = TensorFlowBenchmark(_a )
__snake_case : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __snake_case ( self : str ) -> str:
__snake_case : Optional[int] = "sshleifer/tiny-gpt2"
__snake_case : Any = AutoConfig.from_pretrained(_a )
__snake_case : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_a , inference=_a , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=_a , multi_process=_a , )
__snake_case : Union[str, Any] = TensorFlowBenchmark(_a , [config] )
__snake_case : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __snake_case ( self : Tuple ) -> Union[str, Any]:
__snake_case : Union[str, Any] = "sshleifer/tiny-gpt2"
__snake_case : Dict = AutoConfig.from_pretrained(_a )
__snake_case : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_a , inference=_a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_a , )
__snake_case : Union[str, Any] = TensorFlowBenchmark(_a , [config] )
__snake_case : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __snake_case ( self : List[str] ) -> Optional[Any]:
__snake_case : Optional[Any] = "sshleifer/tiny-gpt2"
__snake_case : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_a , inference=_a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_a , )
__snake_case : Any = TensorFlowBenchmark(_a )
__snake_case : int = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __snake_case ( self : Dict ) -> Tuple:
__snake_case : Union[str, Any] = "sshleifer/tiny-gpt2"
__snake_case : List[str] = AutoConfig.from_pretrained(_a )
__snake_case : Union[str, Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_a , inference=_a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_a , )
__snake_case : List[Any] = TensorFlowBenchmark(_a , [config] )
__snake_case : int = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __snake_case ( self : Dict ) -> Union[str, Any]:
__snake_case : List[Any] = "patrickvonplaten/t5-tiny-random"
__snake_case : Optional[Any] = AutoConfig.from_pretrained(_a )
__snake_case : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_a , inference=_a , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_a , )
__snake_case : str = TensorFlowBenchmark(_a , configs=[config] )
__snake_case : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU" ) ) == 0 , "Cannot do xla on CPU." )
def __snake_case ( self : Optional[Any] ) -> Dict:
__snake_case : Union[str, Any] = "sshleifer/tiny-gpt2"
__snake_case : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_a , inference=_a , sequence_lengths=[8] , batch_sizes=[1] , use_xla=_a , multi_process=_a , )
__snake_case : List[Any] = TensorFlowBenchmark(_a )
__snake_case : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __snake_case ( self : Tuple ) -> int:
__snake_case : Dict = "sshleifer/tiny-gpt2"
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case : Union[str, Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=_a , save_to_csv=_a , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(_a , "inf_time.csv" ) , inference_memory_csv_file=os.path.join(_a , "inf_mem.csv" ) , env_info_csv_file=os.path.join(_a , "env.csv" ) , multi_process=_a , )
__snake_case : List[str] = TensorFlowBenchmark(_a )
benchmark.run()
self.assertTrue(Path(os.path.join(_a , "inf_time.csv" ) ).exists() )
self.assertTrue(Path(os.path.join(_a , "inf_mem.csv" ) ).exists() )
self.assertTrue(Path(os.path.join(_a , "env.csv" ) ).exists() )
def __snake_case ( self : str ) -> List[Any]:
__snake_case : Optional[Any] = "sshleifer/tiny-gpt2"
def _check_summary_is_not_empty(lowerCamelCase : Tuple ):
self.assertTrue(hasattr(_a , "sequential" ) )
self.assertTrue(hasattr(_a , "cumulative" ) )
self.assertTrue(hasattr(_a , "current" ) )
self.assertTrue(hasattr(_a , "total" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=_a , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(_a , "log.txt" ) , log_print=_a , trace_memory_line_by_line=_a , eager_mode=_a , multi_process=_a , )
__snake_case : Optional[Any] = TensorFlowBenchmark(_a )
__snake_case : List[Any] = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(_a , "log.txt" ) ).exists() )
| 361
|
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
return x if y == 0 else greatest_common_divisor(__lowerCamelCase , x % y )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
return (x * y) // greatest_common_divisor(__lowerCamelCase , __lowerCamelCase )
def lowerCAmelCase_ ( __lowerCamelCase = 2_0 ):
__snake_case : Optional[Any] = 1
for i in range(1 , n + 1 ):
__snake_case : Any = lcm(__lowerCamelCase , __lowerCamelCase )
return g
if __name__ == "__main__":
print(f'''{solution() = }''')
| 134
| 0
|
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class lowerCamelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] , _a : str , _a : Any=7 , _a : Optional[Any]=3 , _a : Any=30 , _a : str=400 , _a : str=True , _a : Any=None , _a : List[str]=True , _a : Tuple=[0.5, 0.5, 0.5] , _a : Union[str, Any]=[0.5, 0.5, 0.5] , _a : Optional[int]=True , _a : List[Any]=1 / 255 , _a : Any=True , ) -> List[str]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__lowerCamelCase : Union[str, Any] = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
__lowerCamelCase : Optional[int] = parent
__lowerCamelCase : int = batch_size
__lowerCamelCase : str = num_channels
__lowerCamelCase : int = min_resolution
__lowerCamelCase : str = max_resolution
__lowerCamelCase : Optional[int] = do_resize
__lowerCamelCase : Tuple = size
__lowerCamelCase : Optional[Any] = do_normalize
__lowerCamelCase : int = image_mean
__lowerCamelCase : Tuple = image_std
__lowerCamelCase : str = do_rescale
__lowerCamelCase : Union[str, Any] = rescale_factor
__lowerCamelCase : List[Any] = do_pad
def _lowercase ( self : Any ) -> int:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def _lowercase ( self : List[str] , _a : int , _a : Any=False ) -> Optional[int]:
if not batched:
__lowerCamelCase : Union[str, Any] = image_inputs[0]
if isinstance(_a , Image.Image ):
__lowerCamelCase ,__lowerCamelCase : Any = image.size
else:
__lowerCamelCase ,__lowerCamelCase : Union[str, Any] = image.shape[1], image.shape[2]
if w < h:
__lowerCamelCase : int = int(self.size['shortest_edge'] * h / w )
__lowerCamelCase : List[str] = self.size['shortest_edge']
elif w > h:
__lowerCamelCase : Optional[Any] = self.size['shortest_edge']
__lowerCamelCase : Optional[int] = int(self.size['shortest_edge'] * w / h )
else:
__lowerCamelCase : Dict = self.size['shortest_edge']
__lowerCamelCase : Union[str, Any] = self.size['shortest_edge']
else:
__lowerCamelCase : Dict = []
for image in image_inputs:
__lowerCamelCase ,__lowerCamelCase : Optional[int] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__lowerCamelCase : Dict = max(_a , key=lambda _a : item[0] )[0]
__lowerCamelCase : Any = max(_a , key=lambda _a : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowerCamelCase_ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
a_ =ConditionalDetrImageProcessor if is_vision_available() else None
def _lowercase ( self : Union[str, Any] ) -> Dict:
__lowerCamelCase : List[str] = ConditionalDetrImageProcessingTester(self )
@property
def _lowercase ( self : str ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def _lowercase ( self : Tuple ) -> str:
__lowerCamelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , 'image_mean' ) )
self.assertTrue(hasattr(_a , 'image_std' ) )
self.assertTrue(hasattr(_a , 'do_normalize' ) )
self.assertTrue(hasattr(_a , 'do_resize' ) )
self.assertTrue(hasattr(_a , 'size' ) )
def _lowercase ( self : Optional[Any] ) -> Union[str, Any]:
__lowerCamelCase : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 1333} )
self.assertEqual(image_processor.do_pad , _a )
__lowerCamelCase : Dict = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_a )
self.assertEqual(image_processor.size , {'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad , _a )
def _lowercase ( self : Tuple ) -> List[Any]:
pass
def _lowercase ( self : str ) -> Any:
# Initialize image_processing
__lowerCamelCase : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCamelCase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
__lowerCamelCase : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__lowerCamelCase ,__lowerCamelCase : int = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowerCamelCase ,__lowerCamelCase : str = self.image_processor_tester.get_expected_values(_a , batched=_a )
__lowerCamelCase : Any = image_processing(_a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _lowercase ( self : List[str] ) -> Dict:
# Initialize image_processing
__lowerCamelCase : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowerCamelCase : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a , np.ndarray )
# Test not batched input
__lowerCamelCase : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__lowerCamelCase ,__lowerCamelCase : List[str] = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowerCamelCase : Optional[Any] = image_processing(_a , return_tensors='pt' ).pixel_values
__lowerCamelCase ,__lowerCamelCase : str = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _lowercase ( self : List[Any] ) -> Any:
# Initialize image_processing
__lowerCamelCase : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowerCamelCase : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a , torch.Tensor )
# Test not batched input
__lowerCamelCase : Optional[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__lowerCamelCase ,__lowerCamelCase : Tuple = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowerCamelCase : Dict = image_processing(_a , return_tensors='pt' ).pixel_values
__lowerCamelCase ,__lowerCamelCase : Optional[Any] = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _lowercase ( self : Optional[Any] ) -> Optional[Any]:
# prepare image and target
__lowerCamelCase : Optional[int] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
__lowerCamelCase : int = json.loads(f.read() )
__lowerCamelCase : Tuple = {'image_id': 3_9769, 'annotations': target}
# encode them
__lowerCamelCase : int = ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50' )
__lowerCamelCase : Dict = image_processing(images=_a , annotations=_a , return_tensors='pt' )
# verify pixel values
__lowerCamelCase : Dict = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , _a )
__lowerCamelCase : List[str] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _a , atol=1e-4 ) )
# verify area
__lowerCamelCase : str = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _a ) )
# verify boxes
__lowerCamelCase : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _a )
__lowerCamelCase : List[Any] = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _a , atol=1e-3 ) )
# verify image_id
__lowerCamelCase : Dict = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _a ) )
# verify is_crowd
__lowerCamelCase : Tuple = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _a ) )
# verify class_labels
__lowerCamelCase : Union[str, Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _a ) )
# verify orig_size
__lowerCamelCase : str = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _a ) )
# verify size
__lowerCamelCase : int = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _a ) )
@slow
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
# prepare image, target and masks_path
__lowerCamelCase : str = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
__lowerCamelCase : int = json.loads(f.read() )
__lowerCamelCase : int = {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}
__lowerCamelCase : str = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
__lowerCamelCase : List[str] = ConditionalDetrImageProcessor(format='coco_panoptic' )
__lowerCamelCase : Dict = image_processing(images=_a , annotations=_a , masks_path=_a , return_tensors='pt' )
# verify pixel values
__lowerCamelCase : Any = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , _a )
__lowerCamelCase : Dict = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _a , atol=1e-4 ) )
# verify area
__lowerCamelCase : int = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _a ) )
# verify boxes
__lowerCamelCase : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _a )
__lowerCamelCase : List[Any] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _a , atol=1e-3 ) )
# verify image_id
__lowerCamelCase : Optional[Any] = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _a ) )
# verify is_crowd
__lowerCamelCase : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _a ) )
# verify class_labels
__lowerCamelCase : int = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _a ) )
# verify masks
__lowerCamelCase : Any = 82_2873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , _a )
# verify orig_size
__lowerCamelCase : Optional[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _a ) )
# verify size
__lowerCamelCase : List[str] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _a ) )
| 208
|
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ) -> np.array:
__lowerCamelCase : Any = F'{sampling_rate}'
__lowerCamelCase : List[str] = '1'
__lowerCamelCase : int = 'f32le'
__lowerCamelCase : Dict = [
'ffmpeg',
'-i',
'pipe:0',
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
try:
with subprocess.Popen(_lowerCAmelCase ,stdin=subprocess.PIPE ,stdout=subprocess.PIPE ) as ffmpeg_process:
__lowerCamelCase : Tuple = ffmpeg_process.communicate(_lowerCAmelCase )
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to load audio files from filename' ) from error
__lowerCamelCase : Any = output_stream[0]
__lowerCamelCase : Union[str, Any] = np.frombuffer(_lowerCAmelCase ,np.floataa )
if audio.shape[0] == 0:
raise ValueError('Malformed soundfile' )
return audio
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = "f32le" ,) -> Dict:
__lowerCamelCase : Optional[Any] = F'{sampling_rate}'
__lowerCamelCase : Optional[int] = '1'
if format_for_conversion == "s16le":
__lowerCamelCase : List[Any] = 2
elif format_for_conversion == "f32le":
__lowerCamelCase : Tuple = 4
else:
raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )
__lowerCamelCase : Any = platform.system()
if system == "Linux":
__lowerCamelCase : Tuple = 'alsa'
__lowerCamelCase : Optional[Any] = 'default'
elif system == "Darwin":
__lowerCamelCase : Union[str, Any] = 'avfoundation'
__lowerCamelCase : Tuple = ':0'
elif system == "Windows":
__lowerCamelCase : List[str] = 'dshow'
__lowerCamelCase : Optional[Any] = 'default'
__lowerCamelCase : Optional[int] = [
'ffmpeg',
'-f',
format_,
'-i',
input_,
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-fflags',
'nobuffer',
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
__lowerCamelCase : List[str] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
__lowerCamelCase : int = _ffmpeg_stream(_lowerCAmelCase ,_lowerCAmelCase )
for item in iterator:
yield item
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = "f32le" ,) -> List[str]:
if stream_chunk_s is not None:
__lowerCamelCase : int = stream_chunk_s
else:
__lowerCamelCase : List[Any] = chunk_length_s
__lowerCamelCase : Dict = ffmpeg_microphone(_lowerCAmelCase ,_lowerCAmelCase ,format_for_conversion=_lowerCAmelCase )
if format_for_conversion == "s16le":
__lowerCamelCase : List[str] = np.intaa
__lowerCamelCase : Union[str, Any] = 2
elif format_for_conversion == "f32le":
__lowerCamelCase : Union[str, Any] = np.floataa
__lowerCamelCase : Optional[Any] = 4
else:
raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )
if stride_length_s is None:
__lowerCamelCase : Any = chunk_length_s / 6
__lowerCamelCase : List[str] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(_lowerCAmelCase ,(int, float) ):
__lowerCamelCase : Tuple = [stride_length_s, stride_length_s]
__lowerCamelCase : Union[str, Any] = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
__lowerCamelCase : Optional[Any] = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
__lowerCamelCase : Dict = datetime.datetime.now()
__lowerCamelCase : Any = datetime.timedelta(seconds=_lowerCAmelCase )
for item in chunk_bytes_iter(_lowerCAmelCase ,_lowerCAmelCase ,stride=(stride_left, stride_right) ,stream=_lowerCAmelCase ):
# Put everything back in numpy scale
__lowerCamelCase : Optional[int] = np.frombuffer(item['raw'] ,dtype=_lowerCAmelCase )
__lowerCamelCase : Tuple = (
item['stride'][0] // size_of_sample,
item['stride'][1] // size_of_sample,
)
__lowerCamelCase : Optional[int] = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = False ) -> str:
__lowerCamelCase : Optional[int] = b''
__lowerCamelCase ,__lowerCamelCase : Any = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}' )
__lowerCamelCase : str = 0
for raw in iterator:
acc += raw
if stream and len(_lowerCAmelCase ) < chunk_len:
__lowerCamelCase : Any = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(_lowerCAmelCase ) >= chunk_len:
# We are flushing the accumulator
__lowerCamelCase : Any = (_stride_left, stride_right)
__lowerCamelCase : Optional[int] = {'raw': acc[:chunk_len], 'stride': stride}
if stream:
__lowerCamelCase : List[str] = False
yield item
__lowerCamelCase : Tuple = stride_left
__lowerCamelCase : Union[str, Any] = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(_lowerCAmelCase ) > stride_left:
__lowerCamelCase : Tuple = {'raw': acc, 'stride': (_stride_left, 0)}
if stream:
__lowerCamelCase : List[str] = False
yield item
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ) -> Tuple:
__lowerCamelCase : int = 2**24 # 16Mo
try:
with subprocess.Popen(_lowerCAmelCase ,stdout=subprocess.PIPE ,bufsize=_lowerCAmelCase ) as ffmpeg_process:
while True:
__lowerCamelCase : Union[str, Any] = ffmpeg_process.stdout.read(_lowerCAmelCase )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to stream audio files from filename' ) from error
| 208
| 1
|
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
class lowerCAmelCase__ ( __lowercase ):
def __init__( self : str , *SCREAMING_SNAKE_CASE__ : Dict , **SCREAMING_SNAKE_CASE__ : List[str] ) -> None:
warnings.warn(
'''The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use FlavaImageProcessor instead.''' , SCREAMING_SNAKE_CASE__ , )
super().__init__(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 339
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Dict = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"vocab_file": {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
),
"squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
),
"squeezebert/squeezebert-mnli": (
"https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
),
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
),
},
}
SCREAMING_SNAKE_CASE__ : List[Any] = {
"squeezebert/squeezebert-uncased": 512,
"squeezebert/squeezebert-mnli": 512,
"squeezebert/squeezebert-mnli-headless": 512,
}
SCREAMING_SNAKE_CASE__ : Dict = {
"squeezebert/squeezebert-uncased": {"do_lower_case": True},
"squeezebert/squeezebert-mnli": {"do_lower_case": True},
"squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}
class lowerCAmelCase__ ( __lowercase ):
a__ : Optional[int] = VOCAB_FILES_NAMES
a__ : Any = PRETRAINED_VOCAB_FILES_MAP
a__ : Union[str, Any] = PRETRAINED_INIT_CONFIGURATION
a__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : Optional[Any] = SqueezeBertTokenizer
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : Union[str, Any]=None , SCREAMING_SNAKE_CASE__ : Any=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]="[UNK]" , SCREAMING_SNAKE_CASE__ : Optional[int]="[SEP]" , SCREAMING_SNAKE_CASE__ : Union[str, Any]="[PAD]" , SCREAMING_SNAKE_CASE__ : Tuple="[CLS]" , SCREAMING_SNAKE_CASE__ : str="[MASK]" , SCREAMING_SNAKE_CASE__ : Optional[int]=True , SCREAMING_SNAKE_CASE__ : int=None , **SCREAMING_SNAKE_CASE__ : Dict , ) -> Optional[Any]:
super().__init__(
SCREAMING_SNAKE_CASE__ , tokenizer_file=SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , tokenize_chinese_chars=SCREAMING_SNAKE_CASE__ , strip_accents=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
__lowerCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , SCREAMING_SNAKE_CASE__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , SCREAMING_SNAKE_CASE__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , SCREAMING_SNAKE_CASE__ ) != tokenize_chinese_chars
):
__lowerCamelCase = getattr(SCREAMING_SNAKE_CASE__ , normalizer_state.pop('''type''' ) )
__lowerCamelCase = do_lower_case
__lowerCamelCase = strip_accents
__lowerCamelCase = tokenize_chinese_chars
__lowerCamelCase = normalizer_class(**SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = do_lower_case
def __A ( self : Dict , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str]=None ) -> str:
__lowerCamelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __A ( self : Tuple , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : Optional[List[int]] = None ) -> List[int]:
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __A ( self : int , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[str] = None ) -> Tuple[str]:
__lowerCamelCase = self._tokenizer.model.save(SCREAMING_SNAKE_CASE__ , name=SCREAMING_SNAKE_CASE__ )
return tuple(SCREAMING_SNAKE_CASE__ )
| 339
| 1
|
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Optional[Any]=12 ,lowerCamelCase__ : Tuple=7 ,lowerCamelCase__ : Optional[int]=True ,lowerCamelCase__ : Tuple=True ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : Optional[int]=99 ,lowerCamelCase__ : Any=32 ,lowerCamelCase__ : Any=32 ,lowerCamelCase__ : List[str]=2 ,lowerCamelCase__ : str=4 ,lowerCamelCase__ : Dict=37 ,lowerCamelCase__ : List[str]=0.1 ,lowerCamelCase__ : Optional[int]=0.1 ,lowerCamelCase__ : int=512 ,lowerCamelCase__ : Union[str, Any]=0.0_2 ,lowerCamelCase__ : Optional[Any]=0 ,lowerCamelCase__ : Any=None ,):
UpperCAmelCase__ = parent
UpperCAmelCase__ = batch_size
UpperCAmelCase__ = seq_length
UpperCAmelCase__ = is_training
UpperCAmelCase__ = use_input_mask
UpperCAmelCase__ = use_labels
UpperCAmelCase__ = vocab_size
UpperCAmelCase__ = hidden_size
UpperCAmelCase__ = projection_dim
UpperCAmelCase__ = num_hidden_layers
UpperCAmelCase__ = num_attention_heads
UpperCAmelCase__ = intermediate_size
UpperCAmelCase__ = dropout
UpperCAmelCase__ = attention_dropout
UpperCAmelCase__ = max_position_embeddings
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = scope
UpperCAmelCase__ = bos_token_id
def __lowerCAmelCase ( self : Union[str, Any] ):
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ = None
if self.use_input_mask:
UpperCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
UpperCAmelCase__ = input_mask.numpy()
UpperCAmelCase__ = input_mask.shape
UpperCAmelCase__ = np.random.randint(1 ,seq_length - 1 ,size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
UpperCAmelCase__ = 1
UpperCAmelCase__ = 0
UpperCAmelCase__ = self.get_config()
return config, input_ids, tf.convert_to_tensor(lowerCamelCase__ )
def __lowerCAmelCase ( self : Tuple ):
return BlipTextConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,projection_dim=self.projection_dim ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,dropout=self.dropout ,attention_dropout=self.attention_dropout ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,bos_token_id=self.bos_token_id ,)
def __lowerCAmelCase ( self : List[Any] ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : str ):
UpperCAmelCase__ = TFBlipTextModel(config=lowerCamelCase__ )
UpperCAmelCase__ = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,training=lowerCamelCase__ )
UpperCAmelCase__ = model(lowerCamelCase__ ,training=lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape ,(self.batch_size, self.hidden_size) )
def __lowerCAmelCase ( self : Union[str, Any] ):
UpperCAmelCase__ = self.prepare_config_and_inputs()
UpperCAmelCase__ = config_and_inputs
UpperCAmelCase__ = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class snake_case ( __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
snake_case__ = (TFBlipTextModel,) if is_tf_available() else ()
snake_case__ = False
snake_case__ = False
snake_case__ = False
def __lowerCAmelCase ( self : Tuple ):
UpperCAmelCase__ = BlipTextModelTester(self )
UpperCAmelCase__ = ConfigTester(self ,config_class=lowerCamelCase__ ,hidden_size=37 )
def __lowerCAmelCase ( self : Optional[int] ):
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : Optional[Any] ):
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def __lowerCAmelCase ( self : List[str] ):
pass
def __lowerCAmelCase ( self : int ):
pass
@unittest.skip(reason='Blip does not use inputs_embeds' )
def __lowerCAmelCase ( self : Optional[int] ):
pass
@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING' )
def __lowerCAmelCase ( self : str ):
pass
@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING' )
def __lowerCAmelCase ( self : List[Any] ):
pass
@slow
def __lowerCAmelCase ( self : Tuple ):
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ = TFBlipTextModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def __lowerCAmelCase ( self : Tuple ,lowerCamelCase__ : Optional[Any]=True ):
super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCamelCase__ )
| 98
|
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceClassification,
TFTrainer,
TFTrainingArguments,
)
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def lowerCAmelCase_ ( __UpperCAmelCase: str , __UpperCAmelCase: str , __UpperCAmelCase: str , __UpperCAmelCase: PreTrainedTokenizer , __UpperCAmelCase: int , __UpperCAmelCase: Optional[int] = None , ) -> List[Any]:
UpperCamelCase__ : Dict = {}
if train_file is not None:
UpperCamelCase__ : str = [train_file]
if eval_file is not None:
UpperCamelCase__ : Union[str, Any] = [eval_file]
if test_file is not None:
UpperCamelCase__ : Tuple = [test_file]
UpperCamelCase__ : Optional[Any] = datasets.load_dataset('''csv''' , data_files=__UpperCAmelCase )
UpperCamelCase__ : List[Any] = list(ds[list(files.keys() )[0]].features.keys() )
UpperCamelCase__ : str = features_name.pop(__UpperCAmelCase )
UpperCamelCase__ : List[str] = list(set(ds[list(files.keys() )[0]][label_name] ) )
UpperCamelCase__ : Optional[Any] = {label: i for i, label in enumerate(__UpperCAmelCase )}
UpperCamelCase__ : Union[str, Any] = tokenizer.model_input_names
UpperCamelCase__ : str = {}
if len(__UpperCAmelCase ) == 1:
for k in files.keys():
UpperCamelCase__ : Optional[int] = ds[k].map(
lambda __UpperCAmelCase : tokenizer.batch_encode_plus(
example[features_name[0]] , truncation=__UpperCAmelCase , max_length=__UpperCAmelCase , padding='''max_length''' ) , batched=__UpperCAmelCase , )
elif len(__UpperCAmelCase ) == 2:
for k in files.keys():
UpperCamelCase__ : Dict = ds[k].map(
lambda __UpperCAmelCase : tokenizer.batch_encode_plus(
(example[features_name[0]], example[features_name[1]]) , truncation=__UpperCAmelCase , max_length=__UpperCAmelCase , padding='''max_length''' , ) , batched=__UpperCAmelCase , )
def gen_train():
for ex in transformed_ds[datasets.Split.TRAIN]:
UpperCamelCase__ : Any = {k: v for k, v in ex.items() if k in input_names}
UpperCamelCase__ : str = labelaid[ex[label_name]]
yield (d, label)
def gen_val():
for ex in transformed_ds[datasets.Split.VALIDATION]:
UpperCamelCase__ : Union[str, Any] = {k: v for k, v in ex.items() if k in input_names}
UpperCamelCase__ : str = labelaid[ex[label_name]]
yield (d, label)
def gen_test():
for ex in transformed_ds[datasets.Split.TEST]:
UpperCamelCase__ : Optional[Any] = {k: v for k, v in ex.items() if k in input_names}
UpperCamelCase__ : int = labelaid[ex[label_name]]
yield (d, label)
UpperCamelCase__ : Tuple = (
tf.data.Dataset.from_generator(
__UpperCAmelCase , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TRAIN in transformed_ds
else None
)
if train_ds is not None:
UpperCamelCase__ : Optional[Any] = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )
UpperCamelCase__ : int = (
tf.data.Dataset.from_generator(
__UpperCAmelCase , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.VALIDATION in transformed_ds
else None
)
if val_ds is not None:
UpperCamelCase__ : Dict = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )
UpperCamelCase__ : Optional[Any] = (
tf.data.Dataset.from_generator(
__UpperCAmelCase , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TEST in transformed_ds
else None
)
if test_ds is not None:
UpperCamelCase__ : Union[str, Any] = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )
return train_ds, val_ds, test_ds, labelaid
UpperCAmelCase_ = logging.getLogger(__name__)
@dataclass
class lowercase__ :
