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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). ''' , __magic_name__ , )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = RobertaConfig
snake_case_ = '''roberta'''
def __init__( self , lowerCamelCase__ ) -> str:
'''simple docstring'''
super().__init__(lowerCamelCase__ )
__lowerCamelCase = RobertaEmbeddings(lowerCamelCase__ )
self.init_weights()
@add_start_docstrings(
'''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
also takes care of multi-layer training. ''' , __magic_name__ , )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = RobertaConfig
snake_case_ = '''roberta'''
def __init__( self , lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
super().__init__(lowerCamelCase__ )
__lowerCamelCase = config.num_labels
__lowerCamelCase = config.num_hidden_layers
__lowerCamelCase = DeeRobertaModel(lowerCamelCase__ )
__lowerCamelCase = nn.Dropout(config.hidden_dropout_prob )
__lowerCamelCase = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=-1 , lowerCamelCase__=False , ) -> str:
'''simple docstring'''
__lowerCamelCase = self.num_layers
try:
__lowerCamelCase = self.roberta(
lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , position_ids=lowerCamelCase__ , head_mask=lowerCamelCase__ , inputs_embeds=lowerCamelCase__ , )
__lowerCamelCase = outputs[1]
__lowerCamelCase = self.dropout(lowerCamelCase__ )
__lowerCamelCase = self.classifier(lowerCamelCase__ )
__lowerCamelCase = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
__lowerCamelCase = e.message
__lowerCamelCase = e.exit_layer
__lowerCamelCase = outputs[0]
if not self.training:
__lowerCamelCase = entropy(lowerCamelCase__ )
__lowerCamelCase = []
__lowerCamelCase = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
__lowerCamelCase = MSELoss()
__lowerCamelCase = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
__lowerCamelCase = CrossEntropyLoss()
__lowerCamelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
__lowerCamelCase = []
for highway_exit in outputs[-1]:
__lowerCamelCase = highway_exit[0]
if not self.training:
highway_logits_all.append(lowerCamelCase__ )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
__lowerCamelCase = MSELoss()
__lowerCamelCase = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
__lowerCamelCase = CrossEntropyLoss()
__lowerCamelCase = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(lowerCamelCase__ )
if train_highway:
__lowerCamelCase = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
__lowerCamelCase = (loss,) + outputs
if not self.training:
__lowerCamelCase = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
__lowerCamelCase = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 90
|
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : List[str] = inspect.getfile(accelerate.test_utils )
UpperCAmelCase : Tuple = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] )
UpperCAmelCase : Optional[int] = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["scripts", "test_distributed_data_loop.py"] )
UpperCAmelCase : Tuple = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_ops.py"] )
@require_multi_gpu
def A_ ( self ):
'''simple docstring'''
print(f"Found {torch.cuda.device_count()} devices." )
UpperCAmelCase : Any = ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
@require_multi_gpu
def A_ ( self ):
'''simple docstring'''
print(f"Found {torch.cuda.device_count()} devices." )
UpperCAmelCase : Tuple = ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
print(f"Command: {cmd}" )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
@require_multi_gpu
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
@require_multi_gpu
def A_ ( self ):
'''simple docstring'''
print(f"Found {torch.cuda.device_count()} devices, using 2 devices only" )
UpperCAmelCase : str = ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices="0,1" ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
if __name__ == "__main__":
a : Union[str, Any] = Accelerator()
a : str = (accelerator.state.process_index + 2, 10)
a : List[str] = torch.randint(0, 10, shape).to(accelerator.device)
a : Optional[int] = ""
a : int = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
a : List[Any] = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