'''simple docstring'''
a : int = field(metadata={"help": "Which column contains the label"} )
a : str = field(default=__lowerCamelCase , metadata={"help": "The path of the training file"} )
a : Optional[str] = field(default=__lowerCamelCase , metadata={"help": "The path of the development file"} )
a : Optional[str] = field(default=__lowerCamelCase , metadata={"help": "The path of the test file"} )
a : int = field(
default=128 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
a : bool = field(
default=__lowerCamelCase , metadata={"help": "Overwrite the cached training and evaluation sets"} )
@dataclass
class lowercase__ :
'''simple docstring'''
a : str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
a : Optional[str] = field(
default=__lowerCamelCase , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
a : Optional[str] = field(
default=__lowerCamelCase , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
a : bool = field(default=__lowerCamelCase , metadata={"help": "Set this flag to use fast tokenization."} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
a : Optional[str] = field(
default=__lowerCamelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
def lowerCAmelCase_ ( ) -> int:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase__ : Tuple = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : Any = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO , )
logger.info(
f"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, "
f"16-bits training: {training_args.fpaa}" )
logger.info(f"Training/evaluation parameters {training_args}" )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase__ : List[str] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : Optional[int] = get_tfds(
train_file=data_args.train_file , eval_file=data_args.dev_file , test_file=data_args.test_file , tokenizer=__UpperCAmelCase , label_column_id=data_args.label_column_id , max_seq_length=data_args.max_seq_length , )
UpperCamelCase__ : int = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=len(__UpperCAmelCase ) , labelaid=__UpperCAmelCase , idalabel={id: label for label, id in labelaid.items()} , finetuning_task='''text-classification''' , cache_dir=model_args.cache_dir , )
with training_args.strategy.scope():
UpperCamelCase__ : str = TFAutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_pt=bool('''.bin''' in model_args.model_name_or_path ) , config=__UpperCAmelCase , cache_dir=model_args.cache_dir , )
def compute_metrics(__UpperCAmelCase: EvalPrediction ) -> Dict:
UpperCamelCase__ : Optional[Any] = np.argmax(p.predictions , axis=1 )
return {"acc": (preds == p.label_ids).mean()}
# Initialize our Trainer
UpperCamelCase__ : Union[str, Any] = TFTrainer(
model=__UpperCAmelCase , args=__UpperCAmelCase , train_dataset=__UpperCAmelCase , eval_dataset=__UpperCAmelCase , compute_metrics=__UpperCAmelCase , )
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
UpperCamelCase__ : List[str] = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
UpperCamelCase__ : Tuple = trainer.evaluate()
UpperCamelCase__ : Optional[int] = os.path.join(training_args.output_dir , '''eval_results.txt''' )
with open(__UpperCAmelCase , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(f" {key} = {value}" )
writer.write(f"{key} = {value}\n" )
results.update(__UpperCAmelCase )
return results
if __name__ == "__main__":
main()
| 201
| 0
|
from __future__ import annotations
def lowerCAmelCase_ ( __a , __a ) -> List[Any]:
"""simple docstring"""
print(F"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(__a ):
print(F"""{i}\t\t{d}""" )
def lowerCAmelCase_ ( __a , __a , __a ) -> Tuple:
"""simple docstring"""
for j in range(__a ):
lowerCamelCase__: str =(graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf" ) and distance[u] + w < distance[v]:
return True
return False
def lowerCAmelCase_ ( __a , __a , __a , __a ) -> list[float]:
"""simple docstring"""
lowerCamelCase__: List[str] =[float("inf" )] * vertex_count
lowerCamelCase__: List[str] =0.0
for _ in range(vertex_count - 1 ):
for j in range(__a ):
lowerCamelCase__: Union[str, Any] =(graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf" ) and distance[u] + w < distance[v]:
lowerCamelCase__: int =distance[u] + w
lowerCamelCase__: Tuple =check_negative_cycle(__a , __a , __a )
if negative_cycle_exists:
raise Exception("Negative cycle found" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
__A = int(input("Enter number of vertices: ").strip())
__A = int(input("Enter number of edges: ").strip())
__A = [{} for _ in range(E)]
for i in range(E):
print("Edge ", i + 1)
__A , __A , __A = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
__A = {"src": src, "dst": dest, "weight": weight}
__A = int(input("\nEnter shortest path source:").strip())
__A = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 363
|
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__(self : str) ->List[Any]:
'''simple docstring'''
lowerCamelCase__: List[Any] =[]
def SCREAMING_SNAKE_CASE_ (self : Any , UpperCAmelCase_ : int , UpperCAmelCase_ : str , UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Any) ->Dict:
'''simple docstring'''
self.events.append("on_init_end")
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Dict , **UpperCAmelCase_ : str) ->List[str]:
'''simple docstring'''
self.events.append("on_train_begin")
def SCREAMING_SNAKE_CASE_ (self : List[str] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : str) ->int:
'''simple docstring'''
self.events.append("on_train_end")
def SCREAMING_SNAKE_CASE_ (self : Tuple , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : Optional[int]) ->List[Any]:
'''simple docstring'''
self.events.append("on_epoch_begin")
def SCREAMING_SNAKE_CASE_ (self : Optional[int] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : int , **UpperCAmelCase_ : Any) ->Tuple:
'''simple docstring'''
self.events.append("on_epoch_end")
def SCREAMING_SNAKE_CASE_ (self : List[Any] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Any , UpperCAmelCase_ : Any , **UpperCAmelCase_ : List[Any]) ->Optional[int]:
'''simple docstring'''
self.events.append("on_step_begin")
def SCREAMING_SNAKE_CASE_ (self : List[str] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : List[str]) ->Tuple:
'''simple docstring'''
self.events.append("on_step_end")
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : str) ->Optional[int]:
'''simple docstring'''
self.events.append("on_evaluate")
def SCREAMING_SNAKE_CASE_ (self : Tuple , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Any) ->int:
'''simple docstring'''
self.events.append("on_predict")
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : List[Any]) ->Any:
'''simple docstring'''
self.events.append("on_save")
def SCREAMING_SNAKE_CASE_ (self : str , UpperCAmelCase_ : Any , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Any , **UpperCAmelCase_ : Optional[Any]) ->str:
'''simple docstring'''
self.events.append("on_log")
def SCREAMING_SNAKE_CASE_ (self : Optional[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : str , **UpperCAmelCase_ : List[str]) ->Optional[int]:
'''simple docstring'''
self.events.append("on_prediction_step")
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->int:
'''simple docstring'''
lowerCamelCase__: Tuple =tempfile.mkdtemp()
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->Tuple:
'''simple docstring'''
shutil.rmtree(self.output_dir)
def SCREAMING_SNAKE_CASE_ (self : List[Any] , UpperCAmelCase_ : int=0 , UpperCAmelCase_ : Tuple=0 , UpperCAmelCase_ : str=64 , UpperCAmelCase_ : List[Any]=64 , UpperCAmelCase_ : Union[str, Any]=None , UpperCAmelCase_ : List[str]=False , **UpperCAmelCase_ : Tuple) ->Any:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =RegressionDataset(length=UpperCAmelCase_)
lowerCamelCase__: int =RegressionDataset(length=UpperCAmelCase_)
lowerCamelCase__: str =RegressionModelConfig(a=UpperCAmelCase_ , b=UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =RegressionPreTrainedModel(UpperCAmelCase_)
lowerCamelCase__: int =TrainingArguments(self.output_dir , disable_tqdm=UpperCAmelCase_ , report_to=[] , **UpperCAmelCase_)
return Trainer(
UpperCAmelCase_ , UpperCAmelCase_ , train_dataset=UpperCAmelCase_ , eval_dataset=UpperCAmelCase_ , callbacks=UpperCAmelCase_ , )
def SCREAMING_SNAKE_CASE_ (self : List[str] , UpperCAmelCase_ : str , UpperCAmelCase_ : List[str]) ->Dict:
'''simple docstring'''
self.assertEqual(len(UpperCAmelCase_) , len(UpperCAmelCase_))
# Order doesn't matter
lowerCamelCase__: Dict =sorted(UpperCAmelCase_ , key=lambda UpperCAmelCase_: cb.__name__ if isinstance(UpperCAmelCase_ , UpperCAmelCase_) else cb.__class__.__name__)
lowerCamelCase__: Optional[int] =sorted(UpperCAmelCase_ , key=lambda UpperCAmelCase_: cb.__name__ if isinstance(UpperCAmelCase_ , UpperCAmelCase_) else cb.__class__.__name__)
for cba, cba in zip(UpperCAmelCase_ , UpperCAmelCase_):
if isinstance(UpperCAmelCase_ , UpperCAmelCase_) and isinstance(UpperCAmelCase_ , UpperCAmelCase_):
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_)
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_) and not isinstance(UpperCAmelCase_ , UpperCAmelCase_):
self.assertEqual(UpperCAmelCase_ , cba.__class__)
elif not isinstance(UpperCAmelCase_ , UpperCAmelCase_) and isinstance(UpperCAmelCase_ , UpperCAmelCase_):
self.assertEqual(cba.__class__ , UpperCAmelCase_)
else:
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : List[Any] , UpperCAmelCase_ : Optional[Any]) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: Dict =["on_init_end", "on_train_begin"]
lowerCamelCase__: List[str] =0
lowerCamelCase__: List[Any] =len(trainer.get_eval_dataloader())
lowerCamelCase__: Dict =["on_prediction_step"] * len(trainer.get_eval_dataloader()) + ["on_log", "on_evaluate"]
for _ in range(trainer.state.num_train_epochs):
expected_events.append("on_epoch_begin")
for _ in range(UpperCAmelCase_):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("on_log")
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append("on_save")
expected_events.append("on_epoch_end")
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def SCREAMING_SNAKE_CASE_ (self : int) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: Optional[Any] =self.get_trainer()
lowerCamelCase__: Any =DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCAmelCase_)
# Callbacks passed at init are added to the default callbacks
lowerCamelCase__: Dict =self.get_trainer(callbacks=[MyTestTrainerCallback])
expected_callbacks.append(UpperCAmelCase_)
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCAmelCase_)
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
lowerCamelCase__: int =self.get_trainer(disable_tqdm=UpperCAmelCase_)
lowerCamelCase__: Tuple =DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =DEFAULT_CALLBACKS.copy() + [ProgressCallback]
lowerCamelCase__: Optional[int] =self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(UpperCAmelCase_)
expected_callbacks.remove(UpperCAmelCase_)
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCAmelCase_)
lowerCamelCase__: Dict =self.get_trainer()
lowerCamelCase__: str =trainer.pop_callback(UpperCAmelCase_)
self.assertEqual(cb.__class__ , UpperCAmelCase_)
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCAmelCase_)
trainer.add_callback(UpperCAmelCase_)
expected_callbacks.insert(0 , UpperCAmelCase_)
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCAmelCase_)
# We can also add, pop, or remove by instance
lowerCamelCase__: List[str] =self.get_trainer()
lowerCamelCase__: List[str] =trainer.callback_handler.callbacks[0]
trainer.remove_callback(UpperCAmelCase_)
expected_callbacks.remove(UpperCAmelCase_)
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCAmelCase_)
lowerCamelCase__: str =self.get_trainer()
lowerCamelCase__: List[Any] =trainer.callback_handler.callbacks[0]
lowerCamelCase__: Dict =trainer.pop_callback(UpperCAmelCase_)
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_)
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCAmelCase_)
trainer.add_callback(UpperCAmelCase_)
expected_callbacks.insert(0 , UpperCAmelCase_)
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->int:
'''simple docstring'''
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action="ignore" , category=UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =self.get_trainer(callbacks=[MyTestTrainerCallback])
trainer.train()
lowerCamelCase__: int =trainer.callback_handler.callbacks[-2].events
self.assertEqual(UpperCAmelCase_ , self.get_expected_events(UpperCAmelCase_))
# Independent log/save/eval
lowerCamelCase__: Dict =self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5)
trainer.train()
lowerCamelCase__: Optional[int] =trainer.callback_handler.callbacks[-2].events
self.assertEqual(UpperCAmelCase_ , self.get_expected_events(UpperCAmelCase_))
lowerCamelCase__: Any =self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5)
trainer.train()
lowerCamelCase__: List[Any] =trainer.callback_handler.callbacks[-2].events
self.assertEqual(UpperCAmelCase_ , self.get_expected_events(UpperCAmelCase_))
lowerCamelCase__: int =self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy="steps")
trainer.train()
lowerCamelCase__: str =trainer.callback_handler.callbacks[-2].events
self.assertEqual(UpperCAmelCase_ , self.get_expected_events(UpperCAmelCase_))
lowerCamelCase__: Dict =self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy="epoch")
trainer.train()
lowerCamelCase__: Tuple =trainer.callback_handler.callbacks[-2].events
self.assertEqual(UpperCAmelCase_ , self.get_expected_events(UpperCAmelCase_))
# A bit of everything
lowerCamelCase__: Tuple =self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy="steps" , )
trainer.train()
lowerCamelCase__: int =trainer.callback_handler.callbacks[-2].events
self.assertEqual(UpperCAmelCase_ , self.get_expected_events(UpperCAmelCase_))
# warning should be emitted for duplicated callbacks
with patch("transformers.trainer_callback.logger.warning") as warn_mock:
lowerCamelCase__: Optional[int] =self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(UpperCAmelCase_) in warn_mock.call_args[0][0]
| 273
| 0
|
'''simple docstring'''
def snake_case__ ( _A: int ) -> list:
'''simple docstring'''
lowerCAmelCase = int(_A )
if n_element < 1:
lowerCAmelCase = ValueError("""a should be a positive number""" )
raise my_error
lowerCAmelCase = [1]
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = (0, 0, 0)
lowerCAmelCase = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
__lowercase = input('''Enter the last number (nth term) of the Hamming Number Series: ''')
print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
__lowercase = hamming(int(n))
print('''-----------------------------------------------------''')
print(f'The list with nth numbers is: {hamming_numbers}')
print('''-----------------------------------------------------''')
| 272
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
__lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 272
| 1
|
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
UpperCamelCase = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('''''', '''|''', '''|'''),
datarow=DataRow('''''', '''|''', '''|'''),
padding=1,
with_header_hide=None,
)
UpperCamelCase = []
UpperCamelCase = []
UpperCamelCase = {'''type''': '''section''', '''text''': {'''type''': '''plain_text''', '''text''': '''No failed tests! 🤗''', '''emoji''': True}}
UpperCamelCase = [
{
'''type''': '''header''',
'''text''': {
'''type''': '''plain_text''',
'''text''': f'🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results',
'''emoji''': True,
},
}
]
UpperCamelCase = 0
for log in Path().glob('''*.log'''):
UpperCamelCase = 0
with open(log, '''r''') as f:
for line in f:
UpperCamelCase = json.loads(line)
if line.get('''nodeid''', '''''') != "":
UpperCamelCase = line['''nodeid''']
if line.get('''duration''', None) is not None:
UpperCamelCase = f'{line["duration"]:.4f}'
if line.get('''outcome''', '''''') == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split('''_''')[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
UpperCamelCase = []
log.unlink()
UpperCamelCase = ''''''
UpperCamelCase = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += f"*{name[1:]}: {num_failed} failed test*\n"
else:
message += f"*{name[1:]}: {num_failed} failed tests*\n"
UpperCamelCase = []
UpperCamelCase = {}
for test in failed_tests:
UpperCamelCase = test[0].split('''::''')
UpperCamelCase = data[0].split('''/''')[-1]
if data[0] not in filesafailed:
UpperCamelCase = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
UpperCamelCase = [test[0] for test in failed_table]
UpperCamelCase = list(set(files))
# Count number of instances in failed_tests
UpperCamelCase = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
UpperCamelCase = tabulate(
table,
headers=['''Test Location''', '''Num Failed'''],
tablefmt=hf_table_format,
stralign='''right''',
)
message += f"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 3000:
UpperCamelCase = '''Too many failed tests, please see the full report in the Action results.'''
UpperCamelCase = len(err) + 10
UpperCamelCase = message[: 3000 - offset] + f'\n...\n```\n{err}'
print(f'### {message}')
else:
UpperCamelCase = '''No failed tests! 🤗'''
print(f'## {message}')
payload.append(no_error_payload)
if os.environ.get('''TEST_TYPE''', '''''') != "":
from slack_sdk import WebClient
UpperCamelCase = WebClient(token=os.environ['''SLACK_API_TOKEN'''])
if message != "No failed tests! 🤗":
UpperCamelCase = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': message,
},
}
payload.append(md_report)
UpperCamelCase = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': '''*For more details:*''',
},
'''accessory''': {
'''type''': '''button''',
'''text''': {
'''type''': '''plain_text''',
'''text''': '''Check Action results''',
'''emoji''': True,
},
'''url''': f'https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
payload.append(action_button)
UpperCamelCase = {
'''type''': '''context''',
'''elements''': [
{
'''type''': '''plain_text''',
'''text''': f'Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}',
}
],
}
payload.append(date_report)
UpperCamelCase = client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
UpperCamelCase = response.data['''ts''']
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
UpperCamelCase = ''''''
for i, row in enumerate(test_failures):
if row[0] != test_class:
UpperCamelCase = row[0]
else:
UpperCamelCase = ''''''
UpperCamelCase = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': f'Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```',
},
}
client.chat_postMessage(
channel='''#accelerate-ci-daily''',
thread_ts=ts,
blocks=[payload],
)
| 352
|
'''simple docstring'''
import os
from distutils.util import strtobool
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> List[Any]:
for e in env_keys:
A: Dict = int(os.environ.get(__lowercase , -1 ) )
if val >= 0:
return val
return default
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase=False ) -> List[str]:
A: str = os.environ.get(__lowercase , str(__lowercase ) )
return strtobool(__lowercase ) == 1 # As its name indicates `strtobool` actually returns an int...
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase="no" ) -> str:
A: Optional[int] = os.environ.get(__lowercase , str(__lowercase ) )
return value
| 334
| 0
|
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase_ = logging.getLogger()
def __SCREAMING_SNAKE_CASE ():
snake_case_ = argparse.ArgumentParser()
parser.add_argument('''-f''' )
snake_case_ = parser.parse_args()
return args.f
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = {}
snake_case_ = os.path.join(SCREAMING_SNAKE_CASE__ , '''all_results.json''' )
if os.path.exists(SCREAMING_SNAKE_CASE__ ):
with open(SCREAMING_SNAKE_CASE__ , '''r''' ) as f:
snake_case_ = json.load(SCREAMING_SNAKE_CASE__ )
else:
raise ValueError(F'''can\'t find {path}''' )
return results
def __SCREAMING_SNAKE_CASE ():
snake_case_ = torch.cuda.is_available() and torch_device == '''cuda'''
return is_using_cuda and is_apex_available()
lowerCAmelCase_ = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class snake_case_ ( __A ):
'''simple docstring'''
@classmethod
def snake_case__( cls : Optional[int] ) ->List[Any]:
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
snake_case_ = tempfile.mkdtemp()
snake_case_ = os.path.join(cls.tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
snake_case_ = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def snake_case__( cls : Dict ) ->str:
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def snake_case__( self : int ) ->Optional[int]:
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f'''
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
'''.split()
if is_cuda_and_apex_available():
testargs.append('''--fp16''' )
run_command(self._launch_args + testargs )
snake_case_ = get_results(_UpperCamelCase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''glue_no_trainer''' ) ) )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def snake_case__( self : Any ) ->int:
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f'''
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
'''.split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
snake_case_ = get_results(_UpperCamelCase )
self.assertLess(result['''perplexity'''] , 1_0_0 )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''clm_no_trainer''' ) ) )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def snake_case__( self : str ) ->Union[str, Any]:
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f'''
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(_UpperCamelCase )
self.assertLess(result['''perplexity'''] , 4_2 )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''mlm_no_trainer''' ) ) )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def snake_case__( self : Tuple ) ->Union[str, Any]:
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
snake_case_ = 7 if get_gpu_count() > 1 else 2
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f'''
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(_UpperCamelCase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
self.assertLess(result['''train_loss'''] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''ner_no_trainer''' ) ) )
@unittest.skip(reason='''Fix me @muellerzr''' )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def snake_case__( self : Optional[int] ) ->str:
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f'''
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(_UpperCamelCase )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['''eval_f1'''] , 2_8 )
self.assertGreaterEqual(result['''eval_exact'''] , 2_8 )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''qa_no_trainer''' ) ) )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def snake_case__( self : List[str] ) ->List[str]:
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f'''
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(_UpperCamelCase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''swag_no_trainer''' ) ) )
@slow
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def snake_case__( self : str ) ->Any:
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f'''
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(_UpperCamelCase )
self.assertGreaterEqual(result['''eval_rouge1'''] , 1_0 )
self.assertGreaterEqual(result['''eval_rouge2'''] , 2 )
self.assertGreaterEqual(result['''eval_rougeL'''] , 7 )
self.assertGreaterEqual(result['''eval_rougeLsum'''] , 7 )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''summarization_no_trainer''' ) ) )
@slow
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def snake_case__( self : Any ) ->List[str]:
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f'''
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(_UpperCamelCase )
self.assertGreaterEqual(result['''eval_bleu'''] , 3_0 )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''translation_no_trainer''' ) ) )
@slow
def snake_case__( self : Optional[Any] ) ->Union[str, Any]:
snake_case_ = logging.StreamHandler(sys.stdout )
logger.addHandler(_UpperCamelCase )
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f'''
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
'''.split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(_UpperCamelCase )
self.assertGreaterEqual(result['''eval_overall_accuracy'''] , 0.10 )
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def snake_case__( self : Any ) ->Union[str, Any]:
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f'''
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
'''.split()
if is_cuda_and_apex_available():
testargs.append('''--fp16''' )
run_command(self._launch_args + testargs )
snake_case_ = get_results(_UpperCamelCase )
# The base model scores a 25%
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''step_1''' ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , '''image_classification_no_trainer''' ) ) )
| 8
|
'''simple docstring'''
def __lowerCamelCase ( __snake_case : int, __snake_case : int, __snake_case : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(__snake_case : int, __snake_case : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
A__ : int =update_area_of_max_square(__snake_case, col + 1 )
A__ : int =update_area_of_max_square(row + 1, col + 1 )
A__ : int =update_area_of_max_square(row + 1, __snake_case )
if mat[row][col]:
A__ : Optional[Any] =1 + min([right, diagonal, down] )
A__ : Dict =max(largest_square_area[0], __snake_case )
return sub_problem_sol
else:
return 0
A__ : List[Any] =[0]
update_area_of_max_square(0, 0 )
return largest_square_area[0]
def __lowerCamelCase ( __snake_case : int, __snake_case : int, __snake_case : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
__snake_case : int, __snake_case : int, __snake_case : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
A__ : str =update_area_of_max_square_using_dp_array(__snake_case, col + 1, __snake_case )
A__ : Any =update_area_of_max_square_using_dp_array(row + 1, col + 1, __snake_case )
A__ : List[str] =update_area_of_max_square_using_dp_array(row + 1, __snake_case, __snake_case )
if mat[row][col]:
A__ : Optional[int] =1 + min([right, diagonal, down] )
A__ : Any =max(largest_square_area[0], __snake_case )
A__ : Union[str, Any] =sub_problem_sol
return sub_problem_sol
else:
return 0
A__ : Any =[0]
A__ : Optional[Any] =[[-1] * cols for _ in range(__snake_case )]
update_area_of_max_square_using_dp_array(0, 0, __snake_case )
return largest_square_area[0]
def __lowerCamelCase ( __snake_case : int, __snake_case : int, __snake_case : list[list[int]] ) -> int:
"""simple docstring"""
A__ : Optional[int] =[[0] * (cols + 1) for _ in range(rows + 1 )]
A__ : str =0
for row in range(rows - 1, -1, -1 ):
for col in range(cols - 1, -1, -1 ):
A__ : List[Any] =dp_array[row][col + 1]
A__ : List[str] =dp_array[row + 1][col + 1]
A__ : str =dp_array[row + 1][col]
if mat[row][col] == 1:
A__ : str =1 + min(__snake_case, __snake_case, __snake_case )
A__ : Optional[Any] =max(dp_array[row][col], __snake_case )
else:
A__ : Tuple =0
return largest_square_area
def __lowerCamelCase ( __snake_case : int, __snake_case : int, __snake_case : list[list[int]] ) -> int:
"""simple docstring"""
A__ : Union[str, Any] =[0] * (cols + 1)
A__ : int =[0] * (cols + 1)
A__ : str =0
for row in range(rows - 1, -1, -1 ):
for col in range(cols - 1, -1, -1 ):
A__ : Union[str, Any] =current_row[col + 1]
A__ : List[str] =next_row[col + 1]
A__ : str =next_row[col]
if mat[row][col] == 1:
A__ : str =1 + min(__snake_case, __snake_case, __snake_case )
A__ : Dict =max(current_row[col], __snake_case )
else:
A__ : str =0
A__ : Optional[Any] =current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 134
| 0
|
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class __snake_case ( __lowerCAmelCase ):
def __init__( self , *lowercase , **lowercase) -> List[Any]:
'''simple docstring'''
super().__init__(*lowercase , **lowercase)
a__: Union[str, Any] = {}
def lowerCamelCase_ ( self , lowercase , *lowercase , **lowercase) -> List[str]:
'''simple docstring'''
a__: str = super().add_tokens(lowercase , *lowercase , **lowercase)
if num_added_tokens == 0:
raise ValueError(
f'The tokenizer already contains the token {placeholder_token}. Please pass a different'
' `placeholder_token` that is not already in the tokenizer.')
def lowerCamelCase_ ( self , lowercase , *lowercase , lowercase=1 , **lowercase) -> int:
'''simple docstring'''
a__: int = []
if num_vec_per_token == 1:
self.try_adding_tokens(lowercase , *lowercase , **lowercase)
output.append(lowercase)
else:
a__: Optional[int] = []
for i in range(lowercase):
a__: Optional[int] = placeholder_token + f'_{i}'
self.try_adding_tokens(lowercase , *lowercase , **lowercase)
output.append(lowercase)
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
f'The tokenizer already has placeholder token {token} that can get confused with'
f' {placeholder_token}keep placeholder tokens independent')
a__: Tuple = output
def lowerCamelCase_ ( self , lowercase , lowercase=False , lowercase=1.0) -> List[Any]:
'''simple docstring'''
if isinstance(lowercase , lowercase):
a__: Union[str, Any] = []
for i in range(len(lowercase)):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=lowercase))
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
a__: Optional[int] = self.token_map[placeholder_token]
a__: Optional[int] = tokens[: 1 + int(len(lowercase) * prop_tokens_to_load)]
if vector_shuffle:
a__: Dict = copy.copy(lowercase)
random.shuffle(lowercase)
a__: int = text.replace(lowercase , ' '.join(lowercase))
return text
def __call__( self , lowercase , *lowercase , lowercase=False , lowercase=1.0 , **lowercase) -> int:
'''simple docstring'''
return super().__call__(
self.replace_placeholder_tokens_in_text(
lowercase , vector_shuffle=lowercase , prop_tokens_to_load=lowercase) , *lowercase , **lowercase , )
def lowerCamelCase_ ( self , lowercase , *lowercase , lowercase=False , lowercase=1.0 , **lowercase) -> Tuple:
'''simple docstring'''
return super().encode(
self.replace_placeholder_tokens_in_text(
lowercase , vector_shuffle=lowercase , prop_tokens_to_load=lowercase) , *lowercase , **lowercase , )
| 203
|
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __snake_case :
def __init__( self , lowercase , lowercase=13 , lowercase=10 , lowercase=3 , lowercase=2 , lowercase=2 , lowercase=2 , lowercase=True , lowercase=True , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=10 , lowercase=0.02 , lowercase=0.9 , lowercase=None , ) -> Optional[Any]:
'''simple docstring'''
a__: int = parent
a__: int = batch_size
a__: int = image_size
a__: Optional[int] = num_channels
a__: List[str] = patch_size
a__: List[str] = tubelet_size
a__: Any = num_frames
a__: Any = is_training
a__: Dict = use_labels
a__: Optional[Any] = hidden_size
a__: Optional[int] = num_hidden_layers
a__: Optional[Any] = num_attention_heads
a__: Optional[Any] = intermediate_size
a__: Any = hidden_act
a__: Dict = hidden_dropout_prob
a__: Union[str, Any] = attention_probs_dropout_prob
a__: List[Any] = type_sequence_label_size
a__: Optional[Any] = initializer_range
a__: List[str] = mask_ratio
a__: Union[str, Any] = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
a__: Dict = (image_size // patch_size) ** 2
a__: Tuple = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
a__: Tuple = int(mask_ratio * self.seq_length)
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: List[Any] = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size])
a__: Any = None
if self.use_labels:
a__: Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size)
a__: Optional[int] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> str:
'''simple docstring'''
a__: Any = VideoMAEModel(config=lowercase)
model.to(lowercase)
model.eval()
a__: Optional[Any] = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> int:
'''simple docstring'''
a__: List[str] = VideoMAEForPreTraining(lowercase)
model.to(lowercase)
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
a__: int = torch.ones((self.num_masks,))
a__: Any = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0))])
a__: int = mask.expand(self.batch_size , -1).bool()
a__: Union[str, Any] = model(lowercase , lowercase)
# model only returns predictions for masked patches
a__: List[str] = mask.sum().item()
a__: str = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels))
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Dict = self.prepare_config_and_inputs()
a__ , a__ , a__: Dict = config_and_inputs
a__: Any = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __snake_case ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
a__ = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
a__ = (
{"""feature-extraction""": VideoMAEModel, """video-classification""": VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
a__ = False
a__ = False
a__ = False
a__ = False
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: List[str] = VideoMAEModelTester(self)
a__: str = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37)
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase=False) -> Any:
'''simple docstring'''
a__: Optional[int] = copy.deepcopy(lowercase)
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
a__: List[Any] = torch.ones((self.model_tester.num_masks,))
a__: List[Any] = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0))])
a__: Optional[int] = mask.expand(self.model_tester.batch_size , -1).bool()
a__: Union[str, Any] = bool_masked_pos.to(lowercase)
if return_labels:
if model_class in [
*get_values(lowercase),
]:
a__: str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase)
return inputs_dict
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='VideoMAE does not use inputs_embeds')
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__ , a__: Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__: Union[str, Any] = model_class(lowercase)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
a__: str = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase , nn.Linear))
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__ , a__: Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__: Any = model_class(lowercase)
a__: int = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__: Optional[Any] = [*signature.parameters.keys()]
a__: Tuple = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase)
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
a__: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase)
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowercase)
@slow
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__: int = VideoMAEModel.from_pretrained(lowercase)
self.assertIsNotNone(lowercase)
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
if not self.has_attentions:
pass
else:
a__ , a__: Any = self.model_tester.prepare_config_and_inputs_for_common()
a__: str = True
for model_class in self.all_model_classes:
a__: Optional[int] = self.model_tester.seq_length - self.model_tester.num_masks
a__: List[str] = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
a__: Tuple = True
a__: str = False
a__: Dict = True
a__: List[Any] = model_class(lowercase)
model.to(lowercase)
model.eval()
with torch.no_grad():
a__: int = model(**self._prepare_for_class(lowercase , lowercase))
a__: Any = outputs.attentions
self.assertEqual(len(lowercase) , self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
a__: Tuple = True
a__: List[Any] = model_class(lowercase)
model.to(lowercase)
model.eval()
with torch.no_grad():
a__: str = model(**self._prepare_for_class(lowercase , lowercase))
a__: int = outputs.attentions
self.assertEqual(len(lowercase) , self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
a__: Optional[Any] = len(lowercase)
# Check attention is always last and order is fine
a__: str = True
a__: Dict = True
a__: Tuple = model_class(lowercase)
model.to(lowercase)
model.eval()
with torch.no_grad():
a__: Optional[Any] = model(**self._prepare_for_class(lowercase , lowercase))
self.assertEqual(out_len + 1 , len(lowercase))
a__: int = outputs.attentions
self.assertEqual(len(lowercase) , self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
def check_hidden_states_output(lowercase , lowercase , lowercase):
a__: Union[str, Any] = model_class(lowercase)
model.to(lowercase)
model.eval()
with torch.no_grad():
a__: Tuple = model(**self._prepare_for_class(lowercase , lowercase))
a__: Dict = outputs.hidden_states
a__: Union[str, Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(lowercase) , lowercase)
a__: Union[str, Any] = self.model_tester.seq_length - self.model_tester.num_masks
a__: Optional[Any] = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [seq_length, self.model_tester.hidden_size] , )
a__ , a__: List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__: Dict = True
check_hidden_states_output(lowercase , lowercase , lowercase)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a__: List[Any] = True
check_hidden_states_output(lowercase , lowercase , lowercase)
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
pass
def __a ( ) ->List[Any]:
a__: List[str] = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
a__: Dict = np.load(_SCREAMING_SNAKE_CASE )
return list(_SCREAMING_SNAKE_CASE )
@require_torch
@require_vision
class __snake_case ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5])
if is_vision_available()
else None
)
@slow
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
a__: Dict = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics').to(
lowercase)
a__: Dict = self.default_image_processor
a__: str = prepare_video()
a__: Tuple = image_processor(lowercase , return_tensors='pt').to(lowercase)
# forward pass
with torch.no_grad():
a__: List[Any] = model(**lowercase)
# verify the logits
a__: str = torch.Size((1, 4_00))
self.assertEqual(outputs.logits.shape , lowercase)
a__: Optional[Any] = torch.tensor([0.3669, -0.0688, -0.2421]).to(lowercase)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase , atol=1e-4))
@slow
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: Tuple = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short').to(lowercase)
a__: Optional[Any] = self.default_image_processor
a__: List[Any] = prepare_video()
a__: Union[str, Any] = image_processor(lowercase , return_tensors='pt').to(lowercase)
# add boolean mask, indicating which patches to mask
a__: Optional[Any] = hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos' , filename='bool_masked_pos.pt')
a__: Any = torch.load(lowercase)
# forward pass
with torch.no_grad():
a__: Any = model(**lowercase)
# verify the logits
a__: Union[str, Any] = torch.Size([1, 14_08, 15_36])
a__: Union[str, Any] = torch.tensor(
[[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]] , device=lowercase)
self.assertEqual(outputs.logits.shape , lowercase)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , lowercase , atol=1e-4))
# verify the loss (`config.norm_pix_loss` = `True`)
a__: Optional[int] = torch.tensor([0.5142] , device=lowercase)
self.assertTrue(torch.allclose(outputs.loss , lowercase , atol=1e-4))
# verify the loss (`config.norm_pix_loss` = `False`)
a__: int = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short' , norm_pix_loss=lowercase).to(
lowercase)
with torch.no_grad():
a__: Union[str, Any] = model(**lowercase)
a__: Optional[int] = torch.tensor(torch.tensor([0.6469]) , device=lowercase)
self.assertTrue(torch.allclose(outputs.loss , lowercase , atol=1e-4))
| 203
| 1
|
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class __lowerCAmelCase ( A ):
def __init__( self : List[str] , *A : int , **A : int) -> None:
"""simple docstring"""
warnings.warn(
'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use FlavaImageProcessor instead.' , A , )
super().__init__(*A , **A)
| 339
|
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""simple docstring"""
return self.node_position[vertex]
def _lowerCamelCase ( self : Optional[Any] , A : Optional[int] , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = pos
def _lowerCamelCase ( self : Tuple , A : Tuple , A : Dict , A : List[str] , A : Optional[Any]) -> Dict:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCAmelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCAmelCase = 2 * start + 1
else:
_UpperCAmelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCAmelCase , _UpperCAmelCase = heap[smallest_child], positions[smallest_child]
_UpperCAmelCase , _UpperCAmelCase = (
heap[start],
positions[start],
)
_UpperCAmelCase , _UpperCAmelCase = temp, tempa
_UpperCAmelCase = self.get_position(positions[smallest_child])
self.set_position(
positions[smallest_child] , self.get_position(positions[start]))
self.set_position(positions[start] , A)
self.top_to_bottom(A , A , A , A)
def _lowerCamelCase ( self : Optional[int] , A : str , A : Optional[Any] , A : Optional[int] , A : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = position[index]
while index != 0:
_UpperCAmelCase = int((index - 2) / 2) if index % 2 == 0 else int((index - 1) / 2)
if val < heap[parent]:
_UpperCAmelCase = heap[parent]
_UpperCAmelCase = position[parent]
self.set_position(position[parent] , A)
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , A)
break
_UpperCAmelCase = parent
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , 0)
def _lowerCamelCase ( self : Union[str, Any] , A : Optional[int] , A : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = len(A) // 2 - 1
for i in range(A , -1 , -1):
self.top_to_bottom(A , A , len(A) , A)
def _lowerCamelCase ( self : Optional[int] , A : int , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = positions[0]
_UpperCAmelCase = sys.maxsize
self.top_to_bottom(A , 0 , len(A) , A)
return temp
def A ( _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
_UpperCAmelCase = Heap()
_UpperCAmelCase = [0] * len(_UpperCAmelCase )
_UpperCAmelCase = [-1] * len(_UpperCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCAmelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCAmelCase = []
for vertex in range(len(_UpperCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCAmelCase )
heap.node_position.append(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = 1
_UpperCAmelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCAmelCase = 0
_UpperCAmelCase = distance
heap.heapify(_UpperCAmelCase , _UpperCAmelCase )
for _ in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase = heap.delete_minimum(_UpperCAmelCase , _UpperCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCAmelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCAmelCase )]
):
_UpperCAmelCase = distance
heap.bottom_to_top(
_UpperCAmelCase , heap.get_position(_UpperCAmelCase ) , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
UpperCAmelCase__ = int(input("Enter number of edges: ").strip())
UpperCAmelCase__ = defaultdict(list)
for _ in range(edges_number):
UpperCAmelCase__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 339
| 1
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : int = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[Any] = self.dummy_uncond_unet
__snake_case : Optional[int] = PNDMScheduler()
__snake_case : List[Any] = PNDMPipeline(unet=a_ , scheduler=a_ )
pndm.to(a_ )
pndm.set_progress_bar_config(disable=a_ )
__snake_case : List[str] = torch.manual_seed(0 )
__snake_case : Dict = pndm(generator=a_ , num_inference_steps=20 , output_type='''numpy''' ).images
__snake_case : List[Any] = torch.manual_seed(0 )
__snake_case : List[str] = pndm(generator=a_ , num_inference_steps=20 , output_type='''numpy''' , return_dict=a_ )[0]
__snake_case : Any = image[0, -3:, -3:, -1]
__snake_case : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__snake_case : str = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : str = '''google/ddpm-cifar10-32'''
__snake_case : str = UNetaDModel.from_pretrained(a_ )
__snake_case : List[Any] = PNDMScheduler()
__snake_case : Optional[Any] = PNDMPipeline(unet=a_ , scheduler=a_ )
pndm.to(a_ )
pndm.set_progress_bar_config(disable=a_ )
__snake_case : Optional[Any] = torch.manual_seed(0 )
__snake_case : str = pndm(generator=a_ , output_type='''numpy''' ).images
__snake_case : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__snake_case : List[Any] = np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 24
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[int] = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ ='lxmert'
lowerCamelCase__ ={}
def __init__(self , a_=3_05_22 , a_=7_68 , a_=12 , a_=95_00 , a_=16_00 , a_=4_00 , a_=30_72 , a_="gelu" , a_=0.1 , a_=0.1 , a_=5_12 , a_=2 , a_=0.02 , a_=1E-12 , a_=9 , a_=5 , a_=5 , a_=20_48 , a_=4 , a_=6.67 , a_=True , a_=True , a_=True , a_=True , a_=True , a_=True , a_=True , **a_ , ):
'''simple docstring'''
__snake_case : Optional[int] = vocab_size
__snake_case : List[str] = hidden_size
__snake_case : List[Any] = num_attention_heads
__snake_case : int = hidden_act
__snake_case : int = intermediate_size
__snake_case : Any = hidden_dropout_prob
__snake_case : List[Any] = attention_probs_dropout_prob
__snake_case : Tuple = max_position_embeddings
__snake_case : List[str] = type_vocab_size
__snake_case : str = initializer_range
__snake_case : Tuple = layer_norm_eps
__snake_case : List[Any] = num_qa_labels
__snake_case : int = num_object_labels
__snake_case : Optional[Any] = num_attr_labels
__snake_case : Union[str, Any] = l_layers
__snake_case : Optional[int] = x_layers
__snake_case : Optional[int] = r_layers
__snake_case : Tuple = visual_feat_dim
__snake_case : Optional[int] = visual_pos_dim
__snake_case : Dict = visual_loss_normalizer
__snake_case : str = task_matched
__snake_case : Optional[Any] = task_mask_lm
__snake_case : List[str] = task_obj_predict
__snake_case : Optional[Any] = task_qa
__snake_case : Any = visual_obj_loss
__snake_case : int = visual_attr_loss
__snake_case : List[Any] = visual_feat_loss
__snake_case : Optional[Any] = {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
super().__init__(**a_ )
| 24
| 1
|
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.g4dn.xlarge",
"results": {"train_runtime": 6_5_0, "eval_accuracy": 0.6, "eval_loss": 0.9},
},
{
"framework": "tensorflow",
"script": "run_tf.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.g4dn.xlarge",
"results": {"train_runtime": 6_0_0, "eval_accuracy": 0.3, "eval_loss": 0.9},
},
] )
class A ( unittest.TestCase ):
'''simple docstring'''
def a_ (self ) -> Optional[Any]:
if self.framework == "pytorch":
subprocess.run(
f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split() , encoding="utf-8" , check=_A , )
assert hasattr(self , "env" )
def a_ (self , _UpperCAmelCase=1 ) -> Dict:
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"{self.env.base_job_name}-single" , instance_count=_A , instance_type=self.instance_type , debugger_hook_config=_A , hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path} , metric_definitions=self.env.metric_definitions , py_version="py36" , )
def a_ (self , _UpperCAmelCase ) -> List[Any]:
TrainingJobAnalytics(_A ).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv" )
def a_ (self ) -> List[str]:
__UpperCamelCase : Union[str, Any] = self.create_estimator()
# run training
estimator.fit()
# result dataframe
__UpperCamelCase : Optional[int] = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
__UpperCamelCase : Union[str, Any] = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"] )
__UpperCamelCase : Optional[int] = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
__UpperCamelCase : Tuple = (
Session().describe_training_job(estimator.latest_training_job.name ).get("TrainingTimeInSeconds" , 9_9_9_9_9_9 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy )
assert all(t <= self.results["eval_loss"] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"{estimator.latest_training_job.name}.json" , "w" ) as outfile:
json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss} , _A )
| 298
|
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url ).json()[0]['''urls'''][0]['''src''']
return requests.get(UpperCamelCase__ ).content
if __name__ == "__main__":
__A : Union[str, Any] = input("Enter Video/IGTV url: ").strip()
__A : Tuple = F'{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'
with open(file_name, "wb") as fp:
fp.write(download_video(url))
print(F'Done. Video saved to disk as {file_name}.')
| 273
| 0
|
"""simple docstring"""
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class lowerCAmelCase__ ( unittest.TestCase ):
def __a ( self : Union[str, Any] , snake_case__ : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = 3
UpperCAmelCase__ : Optional[int] = 2_5_0
UpperCAmelCase__ : Union[str, Any] = ids_tensor((batch_size, length) , lowercase_ )
UpperCAmelCase__ : Dict = torch.ones((batch_size, length) , device=lowercase_ , dtype=torch.float ) / length
return input_ids, scores
def __a ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Dict = self._get_tensors(5 )
UpperCAmelCase__ : Any = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=1_0 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = self._get_tensors(9 )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase__ , UpperCAmelCase__ : int = self._get_tensors(1_0 )
self.assertTrue(criteria(lowercase_ , lowercase_ ) )
def __a ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = MaxLengthCriteria(max_length=1_0 )
UpperCAmelCase__ , UpperCAmelCase__ : str = self._get_tensors(5 )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = self._get_tensors(9 )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = self._get_tensors(1_0 )
self.assertTrue(criteria(lowercase_ , lowercase_ ) )
def __a ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Any = MaxNewTokensCriteria(start_length=5 , max_new_tokens=5 )
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = self._get_tensors(5 )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase__ , UpperCAmelCase__ : int = self._get_tensors(9 )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = self._get_tensors(1_0 )
self.assertTrue(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase__ : int = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length , 1_0 )
def __a ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = self._get_tensors(5 )
UpperCAmelCase__ : List[str] = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase__ : int = MaxTimeCriteria(max_time=0.1 , initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(lowercase_ , lowercase_ ) )
def __a ( self : List[str] ):
'''simple docstring'''
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0 )] ) , 1_0 )
with self.assertWarns(lowercase_ ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0 )] ) , 1_1 )
UpperCAmelCase__ : Tuple = validate_stopping_criteria(StoppingCriteriaList() , 1_1 )
self.assertEqual(len(lowercase_ ) , 1 )
| 356
|
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : Optional[int] = {
"""vocab_file""": """vocab.txt""",
"""merges_file""": """bpe.codes""",
}
_lowerCAmelCase : List[Any] = {
"""vocab_file""": {
"""vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
"""vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
},
"""merges_file""": {
"""vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
"""vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
},
}
_lowerCAmelCase : int = {
"""vinai/phobert-base""": 256,
"""vinai/phobert-large""": 256,
}
def SCREAMING_SNAKE_CASE__ ( snake_case : List[Any] )-> str:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = set()
UpperCAmelCase__ : Optional[int] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCAmelCase__ : Dict = char
UpperCAmelCase__ : Tuple = set(snake_case )
return pairs
class lowerCAmelCase__ ( __magic_name__ ):
SCREAMING_SNAKE_CASE_ =VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ =PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : List[Any] , snake_case__ : int , snake_case__ : Union[str, Any] , snake_case__ : Tuple="<s>" , snake_case__ : List[Any]="</s>" , snake_case__ : Union[str, Any]="</s>" , snake_case__ : Union[str, Any]="<s>" , snake_case__ : Any="<unk>" , snake_case__ : int="<pad>" , snake_case__ : List[str]="<mask>" , **snake_case__ : Optional[int] , ):
'''simple docstring'''
super().__init__(
bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , cls_token=snake_case__ , pad_token=snake_case__ , mask_token=snake_case__ , **snake_case__ , )
UpperCAmelCase__ : Dict = vocab_file
UpperCAmelCase__ : Tuple = merges_file
UpperCAmelCase__ : List[Any] = {}
UpperCAmelCase__ : Dict = 0
UpperCAmelCase__ : int = 1
UpperCAmelCase__ : Dict = 2
UpperCAmelCase__ : Dict = 3
self.add_from_file(snake_case__ )
UpperCAmelCase__ : Optional[Any] = {v: k for k, v in self.encoder.items()}
with open(snake_case__ , encoding="utf-8" ) as merges_handle:
UpperCAmelCase__ : Tuple = merges_handle.read().split("\n" )[:-1]
UpperCAmelCase__ : Optional[Any] = [tuple(merge.split()[:-1] ) for merge in merges]
UpperCAmelCase__ : List[Any] = dict(zip(snake_case__ , range(len(snake_case__ ) ) ) )
UpperCAmelCase__ : Dict = {}
def __a ( self : int , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase__ : str = [self.cls_token_id]
UpperCAmelCase__ : Any = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __a ( self : List[str] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None , snake_case__ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case__ , token_ids_a=snake_case__ , already_has_special_tokens=snake_case__ )
if token_ids_a is None:
return [1] + ([0] * len(snake_case__ )) + [1]
return [1] + ([0] * len(snake_case__ )) + [1, 1] + ([0] * len(snake_case__ )) + [1]
def __a ( self : Union[str, Any] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [self.sep_token_id]
UpperCAmelCase__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def __a ( self : List[str] ):
'''simple docstring'''
return len(self.encoder )
def __a ( self : Any ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def __a ( self : Dict , snake_case__ : Tuple ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
UpperCAmelCase__ : Optional[Any] = tuple(snake_case__ )
UpperCAmelCase__ : Optional[Any] = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
UpperCAmelCase__ : Any = get_pairs(snake_case__ )
if not pairs:
return token
while True:
UpperCAmelCase__ : List[Any] = min(snake_case__ , key=lambda snake_case__ : self.bpe_ranks.get(snake_case__ , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = bigram
UpperCAmelCase__ : Optional[Any] = []
UpperCAmelCase__ : Tuple = 0
while i < len(snake_case__ ):
try:
UpperCAmelCase__ : Union[str, Any] = word.index(snake_case__ , snake_case__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
UpperCAmelCase__ : Dict = j
if word[i] == first and i < len(snake_case__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
UpperCAmelCase__ : Dict = tuple(snake_case__ )
UpperCAmelCase__ : List[Any] = new_word
if len(snake_case__ ) == 1:
break
else:
UpperCAmelCase__ : Dict = get_pairs(snake_case__ )
UpperCAmelCase__ : List[Any] = "@@ ".join(snake_case__ )
UpperCAmelCase__ : Optional[int] = word[:-4]
UpperCAmelCase__ : Union[str, Any] = word
return word
def __a ( self : List[Any] , snake_case__ : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : int = re.findall(R"\S+\n?" , snake_case__ )
for token in words:
split_tokens.extend(list(self.bpe(snake_case__ ).split(" " ) ) )
return split_tokens
def __a ( self : Dict , snake_case__ : List[str] ):
'''simple docstring'''
return self.encoder.get(snake_case__ , self.encoder.get(self.unk_token ) )
def __a ( self : List[Any] , snake_case__ : Any ):
'''simple docstring'''
return self.decoder.get(snake_case__ , self.unk_token )
def __a ( self : str , snake_case__ : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = " ".join(snake_case__ ).replace("@@ " , "" ).strip()
return out_string
def __a ( self : Any , snake_case__ : str , snake_case__ : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(snake_case__ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
UpperCAmelCase__ : Tuple = os.path.join(
snake_case__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase__ : str = os.path.join(
snake_case__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
copyfile(self.vocab_file , snake_case__ )
if os.path.abspath(self.merges_file ) != os.path.abspath(snake_case__ ):
copyfile(self.merges_file , snake_case__ )
return out_vocab_file, out_merge_file
def __a ( self : List[Any] , snake_case__ : Union[str, Any] ):
'''simple docstring'''
if isinstance(snake_case__ , snake_case__ ):
try:
with open(snake_case__ , "r" , encoding="utf-8" ) as fd:
self.add_from_file(snake_case__ )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f'Incorrect encoding detected in {f}, please rebuild the dataset' )
return
UpperCAmelCase__ : Dict = f.readlines()
for lineTmp in lines:
UpperCAmelCase__ : Optional[int] = lineTmp.strip()
UpperCAmelCase__ : Tuple = line.rfind(" " )
if idx == -1:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'" )
UpperCAmelCase__ : Any = line[:idx]
UpperCAmelCase__ : str = len(self.encoder )
| 298
| 0
|
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_)
class _lowercase ( lowerCamelCase_):
"""simple docstring"""
A__ = field(default="summarization" , metadata={"include_in_asdict_even_if_is_default": True})
A__ = Features({"text": Value("string")})
A__ = Features({"summary": Value("string")})
A__ = "text"
A__ = "summary"
@property
def lowerCAmelCase ( self : Any ):
'''simple docstring'''
return {self.text_column: "text", self.summary_column: "summary"}
| 184
|
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCamelCase =16
_lowerCamelCase =32
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = 16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('bert-base-cased' )
SCREAMING_SNAKE_CASE =load_dataset('glue', 'mrpc' )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE =tokenizer(examples['sentence1'], examples['sentence2'], truncation=lowerCAmelCase_, max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE =datasets.map(
lowerCAmelCase_, batched=lowerCAmelCase_, remove_columns=['idx', 'sentence1', 'sentence2'], )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE =tokenized_datasets.rename_column('label', 'labels' )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE =128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE =16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE =8
else:
SCREAMING_SNAKE_CASE =None
return tokenizer.pad(
lowerCAmelCase_, padding='longest', max_length=lowerCAmelCase_, pad_to_multiple_of=lowerCAmelCase_, return_tensors='pt', )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['train'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['validation'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowerCamelCase =mocked_dataloaders # noqa: F811
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if os.environ.get('TESTING_MOCKED_DATALOADERS', lowerCAmelCase_ ) == "1":
SCREAMING_SNAKE_CASE =2
# Initialize accelerator
SCREAMING_SNAKE_CASE =Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE =config['lr']
SCREAMING_SNAKE_CASE =int(config['num_epochs'] )
SCREAMING_SNAKE_CASE =int(config['seed'] )
SCREAMING_SNAKE_CASE =int(config['batch_size'] )
SCREAMING_SNAKE_CASE =evaluate.load('glue', 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowerCAmelCase_ )
def inner_training_loop(lowerCAmelCase_ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowerCAmelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE =AutoModelForSequenceClassification.from_pretrained('bert-base-cased', return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE =model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE =AdamW(params=model.parameters(), lr=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =get_dataloaders(lowerCAmelCase_, lowerCAmelCase_ )
# Instantiate scheduler
SCREAMING_SNAKE_CASE =get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_, num_warmup_steps=100, num_training_steps=(len(lowerCAmelCase_ ) * num_epochs), )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.loss
accelerator.backward(lowerCAmelCase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowerCAmelCase_, references=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:', lowerCAmelCase_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision', type=lowerCAmelCase_, default=lowerCAmelCase_, choices=['no', 'fp16', 'bf16', 'fp8'], help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.', )
parser.add_argument('--cpu', action='store_true', help='If passed, will train on the CPU.' )
SCREAMING_SNAKE_CASE =parser.parse_args()
SCREAMING_SNAKE_CASE ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowerCAmelCase_, lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 334
| 0
|
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class a ( _lowerCamelCase ):
def __init__( self : Dict , lowercase_ : List[str] , lowercase_ : Optional[Any] ):
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : Optional[int] , lowercase_ : int = 1 , lowercase_ : Optional[torch.Generator] = None , lowercase_ : int = 50 , lowercase_ : Optional[str] = "pil" , lowercase_ : bool = True , **lowercase_ : int , ):
snake_case_ = torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=lowercase_ , )
snake_case_ = image.to(self.device )
# set step values
self.scheduler.set_timesteps(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
snake_case_ = self.unet(lowercase_ , lowercase_ ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
snake_case_ = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
snake_case_ = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=lowercase_ ), "This is a local test"
| 72
|
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a ( unittest.TestCase ):
@property
def A_ ( self : Tuple ):
torch.manual_seed(0 )