a : List[str] = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 311
| 0
|
'''simple docstring'''
import torch
def _lowerCAmelCase ( ) -> Tuple:
if torch.cuda.is_available():
__lowerCAmelCase = torch.cuda.device_count()
else:
__lowerCAmelCase = 0
print(f'Successfully ran on {num_gpus} GPUs' )
if __name__ == "__main__":
main()
| 46
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : Dict = logging.get_logger(__name__)
_a : Optional[int] = """▁"""
_a : Any = {"""vocab_file""": """sentencepiece.bpe.model"""}
_a : List[Any] = {
"""vocab_file""": {
"""xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
"""xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
"""xlm-roberta-large-finetuned-conll02-dutch""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
),
"""xlm-roberta-large-finetuned-conll02-spanish""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
),
"""xlm-roberta-large-finetuned-conll03-english""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
),
"""xlm-roberta-large-finetuned-conll03-german""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
),
}
}
_a : Tuple = {
"""xlm-roberta-base""": 5_1_2,
"""xlm-roberta-large""": 5_1_2,
"""xlm-roberta-large-finetuned-conll02-dutch""": 5_1_2,
"""xlm-roberta-large-finetuned-conll02-spanish""": 5_1_2,
"""xlm-roberta-large-finetuned-conll03-english""": 5_1_2,
"""xlm-roberta-large-finetuned-conll03-german""": 5_1_2,
}
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : List[Any] =VOCAB_FILES_NAMES
a : str =PRETRAINED_VOCAB_FILES_MAP
a : Any =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : Optional[Any] =["""input_ids""", """attention_mask"""]
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE="<s>",__SCREAMING_SNAKE_CASE="</s>",__SCREAMING_SNAKE_CASE="</s>",__SCREAMING_SNAKE_CASE="<s>",__SCREAMING_SNAKE_CASE="<unk>",__SCREAMING_SNAKE_CASE="<pad>",__SCREAMING_SNAKE_CASE="<mask>",__SCREAMING_SNAKE_CASE = None,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = AddedToken(__SCREAMING_SNAKE_CASE,lstrip=__SCREAMING_SNAKE_CASE,rstrip=__SCREAMING_SNAKE_CASE ) if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) else mask_token
__lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__SCREAMING_SNAKE_CASE,eos_token=__SCREAMING_SNAKE_CASE,unk_token=__SCREAMING_SNAKE_CASE,sep_token=__SCREAMING_SNAKE_CASE,cls_token=__SCREAMING_SNAKE_CASE,pad_token=__SCREAMING_SNAKE_CASE,mask_token=__SCREAMING_SNAKE_CASE,sp_model_kwargs=self.sp_model_kwargs,**__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
__lowerCAmelCase = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
__lowerCAmelCase = 1
__lowerCAmelCase = len(self.sp_model ) + self.fairseq_offset
__lowerCAmelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
'''simple docstring'''
__lowerCAmelCase = self.__dict__.copy()
__lowerCAmelCase = None
__lowerCAmelCase = self.sp_model.serialized_model_proto()
return state
def __setstate__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = d
# for backward compatibility
if not hasattr(self,"""sp_model_kwargs""" ):
__lowerCAmelCase = {}
__lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowerCAmelCase = [self.cls_token_id]
__lowerCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__SCREAMING_SNAKE_CASE,token_ids_a=__SCREAMING_SNAKE_CASE,already_has_special_tokens=__SCREAMING_SNAKE_CASE )
if token_ids_a is None:
return [1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1]
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 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 + sep + token_ids_a + sep ) * [0]
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.sp_model.encode(__SCREAMING_SNAKE_CASE,out_type=__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__lowerCAmelCase = self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = """""".join(__SCREAMING_SNAKE_CASE ).replace(__SCREAMING_SNAKE_CASE,""" """ ).strip()
return out_string
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
__lowerCAmelCase = os.path.join(
__SCREAMING_SNAKE_CASE,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file,__SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(__SCREAMING_SNAKE_CASE,"""wb""" ) as fi:
__lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 46
| 1