snake_case_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def A_ ( self : Dict ):
snake_case_ = self.dummy_uncond_unet
snake_case_ = ScoreSdeVeScheduler()
snake_case_ = ScoreSdeVePipeline(unet=lowercase_ , scheduler=lowercase_ )
sde_ve.to(lowercase_ )
sde_ve.set_progress_bar_config(disable=lowercase_ )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='''numpy''' , generator=lowercase_ ).images
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='''numpy''' , generator=lowercase_ , return_dict=lowercase_ )[
0
]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class a ( unittest.TestCase ):
def A_ ( self : Optional[int] ):
snake_case_ = '''google/ncsnpp-church-256'''
snake_case_ = UNetaDModel.from_pretrained(lowercase_ )
snake_case_ = ScoreSdeVeScheduler.from_pretrained(lowercase_ )
snake_case_ = ScoreSdeVePipeline(unet=lowercase_ , scheduler=lowercase_ )
sde_ve.to(lowercase_ )
sde_ve.set_progress_bar_config(disable=lowercase_ )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=10 , output_type='''numpy''' , generator=lowercase_ ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
snake_case_ = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 72
| 1
|
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
__snake_case = random.Random()
if is_torch_available():
import torch
def __lowerCAmelCase ( lowercase : Optional[int] , lowercase : str=1.0 , lowercase : Any=None , lowercase : str=None ) -> Union[str, Any]:
"""simple docstring"""
if rng is None:
snake_case : Any = global_rng
snake_case : List[Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class _lowerCAmelCase ( unittest.TestCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__=7 , UpperCamelCase__=400 , UpperCamelCase__=2000 , UpperCamelCase__=1 , UpperCamelCase__=0.0 , UpperCamelCase__=1_6000 , UpperCamelCase__=True , UpperCamelCase__=True , ) -> Dict:
'''simple docstring'''
snake_case : Union[str, Any] = parent
snake_case : Any = batch_size
snake_case : Any = min_seq_length
snake_case : int = max_seq_length
snake_case : Any = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
snake_case : str = feature_size
snake_case : Optional[int] = padding_value
snake_case : Optional[int] = sampling_rate
snake_case : List[Any] = return_attention_mask
snake_case : List[Any] = do_normalize
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowerCamelCase ( self , UpperCamelCase__=False , UpperCamelCase__=False ) -> Any:
'''simple docstring'''
def _flatten(UpperCamelCase__ ):
return list(itertools.chain(*UpperCamelCase__ ) )
if equal_length:
snake_case : Optional[Any] = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
snake_case : str = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
snake_case : Any = [np.asarray(UpperCamelCase__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class _lowerCAmelCase ( snake_case_ , unittest.TestCase ):
__UpperCAmelCase : Tuple = ASTFeatureExtractor
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
snake_case : Tuple = ASTFeatureExtractionTester(self )
def lowerCamelCase ( self ) -> int:
'''simple docstring'''
snake_case : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
snake_case : Tuple = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
snake_case : Tuple = [np.asarray(UpperCamelCase__ ) for speech_input in speech_inputs]
# Test not batched input
snake_case : Union[str, Any] = feat_extract(speech_inputs[0] , return_tensors="np" ).input_values
snake_case : Union[str, Any] = feat_extract(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 ) )
# Test batched
snake_case : List[str] = feat_extract(UpperCamelCase__ , padding=UpperCamelCase__ , return_tensors="np" ).input_values
snake_case : Tuple = feat_extract(UpperCamelCase__ , padding=UpperCamelCase__ , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
snake_case : Union[str, Any] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
snake_case : List[str] = np.asarray(UpperCamelCase__ )
snake_case : Optional[int] = feat_extract(UpperCamelCase__ , return_tensors="np" ).input_values
snake_case : Any = feat_extract(UpperCamelCase__ , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 ) )
@require_torch
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
import torch
snake_case : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
snake_case : Optional[int] = np.random.rand(100 ).astype(np.floataa )
snake_case : Dict = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
snake_case : Union[str, Any] = feature_extractor.pad([{"input_values": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
snake_case : Tuple = feature_extractor.pad([{"input_values": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def lowerCamelCase ( self , UpperCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
from datasets import load_dataset
snake_case : Optional[Any] = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
snake_case : Optional[int] = ds.sort("id" ).select(range(UpperCamelCase__ ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
@require_torch
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
snake_case : List[str] = torch.tensor(
[-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
-1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
-1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
-0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869] )
# fmt: on
snake_case : Dict = self._load_datasamples(1 )
snake_case : Union[str, Any] = ASTFeatureExtractor()
snake_case : Optional[Any] = feature_extractor(UpperCamelCase__ , return_tensors="pt" ).input_values
self.assertEquals(input_values.shape , (1, 1024, 128) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , UpperCamelCase__ , atol=1e-4 ) )
| 203
|
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowerCAmelCase :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=12 , UpperCamelCase__=7 , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=99 , UpperCamelCase__=32 , UpperCamelCase__=32 , UpperCamelCase__=2 , UpperCamelCase__=4 , UpperCamelCase__=37 , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=512 , UpperCamelCase__=0.02 , UpperCamelCase__=0 , UpperCamelCase__=None , ) -> Dict:
'''simple docstring'''
snake_case : Optional[Any] = parent
snake_case : Dict = batch_size
snake_case : List[str] = seq_length
snake_case : Dict = is_training
snake_case : Optional[Any] = use_input_mask
snake_case : Optional[int] = use_labels
snake_case : Tuple = vocab_size
snake_case : Optional[Any] = hidden_size
snake_case : Optional[Any] = projection_dim
snake_case : List[Any] = num_hidden_layers
snake_case : List[Any] = num_attention_heads
snake_case : int = intermediate_size
snake_case : str = dropout
snake_case : List[Any] = attention_dropout
snake_case : Any = max_position_embeddings
snake_case : List[Any] = initializer_range
snake_case : Any = scope
snake_case : Union[str, Any] = bos_token_id
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
snake_case : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case : int = None
if self.use_input_mask:
snake_case : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
snake_case : Tuple = input_mask.numpy()
snake_case ,snake_case : str = input_mask.shape
snake_case : Tuple = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(UpperCamelCase__ ):
snake_case : int = 1
snake_case : Tuple = 0
snake_case : Union[str, Any] = self.get_config()
return config, input_ids, tf.convert_to_tensor(UpperCamelCase__ )
def lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
snake_case : str = TFBlipTextModel(config=UpperCamelCase__ )
snake_case : Any = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , training=UpperCamelCase__ )
snake_case : Optional[int] = model(UpperCamelCase__ , training=UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
snake_case : Tuple = self.prepare_config_and_inputs()
snake_case ,snake_case ,snake_case : Tuple = config_and_inputs
snake_case : str = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class _lowerCAmelCase ( snake_case_ , unittest.TestCase ):
__UpperCAmelCase : Any = (TFBlipTextModel,) if is_tf_available() else ()
__UpperCAmelCase : Any = False
__UpperCAmelCase : Dict = False
__UpperCAmelCase : List[Any] = False
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
snake_case : List[Any] = BlipTextModelTester(self )
snake_case : Optional[int] = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=37 )
def lowerCamelCase ( self ) -> int:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def lowerCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip(reason="Blip does not use inputs_embeds" )
def lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
pass
@slow
def lowerCamelCase ( self ) -> int:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case : List[str] = TFBlipTextModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def lowerCamelCase ( self , UpperCamelCase__=True ) -> Optional[int]:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__ )
| 203
| 1
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=UpperCamelCase ):
snake_case_ = ['onnx']
def __init__( self : str , *snake_case : Optional[int] , **snake_case : int ):
'''simple docstring'''
requires_backends(self , ["""onnx"""] )
@classmethod
def _UpperCamelCase ( cls : int , *snake_case : Optional[int] , **snake_case : List[str] ):
'''simple docstring'''
requires_backends(cls , ["""onnx"""] )
@classmethod
def _UpperCamelCase ( cls : Any , *snake_case : Dict , **snake_case : List[Any] ):
'''simple docstring'''
requires_backends(cls , ["""onnx"""] )
| 296
|
"""simple docstring"""
from __future__ import annotations
class __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , snake_case : int ):
'''simple docstring'''
A__ : List[Any] = order
# a_{0} ... a_{k}
A__ : List[Any] = [1.0] + [0.0] * order
# b_{0} ... b_{k}
A__ : str = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
A__ : Union[str, Any] = [0.0] * self.order
# y[n-1] ... y[n-k]
A__ : List[str] = [0.0] * self.order
def _UpperCamelCase ( self : Optional[int] , snake_case : list[float] , snake_case : list[float] ):
'''simple docstring'''
if len(snake_case ) < self.order:
A__ : Any = [1.0, *a_coeffs]
if len(snake_case ) != self.order + 1:
A__ : str = (
F'Expected a_coeffs to have {self.order + 1} elements '
F'for {self.order}-order filter, got {len(snake_case )}'
)
raise ValueError(snake_case )
if len(snake_case ) != self.order + 1:
A__ : Union[str, Any] = (
F'Expected b_coeffs to have {self.order + 1} elements '
F'for {self.order}-order filter, got {len(snake_case )}'
)
raise ValueError(snake_case )
A__ : Dict = a_coeffs
A__ : Any = b_coeffs
def _UpperCamelCase ( self : List[str] , snake_case : float ):
'''simple docstring'''
A__ : str = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
A__ : Dict = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
A__ : Tuple = self.input_history[:-1]
A__ : int = self.output_history[:-1]
A__ : Dict = sample
A__ : Tuple = result
return result
| 296
| 1
|
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@property
def a (self : Dict ):
"""simple docstring"""
torch.manual_seed(0 )
__snake_case = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
@property
def a (self : int ):
"""simple docstring"""
torch.manual_seed(0 )
__snake_case = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=3 , )
return model
@property
def a (self : List[str] ):
"""simple docstring"""
torch.manual_seed(0 )
__snake_case = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModel(a__ )
def a (self : int ):
"""simple docstring"""
__snake_case = self.dummy_uncond_unet
__snake_case = DDIMScheduler()
__snake_case = self.dummy_vq_model
__snake_case = LDMPipeline(unet=a__ , vqvae=a__ , scheduler=a__ )
ldm.to(a__ )
ldm.set_progress_bar_config(disable=a__ )
__snake_case = torch.manual_seed(0 )
__snake_case = ldm(generator=a__ , num_inference_steps=2 , output_type='''numpy''' ).images
__snake_case = torch.manual_seed(0 )
__snake_case = ldm(generator=a__ , num_inference_steps=2 , output_type='''numpy''' , return_dict=a__ )[0]
__snake_case = image[0, -3:, -3:, -1]
__snake_case = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__snake_case = np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2] )
__snake_case = 1E-2 if torch_device != '''mps''' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def a (self : Optional[int] ):
"""simple docstring"""
__snake_case = LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256''' )
ldm.to(a__ )
ldm.set_progress_bar_config(disable=a__ )
__snake_case = torch.manual_seed(0 )
__snake_case = ldm(generator=a__ , num_inference_steps=5 , output_type='''numpy''' ).images
__snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
__snake_case = np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7] )
__snake_case = 1E-2 if torch_device != '''mps''' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 24
|
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCamelCase__ ( ) -> Any:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
__snake_case = '''__test_patch_submodule_mock__'''
with patch_submodule(_test_patching , '''os.path.join''' , snake_case_ ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def lowerCamelCase__ ( ) -> Any:
assert _test_patching.open is open
__snake_case = '''__test_patch_submodule_builtin_mock__'''
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , '''open''' , snake_case_ ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def lowerCamelCase__ ( ) -> List[str]:
# pandas.read_csv is not present in _test_patching
__snake_case = '''__test_patch_submodule_missing_mock__'''
with patch_submodule(_test_patching , '''pandas.read_csv''' , snake_case_ ):
pass
def lowerCamelCase__ ( ) -> Union[str, Any]:
# builtin should always be mocked even if they're not in the globals
# in case they're loaded at one point
__snake_case = '''__test_patch_submodule_missing_builtin_mock__'''
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , '''len''' , snake_case_ ) is None
with patch_submodule(_test_patching , '''len''' , snake_case_ ):
assert _test_patching.len is mock
assert _test_patching.len is len
def lowerCamelCase__ ( ) -> Union[str, Any]:
__snake_case = '''__test_patch_submodule_start_and_stop_mock__'''
__snake_case = patch_submodule(_test_patching , '''open''' , snake_case_ )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def lowerCamelCase__ ( ) -> Optional[int]:
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
__snake_case = '''__test_patch_submodule_successive_join__'''
__snake_case = '''__test_patch_submodule_successive_dirname__'''
__snake_case = '''__test_patch_submodule_successive_rename__'''
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , '''os.path.join''' , snake_case_ ):
with patch_submodule(_test_patching , '''os.rename''' , snake_case_ ):
with patch_submodule(_test_patching , '''os.path.dirname''' , snake_case_ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , '''os.rename''' , snake_case_ ):
with patch_submodule(_test_patching , '''os.path.join''' , snake_case_ ):
with patch_submodule(_test_patching , '''os.path.dirname''' , snake_case_ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def lowerCamelCase__ ( ) -> Tuple:
__snake_case = '''__test_patch_submodule_doesnt_exist_mock__'''
with patch_submodule(_test_patching , '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''' , snake_case_ ):
pass
with patch_submodule(_test_patching , '''os.__attribute_that_doesn_exist__''' , snake_case_ ):
pass
| 24
| 1
|
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class lowerCAmelCase__ :
def __init__( self : List[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : List[Any]=2 , _lowerCamelCase : List[str]=True , _lowerCamelCase : Optional[Any]=False , _lowerCamelCase : Optional[Any]=10 , _lowerCamelCase : Dict=3 , _lowerCamelCase : Optional[int]=32 * 8 , _lowerCamelCase : Optional[int]=32 * 8 , _lowerCamelCase : Dict=4 , _lowerCamelCase : Optional[int]=64 , ):
_snake_case = parent
_snake_case = batch_size
_snake_case = is_training
_snake_case = use_auxiliary_loss
_snake_case = num_queries
_snake_case = num_channels
_snake_case = min_size
_snake_case = max_size
_snake_case = num_labels
_snake_case = hidden_dim
_snake_case = hidden_dim
def lowercase ( self : List[str] ):
_snake_case = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCamelCase )
_snake_case = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCamelCase )
_snake_case = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCamelCase ) > 0.5
).float()
_snake_case = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCamelCase ) > 0.5).long()
_snake_case = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase ( self : Optional[Any] ):
_snake_case = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
_snake_case = self.num_queries
_snake_case = self.num_labels
_snake_case = [1, 1, 1, 1]
_snake_case = self.num_channels
_snake_case = 64
_snake_case = 128
_snake_case = self.hidden_dim
_snake_case = self.hidden_dim
_snake_case = self.hidden_dim
return config
def lowercase ( self : Any ):
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case = self.prepare_config_and_inputs()
_snake_case = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def lowercase ( self : Union[str, Any] , _lowerCamelCase : List[Any] , _lowerCamelCase : int ):
_snake_case = output.encoder_hidden_states
_snake_case = output.pixel_decoder_hidden_states
_snake_case = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , config.decoder_layers )
def lowercase ( self : List[Any] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Any , _lowerCamelCase : List[Any] , _lowerCamelCase : Dict=False ):
with torch.no_grad():
_snake_case = MaskaFormerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
_snake_case = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
_snake_case = model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCamelCase , _lowerCamelCase )
def lowercase ( self : int , _lowerCamelCase : Dict , _lowerCamelCase : List[Any] , _lowerCamelCase : Any , _lowerCamelCase : str , _lowerCamelCase : Union[str, Any] ):
_snake_case = MaskaFormerForUniversalSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
def comm_check_on_output(_lowerCamelCase : List[str] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_snake_case = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
_snake_case = model(_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
_snake_case = model(
pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class lowerCAmelCase__ ( A_ , A_ , unittest.TestCase ):
__a = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
__a = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}
__a = False
__a = False
__a = False
__a = False
def lowercase ( self : int ):
_snake_case = MaskaFormerModelTester(self )
_snake_case = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def lowercase ( self : Dict ):
self.config_tester.run_common_tests()
def lowercase ( self : Tuple ):
_snake_case , _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def lowercase ( self : Any ):
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_lowerCamelCase )
@unittest.skip(reason='''Mask2Former does not use inputs_embeds''' )
def lowercase ( self : Optional[int] ):
pass
@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method''' )
def lowercase ( self : Dict ):
pass
@unittest.skip(reason='''Mask2Former is not a generative model''' )
def lowercase ( self : int ):
pass
@unittest.skip(reason='''Mask2Former does not use token embeddings''' )
def lowercase ( self : List[str] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def lowercase ( self : Optional[int] ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def lowercase ( self : str ):
pass
def lowercase ( self : Optional[int] ):
_snake_case , _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case = model_class(_lowerCamelCase )
_snake_case = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_snake_case = [*signature.parameters.keys()]
_snake_case = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@slow
def lowercase ( self : Optional[int] ):
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
_snake_case = MaskaFormerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def lowercase ( self : List[Any] ):
_snake_case = (self.model_tester.min_size,) * 2
_snake_case = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_lowerCamelCase ),
'''mask_labels''': torch.randn((2, 10, *size) , device=_lowerCamelCase ),
'''class_labels''': torch.zeros(2 , 10 , device=_lowerCamelCase ).long(),
}
_snake_case = self.model_tester.get_config()
_snake_case = MaskaFormerForUniversalSegmentation(_lowerCamelCase ).to(_lowerCamelCase )
_snake_case = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def lowercase ( self : Union[str, Any] ):
_snake_case , _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def lowercase ( self : str ):
_snake_case , _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case = model_class(_lowerCamelCase ).to(_lowerCamelCase )
_snake_case = model(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def lowercase ( self : str ):
if not self.model_tester.is_training:
return
_snake_case = self.all_model_classes[1]
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case = self.model_tester.prepare_config_and_inputs()
_snake_case = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
_snake_case = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase ).loss
loss.backward()
def lowercase ( self : Optional[int] ):
_snake_case = self.all_model_classes[1]
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case = self.model_tester.prepare_config_and_inputs()
_snake_case = True
_snake_case = True
_snake_case = model_class(_lowerCamelCase ).to(_lowerCamelCase )
model.train()
_snake_case = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
_snake_case = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_snake_case = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
_snake_case = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_snake_case = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
UpperCAmelCase__ = 1e-4
def _UpperCAmelCase ( ) -> Tuple:
_snake_case = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class lowerCAmelCase__ ( unittest.TestCase ):
@cached_property
def lowercase ( self : Optional[Any] ):
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def lowercase ( self : int ):
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def lowercase ( self : Any ):
_snake_case = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(_lowerCamelCase )
_snake_case = self.default_image_processor
_snake_case = prepare_img()
_snake_case = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
_snake_case = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 384, 384) )
with torch.no_grad():
_snake_case = model(**_lowerCamelCase )
_snake_case = torch.tensor(
[[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
_snake_case = torch.tensor(
[[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
_snake_case = torch.tensor(
[[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def lowercase ( self : str ):
_snake_case = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(_lowerCamelCase ).eval()
_snake_case = self.default_image_processor
_snake_case = prepare_img()
_snake_case = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
_snake_case = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 384, 384) )
with torch.no_grad():
_snake_case = model(**_lowerCamelCase )
# masks_queries_logits
_snake_case = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
_snake_case = [
[-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
[-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
[-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
]
_snake_case = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
_snake_case = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
_snake_case = torch.tensor(
[
[1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
[0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
[0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def lowercase ( self : Optional[int] ):
_snake_case = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(_lowerCamelCase ).eval()
_snake_case = self.default_image_processor
_snake_case = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='''pt''' , )
_snake_case = inputs['''pixel_values'''].to(_lowerCamelCase )
_snake_case = [el.to(_lowerCamelCase ) for el in inputs['''mask_labels''']]
_snake_case = [el.to(_lowerCamelCase ) for el in inputs['''class_labels''']]
with torch.no_grad():
_snake_case = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 40
|
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _UpperCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : dict ) -> str:
_snake_case = BeautifulSoup(requests.get(__lowerCamelCase , params=__lowerCamelCase ).content , '''html.parser''' )
_snake_case = soup.find('''div''' , attrs={'''class''': '''gs_ri'''} )
_snake_case = div.find('''div''' , attrs={'''class''': '''gs_fl'''} ).find_all('''a''' )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase__ = {
'title': (
'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
'capacitance, volumetric capacitance, and energy density'
),
'journal': 'Chem. Mater.',
'volume': 30,
'pages': '3979-3990',
'year': 2018,
'hl': 'en',
}
print(get_citation('https://scholar.google.com/scholar_lookup', params=params))
| 40
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'DistilBertConfig',
'DistilBertOnnxConfig',
],
'tokenization_distilbert': ['DistilBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = ['DistilBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DistilBertForMaskedLM',
'DistilBertForMultipleChoice',
'DistilBertForQuestionAnswering',
'DistilBertForSequenceClassification',
'DistilBertForTokenClassification',
'DistilBertModel',
'DistilBertPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDistilBertForMaskedLM',
'TFDistilBertForMultipleChoice',
'TFDistilBertForQuestionAnswering',
'TFDistilBertForSequenceClassification',
'TFDistilBertForTokenClassification',
'TFDistilBertMainLayer',
'TFDistilBertModel',
'TFDistilBertPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'FlaxDistilBertForMaskedLM',
'FlaxDistilBertForMultipleChoice',
'FlaxDistilBertForQuestionAnswering',
'FlaxDistilBertForSequenceClassification',
'FlaxDistilBertForTokenClassification',
'FlaxDistilBertModel',
'FlaxDistilBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 65
|
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class A ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def a_ (self ) -> Tuple:
return datasets.DatasetInfo(
features=datasets.Features({"content": datasets.Value("string" )} ) , supervised_keys=_UpperCAmelCase , )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]:
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_dummy_examples()} )]
def a_ (self , _UpperCAmelCase , _UpperCAmelCase ) -> int:
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_UpperCAmelCase )
class A ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def a_ (self ) -> str:
return datasets.DatasetInfo(
features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) , supervised_keys=_UpperCAmelCase , )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase ) -> Union[str, Any]:
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_nested_examples()} )
]
def a_ (self , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_UpperCAmelCase )
def __lowerCAmelCase ( ):
return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"] )]
def __lowerCAmelCase ( ):
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"] )]
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
@require_beam
def a_ (self ) -> Union[str, Any]:
__UpperCamelCase : Union[str, Any] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
__UpperCamelCase : str = DummyBeamDataset(cache_dir=_UpperCAmelCase , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_UpperCAmelCase , builder.name , "default" , "0.0.0" , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
__UpperCamelCase : Optional[int] = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , _UpperCAmelCase )
self.assertEqual(dset["train"].info.splits["train"].num_examples , _UpperCAmelCase )
self.assertDictEqual(dset["train"][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_UpperCAmelCase , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def a_ (self ) -> Optional[Any]:
import apache_beam as beam
__UpperCamelCase : Optional[int] = beam.io.parquetio.WriteToParquet
__UpperCamelCase : List[str] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
__UpperCamelCase : Optional[int] = DummyBeamDataset(cache_dir=_UpperCAmelCase , beam_runner="DirectRunner" )
with patch("apache_beam.io.parquetio.WriteToParquet" ) as write_parquet_mock:
__UpperCamelCase : List[str] = partial(_UpperCAmelCase , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
_UpperCAmelCase , builder.name , "default" , "0.0.0" , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
_UpperCAmelCase , builder.name , "default" , "0.0.0" , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
__UpperCamelCase : List[str] = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , _UpperCAmelCase )
self.assertEqual(dset["train"].info.splits["train"].num_examples , _UpperCAmelCase )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["train"]["content"] ) , sorted(["foo", "bar", "foobar"] ) )
self.assertTrue(
os.path.exists(os.path.join(_UpperCAmelCase , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def a_ (self ) -> str:
with tempfile.TemporaryDirectory() as tmp_cache_dir:
__UpperCamelCase : Optional[Any] = DummyBeamDataset(cache_dir=_UpperCAmelCase )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def a_ (self ) -> List[str]:
__UpperCamelCase : Tuple = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
__UpperCamelCase : str = NestedBeamDataset(cache_dir=_UpperCAmelCase , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_UpperCAmelCase , builder.name , "default" , "0.0.0" , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) )
__UpperCamelCase : Union[str, Any] = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , _UpperCAmelCase )
self.assertEqual(dset["train"].info.splits["train"].num_examples , _UpperCAmelCase )
self.assertDictEqual(dset["train"][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_UpperCAmelCase , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
| 298
| 0
|
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __UpperCamelCase ( _UpperCAmelCase ):
if "cls_token" in name:
__UpperCAmelCase : Tuple = name.replace("cls_token", "vit.embeddings.cls_token" )
if "mask_token" in name:
__UpperCAmelCase : str = name.replace("mask_token", "decoder.mask_token" )
if "decoder_pos_embed" in name:
__UpperCAmelCase : int = name.replace("decoder_pos_embed", "decoder.decoder_pos_embed" )
if "pos_embed" in name and "decoder" not in name:
__UpperCAmelCase : int = name.replace("pos_embed", "vit.embeddings.position_embeddings" )
if "patch_embed.proj" in name:
__UpperCAmelCase : int = name.replace("patch_embed.proj", "vit.embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
__UpperCAmelCase : Union[str, Any] = name.replace("patch_embed.norm", "vit.embeddings.norm" )
if "decoder_blocks" in name:
__UpperCAmelCase : int = name.replace("decoder_blocks", "decoder.decoder_layers" )
if "blocks" in name:
__UpperCAmelCase : Union[str, Any] = name.replace("blocks", "vit.encoder.layer" )
if "attn.proj" in name:
__UpperCAmelCase : Any = name.replace("attn.proj", "attention.output.dense" )
if "attn" in name:
__UpperCAmelCase : Dict = name.replace("attn", "attention.self" )
if "norm1" in name:
__UpperCAmelCase : List[str] = name.replace("norm1", "layernorm_before" )
if "norm2" in name:
__UpperCAmelCase : Dict = name.replace("norm2", "layernorm_after" )
if "mlp.fc1" in name:
__UpperCAmelCase : Dict = name.replace("mlp.fc1", "intermediate.dense" )
if "mlp.fc2" in name:
__UpperCAmelCase : List[str] = name.replace("mlp.fc2", "output.dense" )
if "decoder_embed" in name:
__UpperCAmelCase : Tuple = name.replace("decoder_embed", "decoder.decoder_embed" )
if "decoder_norm" in name:
__UpperCAmelCase : Union[str, Any] = name.replace("decoder_norm", "decoder.decoder_norm" )
if "decoder_pred" in name:
__UpperCAmelCase : int = name.replace("decoder_pred", "decoder.decoder_pred" )
if "norm.weight" in name and "decoder" not in name:
__UpperCAmelCase : str = name.replace("norm.weight", "vit.layernorm.weight" )
if "norm.bias" in name and "decoder" not in name:
__UpperCAmelCase : Optional[int] = name.replace("norm.bias", "vit.layernorm.bias" )
return name
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase ):
for key in orig_state_dict.copy().keys():
__UpperCAmelCase : Optional[int] = orig_state_dict.pop(_UpperCAmelCase )
if "qkv" in key:
__UpperCAmelCase : Tuple = key.split("." )
__UpperCAmelCase : str = int(key_split[1] )
if "decoder_blocks" in key:
__UpperCAmelCase : Optional[int] = config.decoder_hidden_size
__UpperCAmelCase : Optional[int] = "decoder.decoder_layers."
if "weight" in key:
__UpperCAmelCase : Tuple = val[:dim, :]
__UpperCAmelCase : List[Any] = val[dim : dim * 2, :]
__UpperCAmelCase : Dict = val[-dim:, :]
elif "bias" in key:
__UpperCAmelCase : List[str] = val[:dim]
__UpperCAmelCase : Union[str, Any] = val[dim : dim * 2]
__UpperCAmelCase : str = val[-dim:]
else:
__UpperCAmelCase : Union[str, Any] = config.hidden_size
__UpperCAmelCase : Union[str, Any] = "vit.encoder.layer."
if "weight" in key:
__UpperCAmelCase : Any = val[:dim, :]
__UpperCAmelCase : List[Any] = val[dim : dim * 2, :]
__UpperCAmelCase : int = val[-dim:, :]
elif "bias" in key:
__UpperCAmelCase : Tuple = val[:dim]
__UpperCAmelCase : Dict = val[dim : dim * 2]
__UpperCAmelCase : List[str] = val[-dim:]
else:
__UpperCAmelCase : Dict = val
return orig_state_dict
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase ):
__UpperCAmelCase : Optional[Any] = ViTMAEConfig()
if "large" in checkpoint_url:
__UpperCAmelCase : Optional[Any] = 1024
__UpperCAmelCase : int = 4096
__UpperCAmelCase : List[str] = 24
__UpperCAmelCase : Any = 16
elif "huge" in checkpoint_url:
__UpperCAmelCase : str = 14
__UpperCAmelCase : Optional[int] = 1280
__UpperCAmelCase : Optional[int] = 5120
__UpperCAmelCase : Dict = 32
__UpperCAmelCase : Dict = 16
__UpperCAmelCase : List[str] = ViTMAEForPreTraining(_UpperCAmelCase )
__UpperCAmelCase : List[str] = torch.hub.load_state_dict_from_url(_UpperCAmelCase, map_location="cpu" )["model"]
__UpperCAmelCase : Optional[int] = ViTMAEImageProcessor(size=config.image_size )
__UpperCAmelCase : List[Any] = convert_state_dict(_UpperCAmelCase, _UpperCAmelCase )
model.load_state_dict(_UpperCAmelCase )
model.eval()
__UpperCAmelCase : Optional[Any] = "https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"
__UpperCAmelCase : Any = Image.open(requests.get(_UpperCAmelCase, stream=_UpperCAmelCase ).raw )
__UpperCAmelCase : Tuple = ViTMAEImageProcessor(size=config.image_size )
__UpperCAmelCase : Dict = image_processor(images=_UpperCAmelCase, return_tensors="pt" )
# forward pass
torch.manual_seed(2 )
__UpperCAmelCase : Dict = model(**_UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = outputs.logits
if "large" in checkpoint_url:
__UpperCAmelCase : List[Any] = torch.tensor(
[[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]] )
elif "huge" in checkpoint_url:
__UpperCAmelCase : Optional[Any] = torch.tensor(
[[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]] )
else:
__UpperCAmelCase : Dict = torch.tensor(
[[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]] )
# verify logits
assert torch.allclose(logits[0, :3, :3], _UpperCAmelCase, atol=1E-4 )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(_UpperCAmelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
lowerCAmelCase__ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
lowerCAmelCase__ : Dict = parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 37
|
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase_ : Any , UpperCAmelCase_ : Any=13 , UpperCAmelCase_ : Optional[int]=32 , UpperCAmelCase_ : Optional[Any]=2 , UpperCAmelCase_ : Union[str, Any]=3 , UpperCAmelCase_ : Optional[int]=16 , UpperCAmelCase_ : Dict=[1, 2, 1] , UpperCAmelCase_ : str=[2, 2, 4] , UpperCAmelCase_ : Optional[int]=2 , UpperCAmelCase_ : List[Any]=2.0 , UpperCAmelCase_ : str=True , UpperCAmelCase_ : List[str]=0.0 , UpperCAmelCase_ : Union[str, Any]=0.0 , UpperCAmelCase_ : str=0.1 , UpperCAmelCase_ : List[str]="gelu" , UpperCAmelCase_ : Dict=False , UpperCAmelCase_ : str=True , UpperCAmelCase_ : Optional[int]=0.02 , UpperCAmelCase_ : Optional[Any]=1e-5 , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : Union[str, Any]=None , UpperCAmelCase_ : Optional[Any]=True , UpperCAmelCase_ : List[str]=10 , UpperCAmelCase_ : List[Any]=8 , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = parent
__UpperCAmelCase : Union[str, Any] = batch_size
__UpperCAmelCase : Dict = image_size
__UpperCAmelCase : int = patch_size
__UpperCAmelCase : int = num_channels
__UpperCAmelCase : int = embed_dim
__UpperCAmelCase : Dict = depths
__UpperCAmelCase : int = num_heads
__UpperCAmelCase : List[str] = window_size
__UpperCAmelCase : List[str] = mlp_ratio
__UpperCAmelCase : List[Any] = qkv_bias
__UpperCAmelCase : List[Any] = hidden_dropout_prob
__UpperCAmelCase : Dict = attention_probs_dropout_prob
__UpperCAmelCase : Optional[int] = drop_path_rate
__UpperCAmelCase : int = hidden_act
__UpperCAmelCase : Optional[Any] = use_absolute_embeddings
__UpperCAmelCase : List[str] = patch_norm
__UpperCAmelCase : Optional[int] = layer_norm_eps
__UpperCAmelCase : List[Any] = initializer_range
__UpperCAmelCase : str = is_training
__UpperCAmelCase : Optional[int] = scope
__UpperCAmelCase : int = use_labels
__UpperCAmelCase : Union[str, Any] = type_sequence_label_size
__UpperCAmelCase : int = encoder_stride
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__UpperCAmelCase : List[str] = None
if self.use_labels:
__UpperCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCAmelCase : Dict = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def lowerCamelCase_ ( self : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[Any] ):
"""simple docstring"""
__UpperCAmelCase : List[str] = SwinvaModel(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
__UpperCAmelCase : Union[str, Any] = model(UpperCAmelCase_ )
__UpperCAmelCase : Dict = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__UpperCAmelCase : str = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def lowerCamelCase_ ( self : Tuple , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[str] ):
"""simple docstring"""
__UpperCAmelCase : Dict = SwinvaForMaskedImageModeling(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
__UpperCAmelCase : Tuple = model(UpperCAmelCase_ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__UpperCAmelCase : Dict = 1
__UpperCAmelCase : List[str] = SwinvaForMaskedImageModeling(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
__UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__UpperCAmelCase : Dict = model(UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Tuple ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = self.type_sequence_label_size
__UpperCAmelCase : Dict = SwinvaForImageClassification(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
__UpperCAmelCase : Dict = model(UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
__UpperCAmelCase : Dict = self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : List[str] = config_and_inputs
__UpperCAmelCase : Dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( snake_case__ ,snake_case__ ,unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
SCREAMING_SNAKE_CASE = (
{'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
__UpperCAmelCase : List[Any] = SwinvaModelTester(self )
__UpperCAmelCase : Union[str, Any] = ConfigTester(self , config_class=UpperCAmelCase_ , embed_dim=37 )
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
__UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0." )
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
pass
@unittest.skip(reason="Swinv2 does not use inputs_embeds" )
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
pass
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : Optional[Any] = model_class(UpperCAmelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__UpperCAmelCase : Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase_ , nn.Linear ) )
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : str = model_class(UpperCAmelCase_ )
__UpperCAmelCase : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase : Dict = [*signature.parameters.keys()]
__UpperCAmelCase : int = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCAmelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Optional[int] = True
for model_class in self.all_model_classes:
__UpperCAmelCase : Optional[Any] = True
__UpperCAmelCase : Any = False
__UpperCAmelCase : Tuple = True
__UpperCAmelCase : str = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
with torch.no_grad():
__UpperCAmelCase : Dict = model(**self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ ) )
__UpperCAmelCase : Union[str, Any] = outputs.attentions
__UpperCAmelCase : Optional[int] = len(self.model_tester.depths )
self.assertEqual(len(UpperCAmelCase_ ) , UpperCAmelCase_ )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__UpperCAmelCase : Tuple = True
__UpperCAmelCase : List[str] = config.window_size**2
__UpperCAmelCase : Union[str, Any] = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
with torch.no_grad():
__UpperCAmelCase : Optional[Any] = model(**self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ ) )
__UpperCAmelCase : Dict = outputs.attentions
self.assertEqual(len(UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
__UpperCAmelCase : List[str] = len(UpperCAmelCase_ )
# Check attention is always last and order is fine
__UpperCAmelCase : Tuple = True
__UpperCAmelCase : Dict = True
__UpperCAmelCase : Dict = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
with torch.no_grad():
__UpperCAmelCase : Optional[int] = model(**self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ ) )
if hasattr(self.model_tester , "num_hidden_states_types" ):
__UpperCAmelCase : int = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
__UpperCAmelCase : Optional[Any] = 2
self.assertEqual(out_len + added_hidden_states , len(UpperCAmelCase_ ) )
__UpperCAmelCase : List[str] = outputs.attentions
self.assertEqual(len(UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def lowerCamelCase_ ( self : Union[str, Any] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : int , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
with torch.no_grad():
__UpperCAmelCase : Optional[int] = model(**self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ ) )
__UpperCAmelCase : Dict = outputs.hidden_states
__UpperCAmelCase : Dict = getattr(
self.model_tester , "expected_num_hidden_layers" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(UpperCAmelCase_ ) , UpperCAmelCase_ )
# Swinv2 has a different seq_length
__UpperCAmelCase : str = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__UpperCAmelCase : Dict = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
__UpperCAmelCase : str = outputs.reshaped_hidden_states
self.assertEqual(len(UpperCAmelCase_ ) , UpperCAmelCase_ )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : int = reshaped_hidden_states[0].shape
__UpperCAmelCase : Dict = (
reshaped_hidden_states[0].view(UpperCAmelCase_ , UpperCAmelCase_ , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Any = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
__UpperCAmelCase : List[str] = True
self.check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase : Union[str, Any] = True
self.check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : str = 3
__UpperCAmelCase : Optional[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__UpperCAmelCase : int = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__UpperCAmelCase : List[str] = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__UpperCAmelCase : str = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__UpperCAmelCase : Dict = True
self.check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase : List[Any] = True
self.check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , (padded_height, padded_width) )
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
__UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_ )
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
__UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_ )
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : int = SwinvaModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Any = _config_zero_init(UpperCAmelCase_ )
for model_class in self.all_model_classes:
__UpperCAmelCase : str = model_class(config=UpperCAmelCase_ )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f"Parameter {name} of model {model_class} seems not properly initialized" , )
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256" )
if is_vision_available()
else None
)
@slow
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256" ).to(
UpperCAmelCase_ )
__UpperCAmelCase : List[str] = self.default_image_processor
__UpperCAmelCase : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
__UpperCAmelCase : Any = image_processor(images=UpperCAmelCase_ , return_tensors="pt" ).to(UpperCAmelCase_ )
# forward pass
with torch.no_grad():
__UpperCAmelCase : Dict = model(**UpperCAmelCase_ )
# verify the logits
__UpperCAmelCase : Any = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase_ )
__UpperCAmelCase : str = torch.tensor([-0.3947, -0.4306, 0.0026] ).to(UpperCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase_ , atol=1e-4 ) )
| 37
| 1
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __snake_case ( _lowercase):
snake_case__ : str = (
"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
)
snake_case__ : Any = "CIDAS/clipseg-rd64-refined"
snake_case__ : Union[str, Any] = "image_segmenter"
snake_case__ : Union[str, Any] = CLIPSegForImageSegmentation
snake_case__ : int = ["image", "text"]
snake_case__ : List[str] = ["image"]
def __init__( self : str , *__lowerCAmelCase : Union[str, Any] , **__lowerCAmelCase : int ):
"""simple docstring"""
requires_backends(self , ['''vision'''] )
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : "Image" , __lowerCAmelCase : str ):
"""simple docstring"""
return self.pre_processor(text=[label] , images=[image] , padding=__lowerCAmelCase , return_tensors='''pt''' )
def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
with torch.no_grad():
_lowerCamelCase : List[Any] = self.model(**__lowerCAmelCase ).logits
return logits
def SCREAMING_SNAKE_CASE ( self : List[str] , __lowerCAmelCase : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : str = outputs.cpu().detach().numpy()
_lowerCamelCase : Dict = 0
_lowerCamelCase : Any = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 72
|
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase , _lowerCamelCase : int = 9, 14 # noqa: F841
_lowerCamelCase : List[Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
_lowerCamelCase : Any = defaultdict(A_ )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
_lowerCamelCase : List[str] = mst(A_ )
_lowerCamelCase : Optional[int] = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
_lowerCamelCase : int = tuple(answer[:2] )
_lowerCamelCase : int = tuple(edge[::-1] )
assert edge in result or reverse in result
| 72
| 1
|
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
_A = 'linear'
_A = 'cosine'
_A = 'cosine_with_restarts'
_A = 'polynomial'
_A = 'constant'
_A = 'constant_with_warmup'
_A = 'piecewise_constant'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optimizer , _lowerCamelCase : int = -1) -> Tuple:
'''simple docstring'''
return LambdaLR(_lowerCamelCase , lambda _lowerCamelCase: 1 , last_epoch=_lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optimizer , _lowerCamelCase : int , _lowerCamelCase : int = -1) -> Union[str, Any]:
'''simple docstring'''
def lr_lambda(_lowerCamelCase : int):
if current_step < num_warmup_steps:
return float(_lowerCamelCase) / float(max(1.0 , _lowerCamelCase))
return 1.0
return LambdaLR(_lowerCamelCase , _lowerCamelCase , last_epoch=_lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optimizer , _lowerCamelCase : str , _lowerCamelCase : int = -1) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase : Optional[Any] = {}
__UpperCamelCase : int = step_rules.split(",")
for rule_str in rule_list[:-1]:
__UpperCamelCase , __UpperCamelCase : Dict = rule_str.split(":")
__UpperCamelCase : Union[str, Any] = int(_lowerCamelCase)
__UpperCamelCase : int = float(_lowerCamelCase)
__UpperCamelCase : Tuple = value
__UpperCamelCase : str = float(rule_list[-1])
def create_rules_function(_lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any]):
def rule_func(_lowerCamelCase : int) -> float:
__UpperCamelCase : List[str] = sorted(rules_dict.keys())
for i, sorted_step in enumerate(_lowerCamelCase):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
__UpperCamelCase : int = create_rules_function(_lowerCamelCase , _lowerCamelCase)
return LambdaLR(_lowerCamelCase , _lowerCamelCase , last_epoch=_lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Tuple , _lowerCamelCase : Any , _lowerCamelCase : Dict , _lowerCamelCase : Union[str, Any]=-1) -> str:
'''simple docstring'''
def lr_lambda(_lowerCamelCase : int):
if current_step < num_warmup_steps:
return float(_lowerCamelCase) / float(max(1 , _lowerCamelCase))
return max(
0.0 , float(num_training_steps - current_step) / float(max(1 , num_training_steps - num_warmup_steps)))
return LambdaLR(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optimizer , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : float = 0.5 , _lowerCamelCase : int = -1) -> Optional[int]:
'''simple docstring'''
def lr_lambda(_lowerCamelCase : int):
if current_step < num_warmup_steps:
return float(_lowerCamelCase) / float(max(1 , _lowerCamelCase))
__UpperCamelCase : List[str] = float(current_step - num_warmup_steps) / float(max(1 , num_training_steps - num_warmup_steps))
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(_lowerCamelCase) * 2.0 * progress)))
return LambdaLR(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optimizer , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int = 1 , _lowerCamelCase : int = -1) -> List[Any]:
'''simple docstring'''
def lr_lambda(_lowerCamelCase : str):
if current_step < num_warmup_steps:
return float(_lowerCamelCase) / float(max(1 , _lowerCamelCase))
__UpperCamelCase : Optional[int] = float(current_step - num_warmup_steps) / float(max(1 , num_training_steps - num_warmup_steps))
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(_lowerCamelCase) * progress) % 1.0))))
return LambdaLR(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Tuple , _lowerCamelCase : Optional[int] , _lowerCamelCase : Tuple , _lowerCamelCase : List[Any]=1e-7 , _lowerCamelCase : List[str]=1.0 , _lowerCamelCase : Union[str, Any]=-1) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase : str = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(F'lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})')
def lr_lambda(_lowerCamelCase : int):
if current_step < num_warmup_steps:
return float(_lowerCamelCase) / float(max(1 , _lowerCamelCase))
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
__UpperCamelCase : int = lr_init - lr_end
__UpperCamelCase : int = num_training_steps - num_warmup_steps
__UpperCamelCase : str = 1 - (current_step - num_warmup_steps) / decay_steps
__UpperCamelCase : int = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
lowercase : str = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, SchedulerType] , _lowerCamelCase : Optimizer , _lowerCamelCase : Optional[str] = None , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : int = 1 , _lowerCamelCase : float = 1.0 , _lowerCamelCase : int = -1 , ) -> Dict:
'''simple docstring'''
__UpperCamelCase : int = SchedulerType(_lowerCamelCase)
__UpperCamelCase : List[Any] = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(_lowerCamelCase , last_epoch=_lowerCamelCase)
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(_lowerCamelCase , step_rules=_lowerCamelCase , last_epoch=_lowerCamelCase)
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F'{name} requires `num_warmup_steps`, please provide that argument.')
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(_lowerCamelCase , num_warmup_steps=_lowerCamelCase , last_epoch=_lowerCamelCase)
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F'{name} requires `num_training_steps`, please provide that argument.')
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
_lowerCamelCase , num_warmup_steps=_lowerCamelCase , num_training_steps=_lowerCamelCase , num_cycles=_lowerCamelCase , last_epoch=_lowerCamelCase , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
_lowerCamelCase , num_warmup_steps=_lowerCamelCase , num_training_steps=_lowerCamelCase , power=_lowerCamelCase , last_epoch=_lowerCamelCase , )
return schedule_func(
_lowerCamelCase , num_warmup_steps=_lowerCamelCase , num_training_steps=_lowerCamelCase , last_epoch=_lowerCamelCase)
| 151
|
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 10 , _lowerCamelCase : int = 22) -> int:
'''simple docstring'''
__UpperCamelCase : Any = range(1 , _lowerCamelCase)
__UpperCamelCase : int = range(1 , _lowerCamelCase)
return sum(
1 for power in powers for base in bases if len(str(base**power)) == power)
if __name__ == "__main__":
print(f"{solution(10, 22) = }")
| 151
| 1
|
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : Tuple ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Any=13 ,lowerCamelCase__ : Tuple=7 ,lowerCamelCase__ : Optional[int]=True ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : List[Any]=False ,lowerCamelCase__ : Tuple=True ,lowerCamelCase__ : List[Any]=99 ,lowerCamelCase__ : Dict=64 ,lowerCamelCase__ : List[Any]=5 ,lowerCamelCase__ : Tuple=4 ,lowerCamelCase__ : Optional[Any]=64 ,lowerCamelCase__ : Union[str, Any]="gelu" ,lowerCamelCase__ : Optional[int]=0.1 ,lowerCamelCase__ : List[str]=0.1 ,lowerCamelCase__ : Tuple=512 ,lowerCamelCase__ : Tuple=16 ,lowerCamelCase__ : List[str]=2 ,lowerCamelCase__ : Tuple=0.02 ,lowerCamelCase__ : Any=3 ,lowerCamelCase__ : str=4 ,lowerCamelCase__ : List[Any]=None ,) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = seq_length
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_input_mask
SCREAMING_SNAKE_CASE = use_token_type_ids
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = type_sequence_label_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = num_choices
SCREAMING_SNAKE_CASE = scope
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> str:
'''simple docstring'''
return MPNetConfig.from_pretrained("""microsoft/mpnet-base""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] ,self.num_choices )
SCREAMING_SNAKE_CASE = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
return MPNetConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,)
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Dict ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MPNetModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape ,(self.batch_size, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Tuple ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MPNetForQuestionAnswering(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(
lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,start_positions=lowerCamelCase__ ,end_positions=lowerCamelCase__ ,)
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Tuple ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = MPNetForSequenceClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : int ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : str ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Tuple ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.num_choices
SCREAMING_SNAKE_CASE = MPNetForMultipleChoice(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE = model(
lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,labels=lowerCamelCase__ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : Any ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = MPNetForTokenClassification(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE), (SCREAMING_SNAKE_CASE), (SCREAMING_SNAKE_CASE), (SCREAMING_SNAKE_CASE), (SCREAMING_SNAKE_CASE), (SCREAMING_SNAKE_CASE)) = config_and_inputs
SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase__ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[Any] = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
__snake_case : str = (
{
"feature-extraction": MPNetModel,
"fill-mask": MPNetForMaskedLM,
"question-answering": MPNetForQuestionAnswering,
"text-classification": MPNetForSequenceClassification,
"token-classification": MPNetForTokenClassification,
"zero-shot": MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
__snake_case : Tuple = False
__snake_case : Tuple = True
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MPNetModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self ,config_class=lowerCamelCase__ ,hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*lowerCamelCase__ )
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MPNetModel.from_pretrained("""microsoft/mpnet-base""" )
SCREAMING_SNAKE_CASE = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )[0]
SCREAMING_SNAKE_CASE = torch.Size((1, 11, 768) )
self.assertEqual(output.shape ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.tensor(
[[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] ,lowerCamelCase__ ,atol=1e-4 ) )
| 296
|
import random
class UpperCamelCase__ :
'''simple docstring'''
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : str ) -> tuple[list[int], list[int]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [ord(lowerCamelCase__ ) for i in text]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for i in plain:
SCREAMING_SNAKE_CASE = random.randint(1 ,300 )
SCREAMING_SNAKE_CASE = (i + k) * k
cipher.append(lowerCamelCase__ )
key.append(lowerCamelCase__ )
return cipher, key
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : list[int] ,lowerCamelCase__ : list[int] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
for i in range(len(lowerCamelCase__ ) ):
SCREAMING_SNAKE_CASE = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(lowerCamelCase__ ) )
return "".join(lowerCamelCase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 296
| 1
|
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class A_ ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self :Union[str, Any] ) -> Union[str, Any]:
UpperCAmelCase = '| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
UpperCAmelCase = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
UpperCAmelCase = {
'unk_token': '<unk>',
'bos_token': '<s>',
'eos_token': '</s>',
}
UpperCAmelCase = {
'feature_size': 1,
'padding_value': 0.0,
'sampling_rate': 1_60_00,
'return_attention_mask': False,
'do_normalize': True,
}
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
UpperCAmelCase = os.path.join(self.tmpdirname , lowercase_ )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(lowercase_ ) + '\n' )
with open(self.feature_extraction_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(lowercase_ ) + '\n' )
# load decoder from hub
UpperCAmelCase = 'hf-internal-testing/ngram-beam-search-decoder'
def UpperCAmelCase__ ( self :int , **lowercase_ :Union[str, Any] ) -> Optional[Any]:
UpperCAmelCase = self.add_kwargs_tokens_map.copy()
kwargs.update(lowercase_ )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **lowercase_ )
def UpperCAmelCase__ ( self :Dict , **lowercase_ :Optional[int] ) -> int:
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **lowercase_ )
def UpperCAmelCase__ ( self :Optional[Any] , **lowercase_ :List[str] ) -> List[Any]:
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **lowercase_ )
def UpperCAmelCase__ ( self :Optional[Any] ) -> Any:
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase__ ( self :Dict ) -> List[str]:
UpperCAmelCase = self.get_tokenizer()
UpperCAmelCase = self.get_feature_extractor()
UpperCAmelCase = self.get_decoder()
UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase_ )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , lowercase_ )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , lowercase_ )
def UpperCAmelCase__ ( self :Optional[int] ) -> Union[str, Any]:
UpperCAmelCase = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def UpperCAmelCase__ ( self :Optional[Any] ) -> Optional[int]:
UpperCAmelCase = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['xx'] )
with self.assertRaisesRegex(lowercase_ , 'include' ):
WavaVecaProcessorWithLM(
tokenizer=lowercase_ , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def UpperCAmelCase__ ( self :Optional[Any] ) -> Any:
UpperCAmelCase = self.get_feature_extractor()
UpperCAmelCase = self.get_tokenizer()
UpperCAmelCase = self.get_decoder()
UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
UpperCAmelCase = floats_list((3, 10_00) )
UpperCAmelCase = feature_extractor(lowercase_ , return_tensors='np' )
UpperCAmelCase = processor(lowercase_ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCAmelCase__ ( self :List[Any] ) -> Tuple:
UpperCAmelCase = self.get_feature_extractor()
UpperCAmelCase = self.get_tokenizer()
UpperCAmelCase = self.get_decoder()
UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
UpperCAmelCase = 'This is a test string'
UpperCAmelCase = processor(text=lowercase_ )
UpperCAmelCase = tokenizer(lowercase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCAmelCase__ ( self :List[Any] , lowercase_ :List[str]=(2, 10, 16) , lowercase_ :str=77 ) -> Union[str, Any]:
np.random.seed(lowercase_ )
return np.random.rand(*lowercase_ )
def UpperCAmelCase__ ( self :Any ) -> Dict:
UpperCAmelCase = self.get_feature_extractor()
UpperCAmelCase = self.get_tokenizer()
UpperCAmelCase = self.get_decoder()
UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
UpperCAmelCase = self._get_dummy_logits(shape=(10, 16) , seed=13 )
UpperCAmelCase = processor.decode(lowercase_ )
UpperCAmelCase = decoder.decode_beams(lowercase_ )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual('</s> <s> </s>' , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ['fork'], ['spawn']] )
def UpperCAmelCase__ ( self :List[Any] , lowercase_ :Optional[Any] ) -> str:
UpperCAmelCase = self.get_feature_extractor()
UpperCAmelCase = self.get_tokenizer()
UpperCAmelCase = self.get_decoder()
UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
UpperCAmelCase = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
UpperCAmelCase = processor.batch_decode(lowercase_ )
else:
with get_context(lowercase_ ).Pool() as pool:
UpperCAmelCase = processor.batch_decode(lowercase_ , lowercase_ )
UpperCAmelCase = list(lowercase_ )
with get_context('fork' ).Pool() as p:
UpperCAmelCase = decoder.decode_beams_batch(lowercase_ , lowercase_ )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(lowercase_ , decoded_processor.text )
self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>'] , decoded_processor.text )
self.assertListEqual(lowercase_ , decoded_processor.logit_score )
self.assertListEqual(lowercase_ , decoded_processor.lm_score )
def UpperCAmelCase__ ( self :List[Any] ) -> Optional[int]:
UpperCAmelCase = self.get_feature_extractor()
UpperCAmelCase = self.get_tokenizer()
UpperCAmelCase = self.get_decoder()
UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
UpperCAmelCase = self._get_dummy_logits()
UpperCAmelCase = 15
UpperCAmelCase = -20.0
UpperCAmelCase = -4.0
UpperCAmelCase = processor.batch_decode(
lowercase_ , beam_width=lowercase_ , beam_prune_logp=lowercase_ , token_min_logp=lowercase_ , )
UpperCAmelCase = decoded_processor_out.text
UpperCAmelCase = list(lowercase_ )
with get_context('fork' ).Pool() as pool:
UpperCAmelCase = decoder.decode_beams_batch(
lowercase_ , lowercase_ , beam_width=lowercase_ , beam_prune_logp=lowercase_ , token_min_logp=lowercase_ , )
UpperCAmelCase = [d[0][0] for d in decoded_decoder_out]
UpperCAmelCase = [d[0][2] for d in decoded_decoder_out]
UpperCAmelCase = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(lowercase_ , lowercase_ )
self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>'] , lowercase_ )