|
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class UpperCAmelCase_ :
def __init__( self : Optional[int] ) -> None:
_UpperCamelCase = []
_UpperCamelCase = 0
_UpperCamelCase = 0
def _UpperCamelCase ( self : List[Any] ) -> bool:
return self.head == self.tail
def _UpperCamelCase ( self : List[Any] , __UpperCamelCase : Any ) -> None:
self.data.append(__UpperCamelCase )
_UpperCamelCase = self.tail + 1
def _UpperCamelCase ( self : Any ) -> Any:
_UpperCamelCase = self.data[self.head]
_UpperCamelCase = self.head + 1
return ret
def _UpperCamelCase ( self : Optional[int] ) -> int:
return self.tail - self.head
def _UpperCamelCase ( self : List[Any] ) -> None:
print(self.data )
print('''**************''' )
print(self.data[self.head : self.tail] )
class UpperCAmelCase_ :
def __init__( self : int , __UpperCamelCase : Any ) -> None:
_UpperCamelCase = data
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = 1
def _UpperCamelCase ( self : Dict ) -> Any:
return self.data
def _UpperCamelCase ( self : Dict ) -> MyNode | None:
return self.left
def _UpperCamelCase ( self : Union[str, Any] ) -> MyNode | None:
return self.right
def _UpperCamelCase ( self : int ) -> int:
return self.height
def _UpperCamelCase ( self : Optional[Any] , __UpperCamelCase : Any ) -> None:
_UpperCamelCase = data
def _UpperCamelCase ( self : Any , __UpperCamelCase : MyNode | None ) -> None:
_UpperCamelCase = node
def _UpperCamelCase ( self : str , __UpperCamelCase : MyNode | None ) -> None:
_UpperCamelCase = node
def _UpperCamelCase ( self : Optional[Any] , __UpperCamelCase : int ) -> None:
_UpperCamelCase = height
def lowercase ( a__ : MyNode | None ) -> int:
if node is None:
return 0
return node.get_height()
def lowercase ( a__ : int , a__ : int ) -> int:
if a > b:
return a
return b
def lowercase ( a__ : MyNode ) -> MyNode:
print('''left rotation node:''' , node.get_data() )
_UpperCamelCase = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(a__ )
_UpperCamelCase = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(a__ )
_UpperCamelCase = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(a__ )
return ret
def lowercase ( a__ : MyNode ) -> MyNode:
print('''right rotation node:''' , node.get_data() )
_UpperCamelCase = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(a__ )
_UpperCamelCase = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(a__ )
_UpperCamelCase = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(a__ )
return ret
def lowercase ( a__ : MyNode ) -> MyNode:
_UpperCamelCase = node.get_left()
assert left_child is not None
node.set_left(left_rotation(a__ ) )
return right_rotation(a__ )
def lowercase ( a__ : MyNode ) -> MyNode:
_UpperCamelCase = node.get_right()
assert right_child is not None
node.set_right(right_rotation(a__ ) )
return left_rotation(a__ )
def lowercase ( a__ : MyNode | None , a__ : Any ) -> MyNode | None:
if node is None:
return MyNode(a__ )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , a__ ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
_UpperCamelCase = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
_UpperCamelCase = right_rotation(a__ )
else:
_UpperCamelCase = lr_rotation(a__ )
else:
node.set_right(insert_node(node.get_right() , a__ ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
_UpperCamelCase = node.get_right()
assert right_child is not None
if data < right_child.get_data():
_UpperCamelCase = rl_rotation(a__ )
else:
_UpperCamelCase = left_rotation(a__ )
_UpperCamelCase = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(a__ )
return node
def lowercase ( a__ : MyNode ) -> Any:
while True:
_UpperCamelCase = root.get_right()
if right_child is None:
break
_UpperCamelCase = right_child
return root.get_data()
def lowercase ( a__ : MyNode ) -> Any:
while True:
_UpperCamelCase = root.get_left()
if left_child is None:
break
_UpperCamelCase = left_child
return root.get_data()
def lowercase ( a__ : MyNode , a__ : Any ) -> MyNode | None:
_UpperCamelCase = root.get_left()
_UpperCamelCase = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
_UpperCamelCase = get_left_most(a__ )
root.set_data(a__ )
root.set_right(del_node(a__ , a__ ) )
elif left_child is not None:
_UpperCamelCase = left_child
elif right_child is not None:
_UpperCamelCase = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print('''No such data''' )
return root
else:
root.set_left(del_node(a__ , a__ ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(a__ , a__ ) )
if get_height(a__ ) - get_height(a__ ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
_UpperCamelCase = left_rotation(a__ )
else:
_UpperCamelCase = rl_rotation(a__ )
elif get_height(a__ ) - get_height(a__ ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
_UpperCamelCase = right_rotation(a__ )
else:
_UpperCamelCase = lr_rotation(a__ )
_UpperCamelCase = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(a__ )
return root
class UpperCAmelCase_ :
def __init__( self : Union[str, Any] ) -> None:
_UpperCamelCase = None
def _UpperCamelCase ( self : int ) -> int:
return get_height(self.root )
def _UpperCamelCase ( self : Optional[Any] , __UpperCamelCase : Any ) -> None:
print('''insert:''' + str(__UpperCamelCase ) )
_UpperCamelCase = insert_node(self.root , __UpperCamelCase )
def _UpperCamelCase ( self : Optional[Any] , __UpperCamelCase : Any ) -> None:
print('''delete:''' + str(__UpperCamelCase ) )
if self.root is None:
print('''Tree is empty!''' )
return
_UpperCamelCase = del_node(self.root , __UpperCamelCase )
def __str__( self : Any , ) -> str: # a level traversale, gives a more intuitive look on the tree
_UpperCamelCase = ''''''
_UpperCamelCase = MyQueue()
q.push(self.root )
_UpperCamelCase = self.get_height()
if layer == 0:
return output
_UpperCamelCase = 0
while not q.is_empty():
_UpperCamelCase = q.pop()
_UpperCamelCase = ''' ''' * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(__UpperCamelCase )
q.push(__UpperCamelCase )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
_UpperCamelCase = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 , __UpperCamelCase ) - 1:
_UpperCamelCase = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def lowercase ( ) -> None:
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
UpperCAmelCase = AVLtree()
UpperCAmelCase = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 256
|
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def lowercase ( a__ : Dict , a__ : Dict , a__ : List[str] , a__ : int , a__ : Any ) -> Optional[Any]:
for attribute in key.split('''.''' ):
_UpperCamelCase = getattr(a__ , a__ )
if weight_type is not None:
_UpperCamelCase = getattr(a__ , a__ ).shape
else:
_UpperCamelCase = 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":
_UpperCamelCase = value
elif weight_type == "weight_g":
_UpperCamelCase = value
elif weight_type == "weight_v":
_UpperCamelCase = value
elif weight_type == "bias":
_UpperCamelCase = value
else:
_UpperCamelCase = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def lowercase ( a__ : str , a__ : Any , a__ : List[Any] ) -> List[Any]:
_UpperCamelCase = []
_UpperCamelCase = fairseq_model.state_dict()
_UpperCamelCase = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
_UpperCamelCase = False
if "conv_layers" in name:
load_conv_layer(
a__ , a__ , a__ , a__ , hf_model.config.feat_extract_norm == '''group''' , )
_UpperCamelCase = True
else:
for key, mapped_key in MAPPING.items():
_UpperCamelCase = '''sew.''' + 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]:
_UpperCamelCase = True
if "*" in mapped_key:
_UpperCamelCase = name.split(a__ )[0].split('''.''' )[-2]
_UpperCamelCase = mapped_key.replace('''*''' , a__ )
if "weight_g" in name:
_UpperCamelCase = '''weight_g'''
elif "weight_v" in name:
_UpperCamelCase = '''weight_v'''
elif "weight" in name:
_UpperCamelCase = '''weight'''
elif "bias" in name:
_UpperCamelCase = '''bias'''
else:
_UpperCamelCase = None
set_recursively(a__ , a__ , a__ , a__ , a__ )
continue
if not is_used:
unused_weights.append(a__ )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowercase ( a__ : str , a__ : int , a__ : Optional[int] , a__ : Optional[Any] , a__ : int ) -> Any:
_UpperCamelCase = full_name.split('''conv_layers.''' )[-1]
_UpperCamelCase = name.split('''.''' )
_UpperCamelCase = int(items[0] )
_UpperCamelCase = 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.'''
)
_UpperCamelCase = 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.'''
)
_UpperCamelCase = 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."