self.assertTrue(np.array_equal(lowercase_ , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , lowercase_ , atol=1E-3 ) )
self.assertTrue(np.array_equal(lowercase_ , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9474] , lowercase_ , atol=1E-3 ) )
def UpperCAmelCase__ ( self :Any ) -> List[Any]:
UpperCAmelCase = self.get_feature_extractor()
UpperCAmelCase = self.get_tokenizer()
UpperCAmelCase = self.get_decoder()
UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
UpperCAmelCase = self._get_dummy_logits()
UpperCAmelCase = 2.0
UpperCAmelCase = 5.0
UpperCAmelCase = -20.0
UpperCAmelCase = True
UpperCAmelCase = processor.batch_decode(
lowercase_ , alpha=lowercase_ , beta=lowercase_ , unk_score_offset=lowercase_ , lm_score_boundary=lowercase_ , )
UpperCAmelCase = decoded_processor_out.text
UpperCAmelCase = list(lowercase_ )
decoder.reset_params(
alpha=lowercase_ , beta=lowercase_ , unk_score_offset=lowercase_ , lm_score_boundary=lowercase_ , )
with get_context('fork' ).Pool() as pool:
UpperCAmelCase = decoder.decode_beams_batch(
lowercase_ , lowercase_ , )
UpperCAmelCase = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(lowercase_ , lowercase_ )
self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>'] , lowercase_ )
UpperCAmelCase = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , lowercase_ )
def UpperCAmelCase__ ( self :Any ) -> int:
UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
UpperCAmelCase = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase = Path(language_model._kenlm_model.path.decode('utf-8' ) ).parent.parent.absolute()
UpperCAmelCase = os.listdir(lowercase_ )
UpperCAmelCase = ['alphabet.json', 'language_model']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self :List[str] ) -> int:
UpperCAmelCase = snapshot_download('hf-internal-testing/processor_with_lm' )
UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained(lowercase_ )
UpperCAmelCase = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase = Path(language_model._kenlm_model.path.decode('utf-8' ) ).parent.parent.absolute()
UpperCAmelCase = os.listdir(lowercase_ )
UpperCAmelCase = os.listdir(lowercase_ )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self :Tuple ) -> List[str]:
UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
UpperCAmelCase = AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm' )
UpperCAmelCase = floats_list((3, 10_00) )
UpperCAmelCase = processor_wavaveca(lowercase_ , return_tensors='np' )
UpperCAmelCase = processor_auto(lowercase_ , return_tensors='np' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
UpperCAmelCase = self._get_dummy_logits()
UpperCAmelCase = processor_wavaveca.batch_decode(lowercase_ )
UpperCAmelCase = processor_auto.batch_decode(lowercase_ )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def UpperCAmelCase__ ( self :str ) -> Dict:
UpperCAmelCase = self.get_feature_extractor()
UpperCAmelCase = self.get_tokenizer()
UpperCAmelCase = self.get_decoder()
UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
@staticmethod
def UpperCAmelCase__ ( lowercase_ :List[str] , lowercase_ :Any ) -> List[Any]:
UpperCAmelCase = [d[key] for d in offsets]
return retrieved_list
def UpperCAmelCase__ ( self :str ) -> Tuple:
UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
UpperCAmelCase = self._get_dummy_logits()[0]
UpperCAmelCase = processor.decode(lowercase_ , output_word_offsets=lowercase_ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('text' in outputs )
self.assertTrue('word_offsets' in outputs )
self.assertTrue(isinstance(lowercase_ , lowercase_ ) )
self.assertEqual(' '.join(self.get_from_offsets(outputs['word_offsets'] , 'word' ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'] , 'word' ) , ['<s>', '<s>', '</s>'] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'] , 'start_offset' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'] , 'end_offset' ) , [1, 3, 5] )
def UpperCAmelCase__ ( self :int ) -> List[str]:
UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
UpperCAmelCase = self._get_dummy_logits()
UpperCAmelCase = processor.batch_decode(lowercase_ , output_word_offsets=lowercase_ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('text' in outputs )
self.assertTrue('word_offsets' in outputs )
self.assertTrue(isinstance(lowercase_ , lowercase_ ) )
self.assertListEqual(
[' '.join(self.get_from_offsets(lowercase_ , 'word' ) ) for o in outputs['word_offsets']] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0] , 'word' ) , ['<s>', '<s>', '</s>'] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0] , 'start_offset' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0] , 'end_offset' ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def UpperCAmelCase__ ( self :Optional[int] ) -> str:
import torch
UpperCAmelCase = load_dataset('common_voice' , 'en' , split='train' , streaming=lowercase_ )
UpperCAmelCase = ds.cast_column('audio' , datasets.Audio(sampling_rate=1_60_00 ) )
UpperCAmelCase = iter(lowercase_ )
UpperCAmelCase = next(lowercase_ )
UpperCAmelCase = AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm' )
UpperCAmelCase = WavaVecaForCTC.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
UpperCAmelCase = processor(sample['audio']['array'] , return_tensors='pt' ).input_values
with torch.no_grad():
UpperCAmelCase = model(lowercase_ ).logits.cpu().numpy()
UpperCAmelCase = processor.decode(logits[0] , output_word_offsets=lowercase_ )
UpperCAmelCase = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
UpperCAmelCase = [
{
'start_time': d['start_offset'] * time_offset,
'end_time': d['end_offset'] * time_offset,
'word': d['word'],
}
for d in output['word_offsets']
]
UpperCAmelCase = 'WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'
# output words
self.assertEqual(' '.join(self.get_from_offsets(lowercase_ , 'word' ) ) , lowercase_ )
self.assertEqual(' '.join(self.get_from_offsets(lowercase_ , 'word' ) ) , output.text )
# output times
UpperCAmelCase = torch.tensor(self.get_from_offsets(lowercase_ , 'start_time' ) )
UpperCAmelCase = torch.tensor(self.get_from_offsets(lowercase_ , 'end_time' ) )
# fmt: off
UpperCAmelCase = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
UpperCAmelCase = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=0.01 ) )
self.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=0.01 ) )
| 181
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ = {
"""configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ["""RemBertTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ["""RemBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RemBertForCausalLM""",
"""RemBertForMaskedLM""",
"""RemBertForMultipleChoice""",
"""RemBertForQuestionAnswering""",
"""RemBertForSequenceClassification""",
"""RemBertForTokenClassification""",
"""RemBertLayer""",
"""RemBertModel""",
"""RemBertPreTrainedModel""",
"""load_tf_weights_in_rembert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRemBertForCausalLM""",
"""TFRemBertForMaskedLM""",
"""TFRemBertForMultipleChoice""",
"""TFRemBertForQuestionAnswering""",
"""TFRemBertForSequenceClassification""",
"""TFRemBertForTokenClassification""",
"""TFRemBertLayer""",
"""TFRemBertModel""",
"""TFRemBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 181
| 1
|
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,
set_seed,
)
def lowercase ( )-> List[Any]:
'''simple docstring'''
a : Any = argparse.ArgumentParser()
parser.add_argument("--model_ckpt" , type=A_ , default="microsoft/unixcoder-base-nine" )
parser.add_argument("--num_epochs" , type=A_ , default=5 )
parser.add_argument("--batch_size" , type=A_ , default=6 )
parser.add_argument("--gradient_accumulation_steps" , type=A_ , default=1 )
parser.add_argument("--freeze" , type=A_ , default=A_ )
parser.add_argument("--learning_rate" , type=A_ , default=5e-4 )
parser.add_argument("--seed" , type=A_ , default=0 )
parser.add_argument("--lr_scheduler_type" , type=A_ , default="cosine" )
parser.add_argument("--num_warmup_steps" , type=A_ , default=10 )
parser.add_argument("--weight_decay" , type=A_ , default=0.0_1 )
parser.add_argument("--output_dir" , type=A_ , default="./results" )
return parser.parse_args()
__lowercase = load("""accuracy""")
def lowercase ( A_ )-> Tuple:
'''simple docstring'''
a , a : List[str] = eval_pred
a : str = np.argmax(A_ , axis=1 )
return metric.compute(predictions=A_ , references=A_ )
class _A ( _a ):
"""simple docstring"""
def __init__( self : Optional[Any] , __UpperCAmelCase : Union[str, Any]):
super().__init__()
a : int = trainer
def __snake_case ( self : Optional[Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[int] , **__UpperCAmelCase : List[Any]):
if control.should_evaluate:
a : Union[str, Any] = deepcopy(__UpperCAmelCase)
self._trainer.evaluate(eval_dataset=self._trainer.train_dataset , metric_key_prefix="train")
return control_copy
def lowercase ( )-> Union[str, Any]:
'''simple docstring'''
a : List[str] = get_args()
set_seed(args.seed )
a : int = load_dataset("codeparrot/codecomplex" , split="train" )
a : Dict = dataset.train_test_split(test_size=0.2 )
a : int = train_test["test"].train_test_split(test_size=0.5 )
a : Union[str, Any] = DatasetDict(
{
"train": train_test["train"],
"test": test_validation["train"],
"valid": test_validation["test"],
} )
print("Loading tokenizer and model" )
a : Dict = AutoTokenizer.from_pretrained(args.model_ckpt )
a : Optional[int] = tokenizer.eos_token
a : Any = AutoModelForSequenceClassification.from_pretrained(args.model_ckpt , num_labels=7 )
a : Any = model.config.eos_token_id
if args.freeze:
for param in model.roberta.parameters():
a : Optional[Any] = False
a : Union[str, Any] = ClassLabel(num_classes=7 , names=list(set(train_test_validation["train"]["complexity"] ) ) )
def tokenize(A_ ):
a : List[str] = tokenizer(example["src"] , truncation=A_ , max_length=1_024 )
a : Optional[int] = labels.straint(example["complexity"] )
return {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"label": label,
}
a : Optional[Any] = train_test_validation.map(
A_ , batched=A_ , remove_columns=train_test_validation["train"].column_names , )
a : Dict = DataCollatorWithPadding(tokenizer=A_ )
a : Optional[Any] = TrainingArguments(
output_dir=args.output_dir , learning_rate=args.learning_rate , lr_scheduler_type=args.lr_scheduler_type , evaluation_strategy="epoch" , save_strategy="epoch" , logging_strategy="epoch" , per_device_train_batch_size=args.batch_size , per_device_eval_batch_size=args.batch_size , num_train_epochs=args.num_epochs , gradient_accumulation_steps=args.gradient_accumulation_steps , weight_decay=0.0_1 , metric_for_best_model="accuracy" , run_name="complexity-java" , report_to="wandb" , )
a : Union[str, Any] = Trainer(
model=A_ , args=A_ , train_dataset=tokenized_datasets["train"] , eval_dataset=tokenized_datasets["valid"] , tokenizer=A_ , data_collator=A_ , compute_metrics=A_ , )
print("Training..." )
trainer.add_callback(CustomCallback(A_ ) )
trainer.train()
if __name__ == "__main__":
main()
| 40
|
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowercase ( A_ , A_ , A_ = False )-> list[float]:
'''simple docstring'''
if radian_mode:
return [magnitude * cos(A_ ), magnitude * sin(A_ )]
return [magnitude * cos(radians(A_ ) ), magnitude * sin(radians(A_ ) )]
def lowercase ( A_ , A_ , A_ = 10**-1 )-> bool:
'''simple docstring'''
a : NDArray[floataa] = cross(A_ , A_ )
a : float = sum(A_ )
return abs(A_ ) < eps
if __name__ == "__main__":
# Test to check if it works
__lowercase = array(
[
polar_force(7_18.4, 180 - 30),
polar_force(8_79.54, 45),
polar_force(100, -90),
]
)
__lowercase = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
__lowercase = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
__lowercase = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
__lowercase = array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])
__lowercase = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| 40
| 1
|
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class __snake_case :
__lowerCamelCase : torch.Tensor # [batch_size x 3]
__lowerCamelCase : torch.Tensor # [batch_size x 3]
__lowerCamelCase : torch.Tensor # [batch_size x 3]
__lowerCamelCase : torch.Tensor # [batch_size x 3]
__lowerCamelCase : int
__lowerCamelCase : int
__lowerCamelCase : float
__lowerCamelCase : float
__lowerCamelCase : Tuple[int]
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def UpperCAmelCase__ ( self ) -> torch.Tensor:
'''simple docstring'''
UpperCAmelCase : str =torch.arange(self.height * self.width )
UpperCAmelCase : Tuple =torch.stack(
[
pixel_indices % self.width,
torch.div(snake_case__ , self.width , rounding_mode='''trunc''' ),
] , axis=1 , )
return coords
@property
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase , *UpperCAmelCase : List[str] =self.shape
UpperCAmelCase : List[Any] =int(np.prod(snake_case__ ) )
UpperCAmelCase : List[str] =self.get_image_coords()
UpperCAmelCase : Optional[int] =torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
UpperCAmelCase : Any =self.get_camera_rays(snake_case__ )
UpperCAmelCase : Union[str, Any] =rays.view(snake_case__ , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def UpperCAmelCase__ ( self , snake_case__ ) -> torch.Tensor:
'''simple docstring'''
UpperCAmelCase , *UpperCAmelCase , UpperCAmelCase : List[Any] =coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
UpperCAmelCase : Optional[Any] =coords.view(snake_case__ , -1 , 2 )
UpperCAmelCase : str =self.resolution()
UpperCAmelCase : List[str] =self.fov()
UpperCAmelCase : Any =(flat.float() / (res - 1)) * 2 - 1
UpperCAmelCase : int =fracs * torch.tan(fov / 2 )
UpperCAmelCase : Optional[int] =fracs.view(snake_case__ , -1 , 2 )
UpperCAmelCase : List[str] =(
self.z.view(snake_case__ , 1 , 3 )
+ self.x.view(snake_case__ , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(snake_case__ , 1 , 3 ) * fracs[:, :, 1:]
)
UpperCAmelCase : Union[str, Any] =directions / directions.norm(dim=-1 , keepdim=snake_case__ )
UpperCAmelCase : List[Any] =torch.stack(
[
torch.broadcast_to(self.origin.view(snake_case__ , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(snake_case__ , *snake_case__ , 2 , 3 )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> "DifferentiableProjectiveCamera":
'''simple docstring'''
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=snake_case__ , height=snake_case__ , x_fov=self.x_fov , y_fov=self.y_fov , )
def lowerCAmelCase_ ( __lowerCAmelCase )-> DifferentiableProjectiveCamera:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =[]
UpperCAmelCase : Union[str, Any] =[]
UpperCAmelCase : Optional[Any] =[]
UpperCAmelCase : str =[]
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
UpperCAmelCase : Union[str, Any] =np.array([np.sin(__lowerCAmelCase ), np.cos(__lowerCAmelCase ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
UpperCAmelCase : Union[str, Any] =-z * 4
UpperCAmelCase : Union[str, Any] =np.array([np.cos(__lowerCAmelCase ), -np.sin(__lowerCAmelCase ), 0.0] )
UpperCAmelCase : str =np.cross(__lowerCAmelCase , __lowerCAmelCase )
origins.append(__lowerCAmelCase )
xs.append(__lowerCAmelCase )
ys.append(__lowerCAmelCase )
zs.append(__lowerCAmelCase )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(__lowerCAmelCase , axis=0 ) ).float() , x=torch.from_numpy(np.stack(__lowerCAmelCase , axis=0 ) ).float() , y=torch.from_numpy(np.stack(__lowerCAmelCase , axis=0 ) ).float() , z=torch.from_numpy(np.stack(__lowerCAmelCase , axis=0 ) ).float() , width=__lowerCAmelCase , height=__lowerCAmelCase , x_fov=0.7 , y_fov=0.7 , shape=(1, len(__lowerCAmelCase )) , )
| 78
|
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __snake_case ( lowerCamelCase__ ):
@require_torch
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : List[Any] ='''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
UpperCAmelCase : Tuple ='''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
UpperCAmelCase : int ='''
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
UpperCAmelCase : Optional[int] ='''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(snake_case__ )
BertModel.from_pretrained(snake_case__ )
BertTokenizer.from_pretrained(snake_case__ )
pipeline(task='''fill-mask''' , model=snake_case__ )
# baseline - just load from_pretrained with normal network
UpperCAmelCase : List[Any] =[sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
UpperCAmelCase : List[Any] =self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCAmelCase : Optional[Any] ='''1'''
UpperCAmelCase : List[Any] =subprocess.run(snake_case__ , env=snake_case__ , check=snake_case__ , capture_output=snake_case__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
@require_torch
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[Any] ='''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
UpperCAmelCase : Any ='''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
UpperCAmelCase : Union[str, Any] ='''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
UpperCAmelCase : Union[str, Any] ='''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(snake_case__ )
BertModel.from_pretrained(snake_case__ )
BertTokenizer.from_pretrained(snake_case__ )
pipeline(task='''fill-mask''' , model=snake_case__ )
# baseline - just load from_pretrained with normal network
UpperCAmelCase : Any =[sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
UpperCAmelCase : List[str] =self.get_env()
UpperCAmelCase : Any =subprocess.run(snake_case__ , env=snake_case__ , check=snake_case__ , capture_output=snake_case__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
@require_torch
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] ='''
from transformers import BertConfig, BertModel, BertTokenizer
'''
UpperCAmelCase : int ='''
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
'''
UpperCAmelCase : int ='''
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
'''
# baseline - just load from_pretrained with normal network
UpperCAmelCase : Dict =[sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
UpperCAmelCase : Any =self.get_env()
UpperCAmelCase : List[Any] =subprocess.run(snake_case__ , env=snake_case__ , check=snake_case__ , capture_output=snake_case__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
# next emulate no network
UpperCAmelCase : Optional[Any] =[sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCAmelCase : int ='''1'''
UpperCAmelCase : Optional[Any] =subprocess.run(snake_case__ , env=snake_case__ , check=snake_case__ , capture_output=snake_case__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
@require_torch
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Dict ='''
from transformers import pipeline
'''
UpperCAmelCase : List[Any] ='''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
'''
UpperCAmelCase : Tuple ='''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
'''
UpperCAmelCase : Optional[int] =self.get_env()
UpperCAmelCase : int ='''1'''
UpperCAmelCase : Optional[int] =[sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
UpperCAmelCase : List[str] =subprocess.run(snake_case__ , env=snake_case__ , check=snake_case__ , capture_output=snake_case__ )
self.assertEqual(result.returncode , 1 , result.stderr )
self.assertIn(
'''You cannot infer task automatically within `pipeline` when using offline mode''' , result.stderr.decode().replace('''\n''' , '''''' ) , )
@require_torch
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Any ='''
from transformers import AutoModel
'''
UpperCAmelCase : Optional[Any] ='''
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
'''
# baseline - just load from_pretrained with normal network
UpperCAmelCase : Dict =[sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
UpperCAmelCase : Optional[int] =self.get_env()
UpperCAmelCase : Optional[Any] =subprocess.run(snake_case__ , env=snake_case__ , check=snake_case__ , capture_output=snake_case__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCAmelCase : Any ='''1'''
UpperCAmelCase : Dict =subprocess.run(snake_case__ , env=snake_case__ , check=snake_case__ , capture_output=snake_case__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
| 78
| 1
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class lowerCAmelCase_( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : Union[str, Any] = 1
lowerCAmelCase__ : Optional[Any] = 3
lowerCAmelCase__ : Optional[int] = (32, 32)
lowerCAmelCase__ : Optional[int] = floats_tensor((batch_size, num_channels) + sizes ,rng=random.Random(0 ) ).to(__UpperCAmelCase )
return image
@property
def UpperCAmelCase_ ( self ) -> str:
torch.manual_seed(0 )
lowerCAmelCase__ : Any = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
return model
@property
def UpperCAmelCase_ ( self ) -> List[str]:
torch.manual_seed(0 )
lowerCAmelCase__ : Optional[Any] = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
return model
@property
def UpperCAmelCase_ ( self ) -> str:
torch.manual_seed(0 )
lowerCAmelCase__ : List[Any] = RobertaSeriesConfig(
hidden_size=32 ,project_dim=32 ,intermediate_size=37 ,layer_norm_eps=1E-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=5006 ,)
return RobertaSeriesModelWithTransformation(__UpperCAmelCase )
@property
def UpperCAmelCase_ ( self ) -> int:
def extract(*__UpperCAmelCase ,**__UpperCAmelCase ):
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ) -> Optional[int]:
lowerCAmelCase__ : Any = torch.ones([0] )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> str:
self.pixel_values.to(__UpperCAmelCase )
return self
return Out()
return extract
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
lowerCAmelCase__ : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase__ : List[Any] = self.dummy_cond_unet
lowerCAmelCase__ : Tuple = PNDMScheduler(skip_prk_steps=__UpperCAmelCase )
lowerCAmelCase__ : Dict = self.dummy_vae
lowerCAmelCase__ : Tuple = self.dummy_text_encoder
lowerCAmelCase__ : Union[str, Any] = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
lowerCAmelCase__ : str = 77
lowerCAmelCase__ : List[str] = self.dummy_image.to(__UpperCAmelCase )
lowerCAmelCase__ : int = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
lowerCAmelCase__ : Dict = AltDiffusionImgaImgPipeline(
unet=__UpperCAmelCase ,scheduler=__UpperCAmelCase ,vae=__UpperCAmelCase ,text_encoder=__UpperCAmelCase ,tokenizer=__UpperCAmelCase ,safety_checker=__UpperCAmelCase ,feature_extractor=self.dummy_extractor ,)
lowerCAmelCase__ : List[Any] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor ,do_normalize=__UpperCAmelCase )
lowerCAmelCase__ : Tuple = alt_pipe.to(__UpperCAmelCase )
alt_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
lowerCAmelCase__ : int = """A painting of a squirrel eating a burger"""
lowerCAmelCase__ : Optional[Any] = torch.Generator(device=__UpperCAmelCase ).manual_seed(0 )
lowerCAmelCase__ : Optional[Any] = alt_pipe(
[prompt] ,generator=__UpperCAmelCase ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,image=__UpperCAmelCase ,)
lowerCAmelCase__ : List[Any] = output.images
lowerCAmelCase__ : Optional[Any] = torch.Generator(device=__UpperCAmelCase ).manual_seed(0 )
lowerCAmelCase__ : Dict = alt_pipe(
[prompt] ,generator=__UpperCAmelCase ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,image=__UpperCAmelCase ,return_dict=__UpperCAmelCase ,)[0]
lowerCAmelCase__ : Optional[Any] = image[0, -3:, -3:, -1]
lowerCAmelCase__ : str = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCAmelCase__ : Optional[Any] = np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5E-3
@unittest.skipIf(torch_device != """cuda""" ,"""This test requires a GPU""" )
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : Any = self.dummy_cond_unet
lowerCAmelCase__ : Any = PNDMScheduler(skip_prk_steps=__UpperCAmelCase )
lowerCAmelCase__ : int = self.dummy_vae
lowerCAmelCase__ : Union[str, Any] = self.dummy_text_encoder
lowerCAmelCase__ : Union[str, Any] = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
lowerCAmelCase__ : Tuple = 77
lowerCAmelCase__ : Union[str, Any] = self.dummy_image.to(__UpperCAmelCase )
# put models in fp16
lowerCAmelCase__ : Tuple = unet.half()
lowerCAmelCase__ : str = vae.half()
lowerCAmelCase__ : Union[str, Any] = bert.half()
# make sure here that pndm scheduler skips prk
lowerCAmelCase__ : Dict = AltDiffusionImgaImgPipeline(
unet=__UpperCAmelCase ,scheduler=__UpperCAmelCase ,vae=__UpperCAmelCase ,text_encoder=__UpperCAmelCase ,tokenizer=__UpperCAmelCase ,safety_checker=__UpperCAmelCase ,feature_extractor=self.dummy_extractor ,)
lowerCAmelCase__ : Optional[int] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor ,do_normalize=__UpperCAmelCase )
lowerCAmelCase__ : Optional[int] = alt_pipe.to(__UpperCAmelCase )
alt_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
lowerCAmelCase__ : str = """A painting of a squirrel eating a burger"""
lowerCAmelCase__ : int = torch.manual_seed(0 )
lowerCAmelCase__ : Optional[Any] = alt_pipe(
[prompt] ,generator=__UpperCAmelCase ,num_inference_steps=2 ,output_type="""np""" ,image=__UpperCAmelCase ,).images
assert image.shape == (1, 32, 32, 3)
@unittest.skipIf(torch_device != """cuda""" ,"""This test requires a GPU""" )
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
# resize to resolution that is divisible by 8 but not 16 or 32
lowerCAmelCase__ : List[Any] = init_image.resize((760, 504) )
lowerCAmelCase__ : Tuple = """BAAI/AltDiffusion"""
lowerCAmelCase__ : str = AltDiffusionImgaImgPipeline.from_pretrained(
__UpperCAmelCase ,safety_checker=__UpperCAmelCase ,)
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
pipe.enable_attention_slicing()
lowerCAmelCase__ : Union[str, Any] = """A fantasy landscape, trending on artstation"""
lowerCAmelCase__ : Optional[Any] = torch.manual_seed(0 )
lowerCAmelCase__ : Union[str, Any] = pipe(
prompt=__UpperCAmelCase ,image=__UpperCAmelCase ,strength=0.7_5 ,guidance_scale=7.5 ,generator=__UpperCAmelCase ,output_type="""np""" ,)
lowerCAmelCase__ : List[Any] = output.images[0]
lowerCAmelCase__ : str = image[255:258, 383:386, -1]
assert image.shape == (504, 760, 3)
lowerCAmelCase__ : Any = np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class lowerCAmelCase_( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self ) -> int:
lowerCAmelCase__ : Dict = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase__ : Optional[int] = init_image.resize((768, 512) )
lowerCAmelCase__ : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy""" )
lowerCAmelCase__ : List[str] = """BAAI/AltDiffusion"""
lowerCAmelCase__ : Optional[Any] = AltDiffusionImgaImgPipeline.from_pretrained(
__UpperCAmelCase ,safety_checker=__UpperCAmelCase ,)
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
pipe.enable_attention_slicing()
lowerCAmelCase__ : int = """A fantasy landscape, trending on artstation"""
lowerCAmelCase__ : List[str] = torch.manual_seed(0 )
lowerCAmelCase__ : List[str] = pipe(
prompt=__UpperCAmelCase ,image=__UpperCAmelCase ,strength=0.7_5 ,guidance_scale=7.5 ,generator=__UpperCAmelCase ,output_type="""np""" ,)
lowerCAmelCase__ : List[Any] = output.images[0]
assert image.shape == (512, 768, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image ).max() < 1E-2
| 37
|
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
_lowerCAmelCase = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
print('''Torch version:''', None)
try:
import deepspeed
print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
print('''DeepSpeed version:''', None)
try:
import tensorflow as tf
print('''TensorFlow version:''', tf.__version__)
print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
print('''TensorFlow version:''', None)
| 37
| 1
|
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=False ):
try:
import torch # noqa: F401
except ImportError:
logger.error(
"Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
" instructions." )
raise
if not is_sharded:
lowercase :Tuple = os.path.abspath(lowerCamelCase )
logger.info(F"Loading PyTorch weights from {pt_path}" )
lowercase :str = torch.load(lowerCamelCase, map_location="cpu" )
logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters." )
lowercase :int = convert_pytorch_state_dict_to_flax(lowerCamelCase, lowerCamelCase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
lowercase :Union[str, Any] = convert_pytorch_sharded_state_dict_to_flax(lowerCamelCase, lowerCamelCase )
return flax_state_dict
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, ):
def is_key_or_prefix_key_in_dict(lowerCamelCase ) -> bool:
return len(set(lowerCamelCase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
lowercase :Optional[int] = pt_tuple_key[:-1] + ("scale",)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
lowercase :List[Any] = pt_tuple_key[:-1] + ("mean",)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
lowercase :Optional[Any] = pt_tuple_key[:-1] + ("var",)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
lowercase :int = pt_tuple_key[:-1] + ("embedding",)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowercase :int = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(lowerCamelCase ):
lowercase :str = pt_tensor.transpose(2, 3, 1, 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowercase :Dict = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowerCamelCase ):
lowercase :List[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowercase :Tuple = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowercase :Tuple = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
lowercase :str = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
lowercase :Any = pt_tuple_key[-2] + "_g"
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
lowercase :List[Any] = pt_tuple_key[-2] + "_v"
if name is not None:
lowercase :List[Any] = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
# convert pytorch tensor to numpy
lowercase :str = {k: v.numpy() for k, v in pt_state_dict.items()}
lowercase :List[str] = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
lowercase :Any = flax_model.params["params"]
else:
lowercase :Union[str, Any] = flax_model.params
lowercase :Dict = flatten_dict(lowerCamelCase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowercase :Tuple = flatten_dict(flax_model.params["batch_stats"] )
random_flax_state_dict.update(lowerCamelCase )
lowercase :Union[str, Any] = {}
lowercase :Tuple = (model_prefix not in flax_model_params) and (
model_prefix in {k.split("." )[0] for k in pt_state_dict.keys()}
)
lowercase :Dict = (model_prefix in flax_model_params) and (
model_prefix not in {k.split("." )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase :Any = tuple(pt_key.split("." ) )
# remove base model prefix if necessary
lowercase :Dict = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase :Tuple = pt_tuple_key[1:]
# Correctly rename weight parameters
lowercase , lowercase :List[Any] = rename_key_and_reshape_tensor(
lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
# add model prefix if necessary
lowercase :Tuple = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowercase :Optional[int] = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
lowercase :Optional[Any] = jnp.asarray(lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(lowerCamelCase, lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
lowercase :int = jnp.asarray(lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
lowercase :List[str] = jnp.asarray(lowerCamelCase )
return unflatten_dict(lowerCamelCase )
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
import torch
# Load the index
lowercase :Tuple = {}
for shard_file in shard_filenames:
# load using msgpack utils
lowercase :List[Any] = torch.load(lowerCamelCase )
lowercase :Optional[Any] = {k: v.numpy() for k, v in pt_state_dict.items()}
lowercase :Tuple = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowercase :Dict = flax_model.params["params"]
lowercase :Tuple = flatten_dict(lowerCamelCase )
random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"] ) )
else:
lowercase :str = flax_model.params
lowercase :List[str] = flatten_dict(lowerCamelCase )
lowercase :Tuple = (model_prefix not in flax_model_params) and (
model_prefix in {k.split("." )[0] for k in pt_state_dict.keys()}
)
lowercase :Tuple = (model_prefix in flax_model_params) and (
model_prefix not in {k.split("." )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase :str = tuple(pt_key.split("." ) )
# remove base model prefix if necessary
lowercase :Any = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase :Optional[int] = pt_tuple_key[1:]
# Correctly rename weight parameters
lowercase , lowercase :Optional[int] = rename_key_and_reshape_tensor(
lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
# add model prefix if necessary
lowercase :Any = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowercase :str = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
lowercase :Union[str, Any] = jnp.asarray(lowerCamelCase )
continue
if "var" in flax_key[-1]:
lowercase :Tuple = jnp.asarray(lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(lowerCamelCase, lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
lowercase :Tuple = jnp.asarray(lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
lowercase :str = jnp.asarray(lowerCamelCase )
return unflatten_dict(lowerCamelCase )
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
lowercase :str = os.path.abspath(lowerCamelCase )
logger.info(F"Loading Flax weights from {flax_checkpoint_path}" )
# import correct flax class
lowercase :Tuple = getattr(lowerCamelCase, "Flax" + model.__class__.__name__ )
# load flax weight dict
with open(lowerCamelCase, "rb" ) as state_f:
try:
lowercase :Any = from_bytes(lowerCamelCase, state_f.read() )
except UnpicklingError:
raise EnvironmentError(F"Unable to convert {flax_checkpoint_path} to Flax deserializable object. " )
return load_flax_weights_in_pytorch_model(lowerCamelCase, lowerCamelCase )
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
try:
import torch # noqa: F401
except ImportError:
logger.error(
"Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
" instructions." )
raise
# check if we have bf16 weights
lowercase :Optional[Any] = flatten_dict(jax.tree_util.tree_map(lambda lowerCamelCase : x.dtype == jnp.bfloataa, lowerCamelCase ) ).values()
if any(lowerCamelCase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
"before loading those in PyTorch model." )
lowercase :Union[str, Any] = jax.tree_util.tree_map(
lambda lowerCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params, lowerCamelCase )
lowercase :Optional[Any] = flatten_dict(lowerCamelCase )
lowercase :Tuple = pt_model.state_dict()
lowercase :int = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split("." )[0] for k in pt_model_dict.keys()}
)
lowercase :int = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split("." )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
lowercase :str = []
lowercase :str = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
lowercase :str = flax_key_tuple[0] == pt_model.base_model_prefix
lowercase :Dict = ".".join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase :Union[str, Any] = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
lowercase :Optional[int] = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(lowerCamelCase ) not in pt_model_dict:
# conv layer
lowercase :str = flax_key_tuple[:-1] + ("weight",)
lowercase :int = jnp.transpose(lowerCamelCase, (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase ) not in pt_model_dict:
# linear layer
lowercase :Dict = flax_key_tuple[:-1] + ("weight",)
lowercase :Union[str, Any] = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
lowercase :List[str] = flax_key_tuple[:-1] + ("weight",)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
lowercase :Union[str, Any] = flax_key_tuple[:-1] + ("running_mean",)
elif "var" in flax_key_tuple[-1]:
lowercase :Optional[Any] = flax_key_tuple[:-1] + ("running_var",)
if "batch_stats" in flax_state:
lowercase :List[str] = ".".join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
lowercase :int = ".".join(lowerCamelCase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
lowercase :List[str] = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
lowercase :Optional[int] = key.split("." )
lowercase :Dict = None
if key_components[-3::2] == ["parametrizations", "original0"]:
lowercase :int = key_components[-2] + "_g"
elif key_components[-3::2] == ["parametrizations", "original1"]:
lowercase :Union[str, Any] = key_components[-2] + "_v"
if name is not None:
lowercase :str = key_components[:-3] + [name]
lowercase :Tuple = ".".join(lowerCamelCase )
lowercase :List[Any] = key
if flax_key in special_pt_names:
lowercase :Any = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}." )
else:
# add weight to pytorch dict
lowercase :Union[str, Any] = np.asarray(lowerCamelCase ) if not isinstance(lowerCamelCase, np.ndarray ) else flax_tensor
lowercase :Optional[Any] = torch.from_numpy(lowerCamelCase )
# remove from missing keys
missing_keys.remove(lowerCamelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(lowerCamelCase )
pt_model.load_state_dict(lowerCamelCase )
# re-transform missing_keys to list
lowercase :List[Any] = list(lowerCamelCase )
if len(lowerCamelCase ) > 0:
logger.warning(
"Some weights of the Flax model were not used when initializing the PyTorch model"
F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
" FlaxBertForSequenceClassification model)." )
else:
logger.warning(F"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n" )
if len(lowerCamelCase ) > 0:
logger.warning(
F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
" use it for predictions and inference." )
else:
logger.warning(
F"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
"If your task is similar to the task the model of the checkpoint was trained on, "
F"you can already use {pt_model.__class__.__name__} for predictions without further training." )
return pt_model
| 158
|
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( lowerCAmelCase):
_a = (DDIMParallelScheduler,)
_a = (('''eta''', 0.0), ('''num_inference_steps''', 50))
def SCREAMING_SNAKE_CASE ( self: Any , **_lowerCAmelCase: Optional[Any] ):
lowercase :List[Any] = {
"num_train_timesteps": 10_00,
"beta_start": 0.00_01,
"beta_end": 0.02,
"beta_schedule": "linear",
"clip_sample": True,
}
config.update(**_lowerCAmelCase )
return config
def SCREAMING_SNAKE_CASE ( self: str , **_lowerCAmelCase: Any ):
lowercase :Optional[int] = self.scheduler_classes[0]
lowercase :Dict = self.get_scheduler_config(**_lowerCAmelCase )
lowercase :List[str] = scheduler_class(**_lowerCAmelCase )
lowercase , lowercase :str = 10, 0.0
lowercase :List[Any] = self.dummy_model()
lowercase :int = self.dummy_sample_deter
scheduler.set_timesteps(_lowerCAmelCase )
for t in scheduler.timesteps:
lowercase :Optional[int] = model(_lowerCAmelCase , _lowerCAmelCase )
lowercase :Dict = scheduler.step(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ).prev_sample
return sample
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
for timesteps in [1_00, 5_00, 10_00]:
self.check_over_configs(num_train_timesteps=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: int ):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=_lowerCAmelCase )
lowercase :Optional[Any] = self.scheduler_classes[0]
lowercase :List[str] = self.get_scheduler_config(steps_offset=1 )
lowercase :Optional[int] = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )
def SCREAMING_SNAKE_CASE ( self: Tuple ):
for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] , [0.0_02, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_lowerCAmelCase , beta_end=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: int ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: Dict ):
self.check_over_configs(thresholding=_lowerCAmelCase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=_lowerCAmelCase , prediction_type=_lowerCAmelCase , sample_max_value=_lowerCAmelCase , )
def SCREAMING_SNAKE_CASE ( self: str ):
for t in [1, 10, 49]:
self.check_over_forward(time_step=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: int ):
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 5_00] ):
self.check_over_forward(time_step=_lowerCAmelCase , num_inference_steps=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: str ):
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=_lowerCAmelCase , eta=_lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self: str ):
lowercase :Dict = self.scheduler_classes[0]
lowercase :Tuple = self.get_scheduler_config()
lowercase :Optional[Any] = scheduler_class(**_lowerCAmelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_20 , 4_00 ) - 0.1_47_71 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_80 , 9_60 ) - 0.3_24_60 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87 , 4_86 ) - 0.0_09_79 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99 , 9_98 ) - 0.02 ) ) < 1e-5
def SCREAMING_SNAKE_CASE ( self: List[str] ):
lowercase :Union[str, Any] = self.scheduler_classes[0]
lowercase :Union[str, Any] = self.get_scheduler_config()
lowercase :Union[str, Any] = scheduler_class(**_lowerCAmelCase )
lowercase , lowercase :Union[str, Any] = 10, 0.0
scheduler.set_timesteps(_lowerCAmelCase )
lowercase :Dict = self.dummy_model()
lowercase :Dict = self.dummy_sample_deter
lowercase :Union[str, Any] = self.dummy_sample_deter + 0.1
lowercase :int = self.dummy_sample_deter - 0.1
lowercase :Dict = samplea.shape[0]
lowercase :Tuple = torch.stack([samplea, samplea, samplea] , dim=0 )
lowercase :Optional[Any] = torch.arange(_lowerCAmelCase )[0:3, None].repeat(1 , _lowerCAmelCase )
lowercase :Union[str, Any] = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
lowercase :Optional[int] = scheduler.batch_step_no_noise(_lowerCAmelCase , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , _lowerCAmelCase )
lowercase :int = torch.sum(torch.abs(_lowerCAmelCase ) )
lowercase :Optional[int] = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 11_47.79_04 ) < 1e-2
assert abs(result_mean.item() - 0.49_82 ) < 1e-3
def SCREAMING_SNAKE_CASE ( self: List[Any] ):
lowercase :int = self.full_loop()
lowercase :Optional[int] = torch.sum(torch.abs(_lowerCAmelCase ) )
lowercase :Any = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 1_72.00_67 ) < 1e-2
assert abs(result_mean.item() - 0.22_39_67 ) < 1e-3
def SCREAMING_SNAKE_CASE ( self: Any ):
lowercase :Dict = self.full_loop(prediction_type="v_prediction" )
lowercase :int = torch.sum(torch.abs(_lowerCAmelCase ) )
lowercase :Union[str, Any] = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 52.53_02 ) < 1e-2
assert abs(result_mean.item() - 0.06_84 ) < 1e-3
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
# We specify different beta, so that the first alpha is 0.99
lowercase :List[Any] = self.full_loop(set_alpha_to_one=_lowerCAmelCase , beta_start=0.01 )
lowercase :List[Any] = torch.sum(torch.abs(_lowerCAmelCase ) )
lowercase :Union[str, Any] = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 1_49.82_95 ) < 1e-2
assert abs(result_mean.item() - 0.19_51 ) < 1e-3
def SCREAMING_SNAKE_CASE ( self: Any ):
# We specify different beta, so that the first alpha is 0.99
lowercase :Tuple = self.full_loop(set_alpha_to_one=_lowerCAmelCase , beta_start=0.01 )
lowercase :str = torch.sum(torch.abs(_lowerCAmelCase ) )
lowercase :List[str] = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 1_49.07_84 ) < 1e-2
assert abs(result_mean.item() - 0.19_41 ) < 1e-3
| 158
| 1
|
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
lowercase__ = logging.get_logger(__name__)
enable_full_determinism()
class A_ ( _snake_case , _snake_case , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : int = UNetaDModel
UpperCAmelCase_ : Any = """sample"""
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Dict:
UpperCAmelCase : Tuple = 4
UpperCAmelCase : Any = 3
UpperCAmelCase : List[str] = (32, 32)
UpperCAmelCase : str = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase_ )
UpperCAmelCase : List[Any] = torch.tensor([10] ).to(lowercase_ )
return {"sample": noise, "timestep": time_step}
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
return (3, 32, 32)
@property
def UpperCAmelCase_ ( self : str ) -> Optional[int]:
return (3, 32, 32)
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
UpperCAmelCase : Tuple = {
'block_out_channels': (32, 64),
'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
'attention_head_dim': 3,
'out_channels': 3,
'in_channels': 3,
'layers_per_block': 2,
'sample_size': 32,
}
UpperCAmelCase : Optional[int] = self.dummy_input
return init_dict, inputs_dict
class A_ ( _snake_case , _snake_case , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Any = UNetaDModel
UpperCAmelCase_ : List[Any] = """sample"""
@property
def UpperCAmelCase_ ( self : str ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = 4
UpperCAmelCase : Tuple = 4
UpperCAmelCase : List[str] = (32, 32)
UpperCAmelCase : str = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase_ )
UpperCAmelCase : str = torch.tensor([10] ).to(lowercase_ )
return {"sample": noise, "timestep": time_step}
@property
def UpperCAmelCase_ ( self : List[str] ) -> Tuple:
return (4, 32, 32)
@property
def UpperCAmelCase_ ( self : Any ) -> List[str]:
return (4, 32, 32)
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[str]:
UpperCAmelCase : Dict = {
'sample_size': 32,
'in_channels': 4,
'out_channels': 4,
'layers_per_block': 2,
'block_out_channels': (32, 64),
'attention_head_dim': 32,
'down_block_types': ('DownBlock2D', 'DownBlock2D'),
'up_block_types': ('UpBlock2D', 'UpBlock2D'),
}
UpperCAmelCase : str = self.dummy_input
return init_dict, inputs_dict
def UpperCAmelCase_ ( self : str ) -> Tuple:
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update' , output_loading_info=lowercase_ )
self.assertIsNotNone(lowercase_ )
self.assertEqual(len(loading_info['missing_keys'] ) , 0 )
model.to(lowercase_ )
UpperCAmelCase : Any = model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != 'cuda' , 'This test is supposed to run on GPU' )
def UpperCAmelCase_ ( self : str ) -> Optional[int]:
UpperCAmelCase , UpperCAmelCase : List[str] = UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update' , output_loading_info=lowercase_ )
model.to(lowercase_ )
UpperCAmelCase : List[str] = model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != 'cuda' , 'This test is supposed to run on GPU' )
def UpperCAmelCase_ ( self : List[str] ) -> int:
# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
UpperCAmelCase , UpperCAmelCase : Optional[int] = UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update' , output_loading_info=lowercase_ )
model_accelerate.to(lowercase_ )
model_accelerate.eval()
UpperCAmelCase : Dict = torch.randn(
1 , model_accelerate.config.in_channels , model_accelerate.config.sample_size , model_accelerate.config.sample_size , generator=torch.manual_seed(0 ) , )
UpperCAmelCase : int = noise.to(lowercase_ )
UpperCAmelCase : List[Any] = torch.tensor([10] * noise.shape[0] ).to(lowercase_ )
UpperCAmelCase : List[str] = model_accelerate(lowercase_ , lowercase_ )['sample']
# two models don't need to stay in the device at the same time
del model_accelerate
torch.cuda.empty_cache()
gc.collect()
UpperCAmelCase , UpperCAmelCase : int = UNetaDModel.from_pretrained(
'fusing/unet-ldm-dummy-update' , output_loading_info=lowercase_ , low_cpu_mem_usage=lowercase_ )
model_normal_load.to(lowercase_ )
model_normal_load.eval()
UpperCAmelCase : str = model_normal_load(lowercase_ , lowercase_ )['sample']
assert torch_all_close(lowercase_ , lowercase_ , rtol=1E-3 )
def UpperCAmelCase_ ( self : Optional[Any] ) -> int:
UpperCAmelCase : List[str] = UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update' )
model.eval()
model.to(lowercase_ )
UpperCAmelCase : Tuple = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
UpperCAmelCase : Optional[Any] = noise.to(lowercase_ )
UpperCAmelCase : Optional[Any] = torch.tensor([10] * noise.shape[0] ).to(lowercase_ )
with torch.no_grad():
UpperCAmelCase : Tuple = model(lowercase_ , lowercase_ ).sample
UpperCAmelCase : Any = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
UpperCAmelCase : Optional[Any] = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800] )
# fmt: on
self.assertTrue(torch_all_close(lowercase_ , lowercase_ , rtol=1E-3 ) )
class A_ ( _snake_case , _snake_case , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = UNetaDModel
UpperCAmelCase_ : int = """sample"""
@property
def UpperCAmelCase_ ( self : str , lowercase_ : List[str]=(32, 32) ) -> Optional[int]:
UpperCAmelCase : int = 4
UpperCAmelCase : int = 3
UpperCAmelCase : Optional[Any] = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase_ )
UpperCAmelCase : int = torch.tensor(batch_size * [10] ).to(dtype=torch.intaa , device=lowercase_ )
return {"sample": noise, "timestep": time_step}
@property
def UpperCAmelCase_ ( self : Any ) -> Any:
return (3, 32, 32)
@property
def UpperCAmelCase_ ( self : Tuple ) -> Tuple:
return (3, 32, 32)
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
UpperCAmelCase : Tuple = {
'block_out_channels': [32, 64, 64, 64],
'in_channels': 3,
'layers_per_block': 1,
'out_channels': 3,
'time_embedding_type': 'fourier',
'norm_eps': 1E-6,
'mid_block_scale_factor': math.sqrt(2.0 ),
'norm_num_groups': None,
'down_block_types': [
'SkipDownBlock2D',
'AttnSkipDownBlock2D',
'SkipDownBlock2D',
'SkipDownBlock2D',
],
'up_block_types': [
'SkipUpBlock2D',
'SkipUpBlock2D',
'AttnSkipUpBlock2D',
'SkipUpBlock2D',
],
}
UpperCAmelCase : Any = self.dummy_input
return init_dict, inputs_dict
@slow
def UpperCAmelCase_ ( self : Dict ) -> str:
UpperCAmelCase , UpperCAmelCase : Optional[int] = UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256' , output_loading_info=lowercase_ )
self.assertIsNotNone(lowercase_ )
self.assertEqual(len(loading_info['missing_keys'] ) , 0 )
model.to(lowercase_ )
UpperCAmelCase : Tuple = self.dummy_input
UpperCAmelCase : Tuple = floats_tensor((4, 3) + (256, 256) ).to(lowercase_ )
UpperCAmelCase : Optional[int] = noise
UpperCAmelCase : Any = model(**lowercase_ )
assert image is not None, "Make sure output is not None"
@slow
def UpperCAmelCase_ ( self : Union[str, Any] ) -> int:
UpperCAmelCase : Optional[Any] = UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256' )
model.to(lowercase_ )
UpperCAmelCase : Tuple = 4
UpperCAmelCase : Optional[int] = 3
UpperCAmelCase : Optional[int] = (256, 256)
UpperCAmelCase : str = torch.ones((batch_size, num_channels) + sizes ).to(lowercase_ )
UpperCAmelCase : Any = torch.tensor(batch_size * [1E-4] ).to(lowercase_ )
with torch.no_grad():
UpperCAmelCase : Optional[int] = model(lowercase_ , lowercase_ ).sample
UpperCAmelCase : Optional[int] = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
UpperCAmelCase : Optional[Any] = torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_0980.7129, -2_0028.8535, 8148.2822, 2342.2905, 567.7608] )
# fmt: on
self.assertTrue(torch_all_close(lowercase_ , lowercase_ , rtol=1E-2 ) )
def UpperCAmelCase_ ( self : str ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update' )
model.to(lowercase_ )
UpperCAmelCase : Any = 4
UpperCAmelCase : int = 3
UpperCAmelCase : Optional[Any] = (32, 32)
UpperCAmelCase : Optional[int] = torch.ones((batch_size, num_channels) + sizes ).to(lowercase_ )
UpperCAmelCase : Optional[Any] = torch.tensor(batch_size * [1E-4] ).to(lowercase_ )
with torch.no_grad():
UpperCAmelCase : Union[str, Any] = model(lowercase_ , lowercase_ ).sample
UpperCAmelCase : Tuple = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
UpperCAmelCase : int = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256] )
# fmt: on
self.assertTrue(torch_all_close(lowercase_ , lowercase_ , rtol=1E-2 ) )
def UpperCAmelCase_ ( self : Optional[int] ) -> Tuple:
# not required for this model
pass
| 151
|
'''simple docstring'''
import numpy as np
def UpperCamelCase( UpperCAmelCase_ ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 151
| 1
|
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : str ):
"""simple docstring"""
return " ".join(
''.join(word[::-1] ) if len(lowerCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 275
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase :str = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase :List[str] = ['''VisionEncoderDecoderModel''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase :List[str] = ['''TFVisionEncoderDecoderModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase :Optional[Any] = ['''FlaxVisionEncoderDecoderModel''']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
lowerCAmelCase :Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 275
| 1
|
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ ( __a , unittest.TestCase ):
lowerCAmelCase__ = XLNetTokenizer
lowerCAmelCase__ = XLNetTokenizerFast
lowerCAmelCase__ = True
lowerCAmelCase__ = True
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase__ : Tuple = XLNetTokenizer(_A , keep_accents=_A )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def lowercase_ ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = '''<s>'''
UpperCAmelCase__ : List[str] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<eod>''' )
self.assertEqual(len(_A ) , 1_006 )
def lowercase_ ( self : Any ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : int = XLNetTokenizer(_A , keep_accents=_A )
UpperCAmelCase__ : int = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_A , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_A ) , [285, 46, 10, 170, 382] )
UpperCAmelCase__ : str = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
UpperCAmelCase__ : Union[str, Any] = tokenizer.convert_tokens_to_ids(_A )
self.assertListEqual(_A , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
UpperCAmelCase__ : List[str] = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = XLNetTokenizer(_A , do_lower_case=_A )
UpperCAmelCase__ : int = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''''',
'''i''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''se''',
'''.''',
] , )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''▁he''', '''ll''', '''o'''] )
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = XLNetTokenizer(_A , do_lower_case=_A )
UpperCAmelCase__ : Union[str, Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''se''',
'''.''',
] , )
@slow
def lowercase_ ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : int = XLNetTokenizer.from_pretrained('''xlnet-base-cased''' )
UpperCAmelCase__ : Optional[int] = tokenizer.encode('''sequence builders''' , add_special_tokens=_A )
UpperCAmelCase__ : int = tokenizer.encode('''multi-sequence build''' , add_special_tokens=_A )
UpperCAmelCase__ : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(_A )
UpperCAmelCase__ : List[str] = tokenizer.build_inputs_with_special_tokens(_A , _A )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = {'''input_ids''': [[17, 21_442, 270, 17, 10, 14_645, 318, 34, 17, 4_546, 3_145, 787, 13, 7_752, 22_018, 23, 21, 17, 4_546, 3_145, 787, 13, 3_352, 14_431, 13, 5_500, 11, 1_176, 580, 13, 16_819, 4_797, 23, 17, 10, 17_135, 658, 19, 457, 7_932, 13, 184, 19, 3_154, 17_135, 6_468, 19, 1_404, 12_269, 19, 4_229, 5_356, 16_264, 46, 19, 17, 20_545, 10_395, 9, 9, 9, 11, 28, 6_421, 9_531, 20_729, 17, 10, 353, 17_022, 11, 21, 6_421, 9_531, 16_949, 17, 10, 11_509, 753, 11, 33, 95, 2_421, 7_385, 956, 14_431, 2_626, 25, 842, 7_385, 4_836, 21, 1_429, 2_272, 9_855, 3_120, 161, 24_738, 19, 13_203, 658, 218, 787, 21, 430, 18_482, 847, 2_637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 22_178, 27, 1_064, 22, 956, 13, 11_101, 1_429, 5_854, 24_313, 18_953, 40, 422, 24_366, 68, 1_758, 37, 10_483, 14_257, 31, 207, 263, 21, 203, 3_773, 25, 71, 9_735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2_049, 3_442, 17, 13_894, 3_380, 23, 95, 18, 17_634, 2_288, 9, 4, 3]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name='''xlnet-base-cased''' , revision='''c841166438c31ec7ca9a106dee7bb312b73ae511''' , )
| 181
|
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def a__ ( lowerCAmelCase__ ) -> Optional[Any]:
UpperCAmelCase__ : Any = min(lowerCAmelCase__ ) # min() finds the minimum value
UpperCAmelCase__ : Optional[int] = max(lowerCAmelCase__ ) # max() finds the maximum value
UpperCAmelCase__ : int = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
UpperCAmelCase__ : Any = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(lowerCAmelCase__ , lowerCAmelCase__ ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
UpperCAmelCase__ : Optional[int] = 0
for count in range(lowerCAmelCase__ ):
while holes[count] > 0:
holes[count] -= 1
UpperCAmelCase__ : Dict = count + min_val
i += 1
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase__ : List[str] = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(lowerCAmelCase__ )
print('''Sorted order is:''' , ''' '''.join(lowerCAmelCase__ ) )
if __name__ == "__main__":
main()
| 181
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """cvt"""
def __init__( self : int , __UpperCamelCase : Union[str, Any]=3 , __UpperCamelCase : Optional[int]=[7, 3, 3] , __UpperCamelCase : List[str]=[4, 2, 2] , __UpperCamelCase : Optional[int]=[2, 1, 1] , __UpperCamelCase : Optional[int]=[6_4, 1_9_2, 3_8_4] , __UpperCamelCase : Tuple=[1, 3, 6] , __UpperCamelCase : Optional[Any]=[1, 2, 1_0] , __UpperCamelCase : str=[4.0, 4.0, 4.0] , __UpperCamelCase : Optional[int]=[0.0, 0.0, 0.0] , __UpperCamelCase : Optional[int]=[0.0, 0.0, 0.0] , __UpperCamelCase : List[Any]=[0.0, 0.0, 0.1] , __UpperCamelCase : str=[True, True, True] , __UpperCamelCase : Union[str, Any]=[False, False, True] , __UpperCamelCase : Union[str, Any]=["dw_bn", "dw_bn", "dw_bn"] , __UpperCamelCase : Tuple=[3, 3, 3] , __UpperCamelCase : Tuple=[1, 1, 1] , __UpperCamelCase : List[Any]=[2, 2, 2] , __UpperCamelCase : Tuple=[1, 1, 1] , __UpperCamelCase : List[str]=[1, 1, 1] , __UpperCamelCase : Union[str, Any]=0.0_2 , __UpperCamelCase : Any=1e-12 , **__UpperCamelCase : Tuple , )->Tuple:
super().__init__(**__UpperCamelCase )
_UpperCAmelCase = num_channels
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = depth
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = drop_rate
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = cls_token
_UpperCAmelCase = qkv_projection_method
_UpperCAmelCase = kernel_qkv
_UpperCAmelCase = padding_kv
_UpperCAmelCase = stride_kv
_UpperCAmelCase = padding_q
_UpperCAmelCase = stride_q
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
| 326
|
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
UpperCamelCase__ = None
__A : Union[str, Any] = namedtuple("CoinsDistribResult", "moves excess")
def lowercase ( _SCREAMING_SNAKE_CASE : TreeNode | None ):
'''simple docstring'''
if root is None:
return 0
# Validation
def count_nodes(_SCREAMING_SNAKE_CASE : TreeNode | None ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(_SCREAMING_SNAKE_CASE : TreeNode | None ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(_SCREAMING_SNAKE_CASE ) != count_coins(_SCREAMING_SNAKE_CASE ):
raise ValueError('''The nodes number should be same as the number of coins''' )
# Main calculation
def get_distrib(_SCREAMING_SNAKE_CASE : TreeNode | None ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
_UpperCAmelCase , _UpperCAmelCase = get_distrib(node.left )
_UpperCAmelCase , _UpperCAmelCase = get_distrib(node.right )
_UpperCAmelCase = 1 - left_distrib_excess
_UpperCAmelCase = 1 - right_distrib_excess
_UpperCAmelCase = (
left_distrib_moves
+ right_distrib_moves
+ abs(_SCREAMING_SNAKE_CASE )
+ abs(_SCREAMING_SNAKE_CASE )
)
_UpperCAmelCase = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return get_distrib(_SCREAMING_SNAKE_CASE )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 326
| 1
|
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
snake_case_ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ = 16000 ):
UpperCAmelCase = int(round(sample_rate * max_length ) )
if len(lowercase_ ) <= sample_length:
return wav
UpperCAmelCase = randint(0 , len(lowercase_ ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class A_ :
"""simple docstring"""
__UpperCamelCase = field(default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Name of a dataset from the datasets package"""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """A file containing the training audio paths and labels."""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """A file containing the validation audio paths and labels."""} )
__UpperCamelCase = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
__UpperCamelCase = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
__UpperCamelCase = field(
default="""audio""" , metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""} , )
__UpperCamelCase = field(
default="""label""" , metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
__UpperCamelCase = field(
default=20 , metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""} , )
@dataclass
class A_ :
"""simple docstring"""
__UpperCamelCase = field(
default="""facebook/wav2vec2-base""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""} )
__UpperCamelCase = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Name or path of preprocessor config."""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def UpperCAmelCase__ ( self :Dict ) -> Union[str, Any]:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'The argument `--freeze_feature_extractor` is deprecated and '
'will be removed in a future version. Use `--freeze_feature_encoder`'
'instead. Setting `freeze_feature_encoder==True`.' , lowercase_ , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'The argument `--freeze_feature_extractor` is deprecated and '
'should not be used in combination with `--freeze_feature_encoder`.'
'Only make use of `--freeze_feature_encoder`.' )
def _lowerCAmelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_audio_classification' , lowercase_ , lowercase_ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(lowercase_ )
transformers.utils.logging.set_verbosity(lowercase_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} """
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to train from scratch.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset and prepare it for the audio classification task.
UpperCAmelCase = DatasetDict()
UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
'Make sure to set `--audio_column_name` to the correct audio column - one of '
F"""{', '.join(raw_datasets['train'].column_names )}.""" )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
'Make sure to set `--label_column_name` to the correct text column - one of '
F"""{', '.join(raw_datasets['train'].column_names )}.""" )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
UpperCAmelCase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
UpperCAmelCase = feature_extractor.model_input_names[0]
def train_transforms(lowercase_ ):
UpperCAmelCase = []
for audio in batch[data_args.audio_column_name]:
UpperCAmelCase = random_subsample(
audio['array'] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(lowercase_ )
UpperCAmelCase = feature_extractor(lowercase_ , sampling_rate=feature_extractor.sampling_rate )
UpperCAmelCase = {model_input_name: inputs.get(lowercase_ )}
UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(lowercase_ ):
UpperCAmelCase = [audio['array'] for audio in batch[data_args.audio_column_name]]
UpperCAmelCase = feature_extractor(lowercase_ , sampling_rate=feature_extractor.sampling_rate )
UpperCAmelCase = {model_input_name: inputs.get(lowercase_ )}
UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
UpperCAmelCase = raw_datasets['train'].features[data_args.label_column_name].names
UpperCAmelCase , UpperCAmelCase = {}, {}
for i, label in enumerate(lowercase_ ):
UpperCAmelCase = str(lowercase_ )
UpperCAmelCase = label
# Load the accuracy metric from the datasets package
UpperCAmelCase = evaluate.load('accuracy' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(lowercase_ ):
UpperCAmelCase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=lowercase_ , references=eval_pred.label_ids )
UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(lowercase_ ) , labelaid=lowercase_ , idalabel=lowercase_ , finetuning_task='audio-classification' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
UpperCAmelCase = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowercase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
UpperCAmelCase = (
raw_datasets['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(lowercase_ , output_all_columns=lowercase_ )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
UpperCAmelCase = (
raw_datasets['eval'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(lowercase_ , output_all_columns=lowercase_ )
# Initialize our trainer
UpperCAmelCase = Trainer(
model=lowercase_ , args=lowercase_ , train_dataset=raw_datasets['train'] if training_args.do_train else None , eval_dataset=raw_datasets['eval'] if training_args.do_eval else None , compute_metrics=lowercase_ , tokenizer=lowercase_ , )
# Training
if training_args.do_train:
UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCAmelCase = last_checkpoint
UpperCAmelCase = trainer.train(resume_from_checkpoint=lowercase_ )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
UpperCAmelCase = trainer.evaluate()
trainer.log_metrics('eval' , lowercase_ )
trainer.save_metrics('eval' , lowercase_ )
# Write model card and (optionally) push to hub
UpperCAmelCase = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'audio-classification',
'dataset': data_args.dataset_name,
'tags': ['audio-classification'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase_ )
else:
trainer.create_model_card(**lowercase_ )
if __name__ == "__main__":
main()
| 78
|
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ):
UpperCAmelCase = x
UpperCAmelCase = y
for step in range(lowercase_ ): # noqa: B007
UpperCAmelCase = a * a - b * b + x
UpperCAmelCase = 2 * a * b + y
UpperCAmelCase = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def _lowerCAmelCase ( lowercase_ ):
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def _lowerCAmelCase ( lowercase_ ):
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(lowercase_ , 1 , 1 ) )
def _lowerCAmelCase ( lowercase_ = 800 , lowercase_ = 600 , lowercase_ = -0.6 , lowercase_ = 0 , lowercase_ = 3.2 , lowercase_ = 50 , lowercase_ = True , ):
UpperCAmelCase = Image.new('RGB' , (image_width, image_height) )
UpperCAmelCase = img.load()
# loop through the image-coordinates
for image_x in range(lowercase_ ):
for image_y in range(lowercase_ ):
# determine the figure-coordinates based on the image-coordinates
UpperCAmelCase = figure_width / image_width * image_height
UpperCAmelCase = figure_center_x + (image_x / image_width - 0.5) * figure_width
UpperCAmelCase = figure_center_y + (image_y / image_height - 0.5) * figure_height
UpperCAmelCase = get_distance(lowercase_ , lowercase_ , lowercase_ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
UpperCAmelCase = get_color_coded_rgb(lowercase_ )
else:
UpperCAmelCase = get_black_and_white_rgb(lowercase_ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
snake_case_ = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 78
| 1
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self: List[Any] ):
__lowerCamelCase : int = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
__lowerCamelCase : str = get_activation('gelu' )
self.assertTrue(torch.allclose(gelu_python(a ) , torch_builtin(a ) ) )
self.assertFalse(torch.allclose(gelu_python(a ) , gelu_new(a ) ) )
def _snake_case ( self: Optional[int] ):
__lowerCamelCase : int = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
__lowerCamelCase : int = get_activation('gelu' )
__lowerCamelCase : Tuple = get_activation('gelu_10' )
__lowerCamelCase : Dict = torch_builtin(a )
__lowerCamelCase : List[str] = geluaa(a )
__lowerCamelCase : Dict = torch.where(y_gelu_aa < 1_0.0 , 1 , 0 )
self.assertTrue(torch.max(a ).item() == 1_0.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def _snake_case ( self: List[str] ):
get_activation('gelu' )
get_activation('gelu_10' )
get_activation('gelu_fast' )
get_activation('gelu_new' )
get_activation('gelu_python' )
get_activation('gelu_pytorch_tanh' )
get_activation('linear' )
get_activation('mish' )
get_activation('quick_gelu' )
get_activation('relu' )
get_activation('sigmoid' )
get_activation('silu' )
get_activation('swish' )
get_activation('tanh' )
with self.assertRaises(a ):
get_activation('bogus' )
with self.assertRaises(a ):
get_activation(a )
def _snake_case ( self: List[Any] ):
__lowerCamelCase : Optional[int] = get_activation('gelu' )
__lowerCamelCase : Optional[int] = 1
__lowerCamelCase : Tuple = get_activation('gelu' )
self.assertEqual(acta.a , 1 )
with self.assertRaises(a ):
__lowerCamelCase : List[str] = acta.a
| 194
|
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Dict = prime_factors(SCREAMING_SNAKE_CASE__ )
if is_square_free(SCREAMING_SNAKE_CASE__ ):
return -1 if len(SCREAMING_SNAKE_CASE__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 194
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_SCREAMING_SNAKE_CASE = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 158
|
'''simple docstring'''
import math
import unittest
def __a(SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class lowerCAmelCase_ ( unittest.TestCase ):
def _snake_case ( self ) -> str:
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(11 ) )
self.assertTrue(is_prime(13 ) )
self.assertTrue(is_prime(17 ) )
self.assertTrue(is_prime(19 ) )
self.assertTrue(is_prime(23 ) )
self.assertTrue(is_prime(29 ) )
def _snake_case ( self ) -> List[Any]:
with self.assertRaises(_lowerCAmelCase ):
is_prime(-19 )
self.assertFalse(
is_prime(0 ) , "Zero doesn't have any positive factors, primes must have exactly two." , )
self.assertFalse(
is_prime(1 ) , "One only has 1 positive factor, primes must have exactly two." , )
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main()
| 158
| 1
|
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" ,[
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" ,num_bytes=13_37 ,num_examples=42 ,dataset_name="""my_dataset""" )} ),
SplitDict({"""train""": SplitInfo(name="""train""" ,num_bytes=13_37 ,num_examples=42 )} ),
SplitDict({"""train""": SplitInfo()} ),
] ,)
def __lowerCamelCase ( snake_case__ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = split_dict._to_yaml_list()
assert len(snake_case__ ) == len(snake_case__ )
_SCREAMING_SNAKE_CASE = SplitDict._from_yaml_list(snake_case__ )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
_SCREAMING_SNAKE_CASE = None
# the split name of split_dict takes over the name of the split info object
_SCREAMING_SNAKE_CASE = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"""split_info""" ,[SplitInfo(), SplitInfo(dataset_name=snake_case__ ), SplitInfo(dataset_name="""my_dataset""" )] )
def __lowerCamelCase ( snake_case__ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = asdict(SplitDict({"""train""": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 125
|
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __UpperCAmelCase (_UpperCAmelCase ):
__snake_case : Dict = "naver-clova-ix/donut-base-finetuned-docvqa"
__snake_case : Dict = (
"This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
"should be the document containing the information, as well as a `question` that is the question about the "
"document. It returns a text that contains the answer to the question."
)
__snake_case : Dict = "document_qa"
__snake_case : Any = AutoProcessor
__snake_case : int = VisionEncoderDecoderModel
__snake_case : Union[str, Any] = ["image", "text"]
__snake_case : Optional[int] = ["text"]
def __init__( self: List[Any] , *UpperCAmelCase_: Optional[int] , **UpperCAmelCase_: List[str] ):
'''simple docstring'''
if not is_vision_available():
raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool.""" )
super().__init__(*UpperCAmelCase_ , **UpperCAmelCase_ )
def UpperCamelCase ( self: Optional[int] , UpperCAmelCase_: "Image" , UpperCAmelCase_: str ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
_SCREAMING_SNAKE_CASE = task_prompt.replace("""{user_input}""" , UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.pre_processor.tokenizer(
UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ , return_tensors="""pt""" ).input_ids
_SCREAMING_SNAKE_CASE = self.pre_processor(UpperCAmelCase_ , return_tensors="""pt""" ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def UpperCamelCase ( self: Dict , UpperCAmelCase_: str ):
'''simple docstring'''
return self.model.generate(
inputs["""pixel_values"""].to(self.device ) , decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=UpperCAmelCase_ , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=UpperCAmelCase_ , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=UpperCAmelCase_ , ).sequences
def UpperCamelCase ( self: Dict , UpperCAmelCase_: Tuple ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.pre_processor.batch_decode(UpperCAmelCase_ )[0]
_SCREAMING_SNAKE_CASE = sequence.replace(self.pre_processor.tokenizer.eos_token , """""" )
_SCREAMING_SNAKE_CASE = sequence.replace(self.pre_processor.tokenizer.pad_token , """""" )
_SCREAMING_SNAKE_CASE = re.sub(R"""<.*?>""" , """""" , UpperCAmelCase_ , count=1 ).strip() # remove first task start token
_SCREAMING_SNAKE_CASE = self.pre_processor.tokenajson(UpperCAmelCase_ )
return sequence["answer"]
| 125
| 1
|
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class __lowercase (_UpperCAmelCase ):
_UpperCamelCase = 42
_UpperCamelCase = jnp.floataa
_UpperCamelCase = True
def UpperCamelCase__ ( self ) ->str:
'''simple docstring'''
super().setup()
__lowerCAmelCase : int = nn.Dense(5 , dtype=self.dtype )
def __call__( self , *A_ , **A_ ) ->Optional[int]:
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = super().__call__(*A_ , **A_ )
__lowerCAmelCase : List[str] = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class __lowercase (_UpperCAmelCase ):
_UpperCamelCase = FlaxBigBirdForNaturalQuestionsModule
def _lowercase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
def cross_entropy(lowercase__ , lowercase__ , lowercase__=None ):
__lowerCAmelCase : int = logits.shape[-1]
__lowerCAmelCase : Union[str, Any] = (labels[..., None] == jnp.arange(lowercase__ )[None]).astype('''f4''' )
__lowerCAmelCase : Optional[Any] = jax.nn.log_softmax(lowercase__ , axis=-1 )
__lowerCAmelCase : Any = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
__lowerCAmelCase : Union[str, Any] = reduction(lowercase__ )
return loss
__lowerCAmelCase : str = partial(lowercase__ , reduction=jnp.mean )
__lowerCAmelCase : List[str] = cross_entropy(lowercase__ , lowercase__ )
__lowerCAmelCase : str = cross_entropy(lowercase__ , lowercase__ )
__lowerCAmelCase : Dict = cross_entropy(lowercase__ , lowercase__ )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __lowercase :
_UpperCamelCase = "google/bigbird-roberta-base"
_UpperCamelCase = 3000
_UpperCamelCase = 10500
_UpperCamelCase = 128
_UpperCamelCase = 3
_UpperCamelCase = 1
_UpperCamelCase = 5
# tx_args
_UpperCamelCase = 3E-5
_UpperCamelCase = 0.0
_UpperCamelCase = 20000
_UpperCamelCase = 0.0095
_UpperCamelCase = "bigbird-roberta-natural-questions"
_UpperCamelCase = "training-expt"
_UpperCamelCase = "data/nq-training.jsonl"
_UpperCamelCase = "data/nq-validation.jsonl"
def UpperCamelCase__ ( self ) ->Dict:
'''simple docstring'''
os.makedirs(self.base_dir , exist_ok=A_ )
__lowerCAmelCase : Optional[Any] = os.path.join(self.base_dir , self.save_dir )
__lowerCAmelCase : str = self.batch_size_per_device * jax.device_count()
@dataclass
class __lowercase :
_UpperCamelCase = 42
_UpperCamelCase = 4096 # no dynamic padding on TPUs
def __call__( self , A_ ) ->Tuple:
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = self.collate_fn(A_ )
__lowerCAmelCase : int = jax.tree_util.tree_map(A_ , A_ )
return batch
def UpperCamelCase__ ( self , A_ ) ->List[str]:
'''simple docstring'''
__lowerCAmelCase, __lowerCAmelCase : Optional[Any] = self.fetch_inputs(features['''input_ids'''] )
__lowerCAmelCase : Dict = {
'''input_ids''': jnp.array(A_ , dtype=jnp.intaa ),
'''attention_mask''': jnp.array(A_ , dtype=jnp.intaa ),
'''start_labels''': jnp.array(features['''start_token'''] , dtype=jnp.intaa ),
'''end_labels''': jnp.array(features['''end_token'''] , dtype=jnp.intaa ),
'''pooled_labels''': jnp.array(features['''category'''] , dtype=jnp.intaa ),
}
return batch
def UpperCamelCase__ ( self , A_ ) ->Union[str, Any]:
'''simple docstring'''
__lowerCAmelCase : Any = [self._fetch_inputs(A_ ) for ids in input_ids]
return zip(*A_ )
def UpperCamelCase__ ( self , A_ ) ->Dict:
'''simple docstring'''
__lowerCAmelCase : List[Any] = [1 for _ in range(len(A_ ) )]
while len(A_ ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def _lowercase ( lowercase__ , lowercase__ , lowercase__=None ):
if seed is not None:
__lowerCAmelCase : List[str] = dataset.shuffle(seed=lowercase__ )
for i in range(len(lowercase__ ) // batch_size ):
__lowerCAmelCase : Tuple = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(lowercase__ )
@partial(jax.pmap , axis_name='''batch''' )
def _lowercase ( lowercase__ , lowercase__ , **lowercase__ ):
def loss_fn(lowercase__ ):
__lowerCAmelCase : Any = model_inputs.pop('''start_labels''' )
__lowerCAmelCase : Tuple = model_inputs.pop('''end_labels''' )
__lowerCAmelCase : str = model_inputs.pop('''pooled_labels''' )
__lowerCAmelCase : Any = state.apply_fn(**lowercase__ , params=lowercase__ , dropout_rng=lowercase__ , train=lowercase__ )
__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase : Optional[int] = outputs
return state.loss_fn(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , )
__lowerCAmelCase, __lowerCAmelCase : List[str] = jax.random.split(lowercase__ )
__lowerCAmelCase : Dict = jax.value_and_grad(lowercase__ )
__lowerCAmelCase, __lowerCAmelCase : Tuple = grad_fn(state.params )
__lowerCAmelCase : List[Any] = jax.lax.pmean({'''loss''': loss} , axis_name='''batch''' )
__lowerCAmelCase : List[Any] = jax.lax.pmean(lowercase__ , '''batch''' )
__lowerCAmelCase : Optional[int] = state.apply_gradients(grads=lowercase__ )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='''batch''' )
def _lowercase ( lowercase__ , **lowercase__ ):
__lowerCAmelCase : int = model_inputs.pop('''start_labels''' )
__lowerCAmelCase : List[Any] = model_inputs.pop('''end_labels''' )
__lowerCAmelCase : str = model_inputs.pop('''pooled_labels''' )
__lowerCAmelCase : Dict = state.apply_fn(**lowercase__ , params=state.params , train=lowercase__ )
__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase : Tuple = outputs
__lowerCAmelCase : Optional[int] = state.loss_fn(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
__lowerCAmelCase : List[str] = jax.lax.pmean({'''loss''': loss} , axis_name='''batch''' )
return metrics
class __lowercase (train_state.TrainState ):
_UpperCamelCase = struct.field(pytree_node=_UpperCAmelCase )
@dataclass
class __lowercase :
_UpperCamelCase = 42
_UpperCamelCase = 42
_UpperCamelCase = 42
_UpperCamelCase = 42
_UpperCamelCase = 42
_UpperCamelCase = 42
_UpperCamelCase = None
def UpperCamelCase__ ( self , A_ , A_ , A_ , A_=None ) ->Optional[int]:
'''simple docstring'''
__lowerCAmelCase : List[str] = model.params
__lowerCAmelCase : Optional[int] = TrainState.create(
apply_fn=model.__call__ , params=A_ , tx=A_ , loss_fn=A_ , )
if ckpt_dir is not None:
__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase : Optional[int] = restore_checkpoint(A_ , A_ )
__lowerCAmelCase : str = {
'''lr''': args.lr,
'''init_lr''': args.init_lr,
'''warmup_steps''': args.warmup_steps,
'''num_train_steps''': num_train_steps,
'''weight_decay''': args.weight_decay,
}
__lowerCAmelCase, __lowerCAmelCase : Tuple = build_tx(**A_ )
__lowerCAmelCase : Optional[Any] = train_state.TrainState(
step=A_ , apply_fn=model.__call__ , params=A_ , tx=A_ , opt_state=A_ , )
__lowerCAmelCase : str = args
__lowerCAmelCase : Tuple = data_collator
__lowerCAmelCase : Union[str, Any] = lr
__lowerCAmelCase : Dict = params
__lowerCAmelCase : Any = jax_utils.replicate(A_ )
return state
def UpperCamelCase__ ( self , A_ , A_ , A_ ) ->List[Any]:
'''simple docstring'''
__lowerCAmelCase : str = self.args
__lowerCAmelCase : Dict = len(A_ ) // args.batch_size
__lowerCAmelCase : Optional[Any] = jax.random.PRNGKey(0 )
__lowerCAmelCase : Union[str, Any] = jax.random.split(A_ , jax.device_count() )
for epoch in range(args.max_epochs ):
__lowerCAmelCase : Optional[Any] = jnp.array(0 , dtype=jnp.floataa )
__lowerCAmelCase : List[Any] = get_batched_dataset(A_ , args.batch_size , seed=A_ )
__lowerCAmelCase : Dict = 0
for batch in tqdm(A_ , total=A_ , desc=f"""Running EPOCH-{epoch}""" ):
__lowerCAmelCase : Optional[int] = self.data_collator(A_ )
__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase : List[str] = self.train_step_fn(A_ , A_ , **A_ )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
if i % args.logging_steps == 0:
__lowerCAmelCase : str = jax_utils.unreplicate(state.step )
__lowerCAmelCase : Any = running_loss.item() / i
__lowerCAmelCase : str = self.scheduler_fn(state_step - 1 )
__lowerCAmelCase : Union[str, Any] = self.evaluate(A_ , A_ )
__lowerCAmelCase : int = {
'''step''': state_step.item(),
'''eval_loss''': eval_loss.item(),
'''tr_loss''': tr_loss,
'''lr''': lr.item(),
}
tqdm.write(str(A_ ) )
self.logger.log(A_ , commit=A_ )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + f"""-e{epoch}-s{i}""" , state=A_ )
def UpperCamelCase__ ( self , A_ , A_ ) ->Optional[Any]:
'''simple docstring'''
__lowerCAmelCase : Dict = get_batched_dataset(A_ , self.args.batch_size )
__lowerCAmelCase : Dict = len(A_ ) // self.args.batch_size
__lowerCAmelCase : Any = jnp.array(0 , dtype=jnp.floataa )
__lowerCAmelCase : Optional[Any] = 0
for batch in tqdm(A_ , total=A_ , desc='''Evaluating ... ''' ):
__lowerCAmelCase : Optional[int] = self.data_collator(A_ )
__lowerCAmelCase : int = self.val_step_fn(A_ , **A_ )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
return running_loss / i
def UpperCamelCase__ ( self , A_ , A_ ) ->str:
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = jax_utils.unreplicate(A_ )
print(f"""SAVING CHECKPOINT IN {save_dir}""" , end=''' ... ''' )
self.model_save_fn(A_ , params=state.params )
with open(os.path.join(A_ , '''opt_state.msgpack''' ) , '''wb''' ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(A_ , '''args.joblib''' ) )
joblib.dump(self.data_collator , os.path.join(A_ , '''data_collator.joblib''' ) )
with open(os.path.join(A_ , '''training_state.json''' ) , '''w''' ) as f:
json.dump({'''step''': state.step.item()} , A_ )
print('''DONE''' )
def _lowercase ( lowercase__ , lowercase__ ):
print(f"""RESTORING CHECKPOINT FROM {save_dir}""" , end=''' ... ''' )
with open(os.path.join(lowercase__ , '''flax_model.msgpack''' ) , '''rb''' ) as f:
__lowerCAmelCase : str = from_bytes(state.params , f.read() )
with open(os.path.join(lowercase__ , '''opt_state.msgpack''' ) , '''rb''' ) as f:
__lowerCAmelCase : Optional[Any] = from_bytes(state.opt_state , f.read() )
__lowerCAmelCase : Any = joblib.load(os.path.join(lowercase__ , '''args.joblib''' ) )
__lowerCAmelCase : List[Any] = joblib.load(os.path.join(lowercase__ , '''data_collator.joblib''' ) )
with open(os.path.join(lowercase__ , '''training_state.json''' ) , '''r''' ) as f:
__lowerCAmelCase : int = json.load(lowercase__ )
__lowerCAmelCase : List[Any] = training_state['''step''']
print('''DONE''' )
return params, opt_state, step, args, data_collator
def _lowercase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
__lowerCAmelCase : Optional[int] = num_train_steps - warmup_steps
__lowerCAmelCase : List[Any] = optax.linear_schedule(init_value=lowercase__ , end_value=lowercase__ , transition_steps=lowercase__ )
__lowerCAmelCase : int = optax.linear_schedule(init_value=lowercase__ , end_value=1E-7 , transition_steps=lowercase__ )
__lowerCAmelCase : str = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def _lowercase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
def weight_decay_mask(lowercase__ ):
__lowerCAmelCase : Optional[Any] = traverse_util.flatten_dict(lowercase__ )
__lowerCAmelCase : str = {k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
return traverse_util.unflatten_dict(lowercase__ )
__lowerCAmelCase : Optional[int] = scheduler_fn(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
__lowerCAmelCase : List[str] = optax.adamw(learning_rate=lowercase__ , weight_decay=lowercase__ , mask=lowercase__ )
return tx, lr
| 275
|
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def _lowercase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
if isinstance(lowercase__ , lowercase__ ):
__lowerCAmelCase : Dict = np.full((len(lowercase__ ), sequence_length, 2) , lowercase__ )
else:
__lowerCAmelCase : Optional[int] = np.full((len(lowercase__ ), sequence_length) , lowercase__ )
for i, tensor in enumerate(lowercase__ ):
if padding_side == "right":
if isinstance(lowercase__ , lowercase__ ):
__lowerCAmelCase : Union[str, Any] = tensor[:sequence_length]
else:
__lowerCAmelCase : int = tensor[:sequence_length]
else:
if isinstance(lowercase__ , lowercase__ ):
__lowerCAmelCase : Union[str, Any] = tensor[:sequence_length]
else:
__lowerCAmelCase : Optional[Any] = tensor[:sequence_length]
return out_tensor.tolist()
def _lowercase ( lowercase__ ):
__lowerCAmelCase : Union[str, Any] = ord(lowercase__ )
if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
return True
__lowerCAmelCase : int = unicodedata.category(lowercase__ )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class __lowercase (_UpperCAmelCase ):
_UpperCamelCase = 42
_UpperCamelCase = True
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = -100
_UpperCamelCase = "pt"
def UpperCamelCase__ ( self , A_ ) ->Optional[int]:
'''simple docstring'''
import torch
__lowerCAmelCase : List[str] = '''label''' if '''label''' in features[0].keys() else '''labels'''
__lowerCAmelCase : Union[str, Any] = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__lowerCAmelCase : List[Any] = self.tokenizer.pad(
A_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
__lowerCAmelCase : Dict = torch.tensor(batch['''entity_ids'''] ).shape[1]
__lowerCAmelCase : Optional[int] = self.tokenizer.padding_side
if padding_side == "right":
__lowerCAmelCase : Any = [
list(A_ ) + [self.label_pad_token_id] * (sequence_length - len(A_ )) for label in labels
]
else:
__lowerCAmelCase : Optional[int] = [
[self.label_pad_token_id] * (sequence_length - len(A_ )) + list(A_ ) for label in labels
]
__lowerCAmelCase : Tuple = [feature['''ner_tags'''] for feature in features]
__lowerCAmelCase : List[Any] = padding_tensor(A_ , -1 , A_ , A_ )
__lowerCAmelCase : Optional[int] = [feature['''original_entity_spans'''] for feature in features]
__lowerCAmelCase : Any = padding_tensor(A_ , (-1, -1) , A_ , A_ )
__lowerCAmelCase : Optional[Any] = {k: torch.tensor(A_ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 275
| 1
|
"""simple docstring"""
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase ( __lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase : int = TransfoXLTokenizer
UpperCAmelCase : Any = False
UpperCAmelCase : int = False
def _lowercase (self : str) -> Dict:
super().setUp()
__snake_case : List[Any] = [
'<unk>',
'[CLS]',
'[SEP]',
'want',
'unwanted',
'wa',
'un',
'running',
',',
'low',
'l',
]
__snake_case : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'])
with open(self.vocab_file , 'w' , encoding='utf-8') as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
def _lowercase (self : Any , **_A : Optional[int]) -> int:
__snake_case : Tuple = True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase_)
def _lowercase (self : Optional[Any] , _A : str) -> List[Any]:
__snake_case : List[str] = '<unk> UNwanted , running'
__snake_case : List[Any] = '<unk> unwanted, running'
return input_text, output_text
def _lowercase (self : str) -> Optional[Any]:
__snake_case : Optional[Any] = TransfoXLTokenizer(vocab_file=self.vocab_file , lower_case=lowerCAmelCase_)
__snake_case : int = tokenizer.tokenize('<unk> UNwanted , running')
self.assertListEqual(lowerCAmelCase_ , ['<unk>', 'unwanted', ',', 'running'])
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_) , [0, 4, 8, 7])
def _lowercase (self : Optional[int]) -> List[str]:
__snake_case : Union[str, Any] = TransfoXLTokenizer(lower_case=lowerCAmelCase_)
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo ! how \n Are yoU ? ') , ['hello', '!', 'how', 'are', 'you', '?'])
def _lowercase (self : Union[str, Any]) -> str:
__snake_case : List[str] = TransfoXLTokenizer(lower_case=lowerCAmelCase_)
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo ! how \n Are yoU ? ') , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'])
def _lowercase (self : List[str]) -> Dict:
__snake_case : Optional[Any] = TransfoXLTokenizer(lower_case=lowerCAmelCase_)
__snake_case : Any = 'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
__snake_case : str = [
'Hello',
'(',
'bracket',
')',
'and',
'side',
'@-@',
'scrolled',
'[',
'and',
']',
'Henry',
'\'s',
'$',
'5',
'@,@',
'000',
'with',
'3',
'@.@',
'34',
'm',
'.',
'What',
'\'s',
'up',
'!',
'?',
]
self.assertListEqual(tokenizer.tokenize(lowerCAmelCase_) , lowerCAmelCase_)
self.assertEqual(tokenizer.convert_tokens_to_string(lowerCAmelCase_) , lowerCAmelCase_)
def _lowercase (self : str) -> str:
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : List[Any] = len(lowerCAmelCase_)
tokenizer.add_tokens(['new1', 'new2'])
tokenizer.move_added_token('new1' , 1)
# Check that moved token is not copied (duplicate)
self.assertEqual(len(lowerCAmelCase_) , original_len + 2)
# Check that token is moved to specified id
self.assertEqual(tokenizer.encode('new1') , [1])
self.assertEqual(tokenizer.decode([1]) , 'new1')
| 365
|
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class UpperCamelCase ( unittest.TestCase ):
def _lowercase (self : Union[str, Any]) -> Optional[int]:
__snake_case : Optional[Any] = 0
def _lowercase (self : Tuple) -> int:
__snake_case : Optional[Any] = AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
self.assertIsInstance(_A , _A)
def _lowercase (self : str) -> List[str]:
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : List[str] = Path(_A) / 'preprocessor_config.json'
__snake_case : Optional[Any] = Path(_A) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
json.dump({'model_type': 'clip'} , open(_A , 'w'))
__snake_case : Optional[int] = AutoImageProcessor.from_pretrained(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : Any) -> Optional[int]:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : Any = Path(_A) / 'preprocessor_config.json'
__snake_case : List[Any] = Path(_A) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
json.dump({'model_type': 'clip'} , open(_A , 'w'))
__snake_case : Tuple = AutoImageProcessor.from_pretrained(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : List[Any]) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__snake_case : List[Any] = Path(_A) / 'preprocessor_config.json'
__snake_case : Optional[Any] = Path(_A) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
json.dump({'model_type': 'clip'} , open(_A , 'w'))
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__snake_case : List[str] = AutoImageProcessor.from_pretrained(_A).to_dict()
config_dict.pop('image_processor_type')
__snake_case : Optional[int] = CLIPImageProcessor(**_A)
# save in new folder
model_config.save_pretrained(_A)
config.save_pretrained(_A)
__snake_case : Optional[int] = AutoImageProcessor.from_pretrained(_A)
# make sure private variable is not incorrectly saved
__snake_case : int = json.loads(config.to_json_string())
self.assertTrue('_processor_class' not in dict_as_saved)
self.assertIsInstance(_A , _A)
def _lowercase (self : Union[str, Any]) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : int = Path(_A) / 'preprocessor_config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
__snake_case : List[str] = AutoImageProcessor.from_pretrained(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : Optional[int]) -> Dict:
with self.assertRaisesRegex(
_A , 'clip-base is not a local folder and is not a valid model identifier'):
__snake_case : Tuple = AutoImageProcessor.from_pretrained('clip-base')
def _lowercase (self : str) -> int:
with self.assertRaisesRegex(
_A , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
__snake_case : str = AutoImageProcessor.from_pretrained(_A , revision='aaaaaa')
def _lowercase (self : List[Any]) -> str:
with self.assertRaisesRegex(
_A , 'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.' , ):
__snake_case : List[Any] = AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model')
def _lowercase (self : Optional[int]) -> List[str]:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_A):
__snake_case : Any = AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
# If remote code is disabled, we can't load this config.
with self.assertRaises(_A):
__snake_case : Tuple = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_A)
__snake_case : Union[str, Any] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_A)
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor')
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_A)
__snake_case : Optional[int] = AutoImageProcessor.from_pretrained(_A , trust_remote_code=_A)
self.assertEqual(reloaded_image_processor.__class__.__name__ , 'NewImageProcessor')
def _lowercase (self : int) -> Optional[int]:
try:
AutoConfig.register('custom' , _A)
AutoImageProcessor.register(_A , _A)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_A):
AutoImageProcessor.register(_A , _A)
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : Tuple = Path(_A) / 'preprocessor_config.json'
__snake_case : Dict = Path(_A) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
json.dump({'model_type': 'clip'} , open(_A , 'w'))
__snake_case : Tuple = CustomImageProcessor.from_pretrained(_A)
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_A)
__snake_case : Tuple = AutoImageProcessor.from_pretrained(_A)
self.assertIsInstance(_A , _A)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _lowercase (self : List[Any]) -> Tuple:
class UpperCamelCase ( lowercase ):
UpperCAmelCase : str = True
try:
AutoConfig.register('custom' , _A)
AutoImageProcessor.register(_A , _A)
# If remote code is not set, the default is to use local
__snake_case : Tuple = AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor')
self.assertTrue(image_processor.is_local)
# If remote code is disabled, we load the local one.
__snake_case : Optional[int] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_A)
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor')
self.assertTrue(image_processor.is_local)
# If remote is enabled, we load from the Hub
__snake_case : List[Any] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_A)
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor')
self.assertTrue(not hasattr(_A , 'is_local'))
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 95
| 0
|
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