)
_UpperCamelCase = 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.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(a__ )
def lowercase ( a__ : List[str] , a__ : Optional[int] ) -> Union[str, Any]:
_UpperCamelCase = SEWConfig()
if is_finetuned:
_UpperCamelCase = model.wav_encoder.wav_model.cfg
else:
_UpperCamelCase = model.cfg
_UpperCamelCase = fs_config.conv_bias
_UpperCamelCase = eval(fs_config.conv_feature_layers )
_UpperCamelCase = [x[0] for x in conv_layers]
_UpperCamelCase = [x[1] for x in conv_layers]
_UpperCamelCase = [x[2] for x in conv_layers]
_UpperCamelCase = '''gelu'''
_UpperCamelCase = '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
_UpperCamelCase = 0.0
_UpperCamelCase = fs_config.activation_fn.name
_UpperCamelCase = fs_config.encoder_embed_dim
_UpperCamelCase = 0.02
_UpperCamelCase = fs_config.encoder_ffn_embed_dim
_UpperCamelCase = 1e-5
_UpperCamelCase = fs_config.encoder_layerdrop
_UpperCamelCase = fs_config.encoder_attention_heads
_UpperCamelCase = fs_config.conv_pos_groups
_UpperCamelCase = fs_config.conv_pos
_UpperCamelCase = len(a__ )
_UpperCamelCase = fs_config.encoder_layers
_UpperCamelCase = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
_UpperCamelCase = model.cfg
_UpperCamelCase = fs_config.final_dropout
_UpperCamelCase = fs_config.layerdrop
_UpperCamelCase = fs_config.activation_dropout
_UpperCamelCase = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
_UpperCamelCase = fs_config.attention_dropout
_UpperCamelCase = fs_config.dropout_input
_UpperCamelCase = fs_config.dropout
_UpperCamelCase = fs_config.mask_channel_length
_UpperCamelCase = fs_config.mask_channel_prob
_UpperCamelCase = fs_config.mask_length
_UpperCamelCase = fs_config.mask_prob
_UpperCamelCase = '''Wav2Vec2FeatureExtractor'''
_UpperCamelCase = '''Wav2Vec2CTCTokenizer'''
return config
@torch.no_grad()
def lowercase ( a__ : List[Any] , a__ : Optional[Any] , a__ : str=None , a__ : Tuple=None , a__ : Tuple=True ) -> Union[str, Any]:
if is_finetuned:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
_UpperCamelCase = SEWConfig.from_pretrained(a__ )
else:
_UpperCamelCase = convert_config(model[0] , a__ )
_UpperCamelCase = model[0].eval()
_UpperCamelCase = True if config.feat_extract_norm == '''layer''' else False
_UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=a__ , return_attention_mask=a__ , )
if is_finetuned:
if dict_path:
_UpperCamelCase = Dictionary.load(a__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_UpperCamelCase = target_dict.pad_index
_UpperCamelCase = target_dict.bos_index
_UpperCamelCase = target_dict.pad_index
_UpperCamelCase = target_dict.bos_index
_UpperCamelCase = target_dict.eos_index
_UpperCamelCase = len(target_dict.symbols )
_UpperCamelCase = os.path.join(a__ , '''vocab.json''' )
if not os.path.isdir(a__ ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(a__ ) )
return
os.makedirs(a__ , exist_ok=a__ )
with open(a__ , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices , a__ )
_UpperCamelCase = WavaVecaCTCTokenizer(
a__ , 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=a__ , )
_UpperCamelCase = WavaVecaProcessor(feature_extractor=a__ , tokenizer=a__ )
processor.save_pretrained(a__ )
_UpperCamelCase = SEWForCTC(a__ )
else:
_UpperCamelCase = SEWModel(a__ )
feature_extractor.save_pretrained(a__ )
recursively_load_weights(a__ , a__ , a__ )
hf_model.save_pretrained(a__ )
if __name__ == "__main__":
UpperCAmelCase = 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(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCAmelCase = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 256
| 1
|
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
UpperCAmelCase = [
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'
' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'
' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.',
'The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'
' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'
' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'
' body.',
'Amnesty International releases its annual report on the death penalty. The report catalogs the use of'
' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'
' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'
' punishment.',
]
UpperCAmelCase = [
'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'
' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'
' had informed his Lufthansa training school of an episode of severe depression, airline says .',
'Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'
' Israel and the United States opposed the move, which could open the door to war crimes investigations against'
' Israelis .',
'Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'
' death . Organization claims that governments around the world are using the threat of terrorism to advance'
' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'
' sentences up by 28% .',
]
def lowercase ( ) -> int:
_UpperCamelCase = calculate_rouge(a__ , a__ , bootstrap_aggregation=a__ , rouge_keys=['''rouge2''', '''rougeL'''] )
assert isinstance(a__ , a__ )
_UpperCamelCase = calculate_rouge(a__ , a__ , bootstrap_aggregation=a__ , rouge_keys=['''rouge2'''] )
assert (
pd.DataFrame(no_aggregation['''rouge2'''] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra['''rouge2'''] ).fmeasure.mean()
)
def lowercase ( ) -> List[str]:
_UpperCamelCase = '''rougeLsum'''
_UpperCamelCase = calculate_rouge(a__ , a__ , newline_sep=a__ , rouge_keys=[k] )[k]
_UpperCamelCase = calculate_rouge(a__ , a__ , newline_sep=a__ , rouge_keys=[k] )[k]
assert score > score_no_sep
def lowercase ( ) -> Optional[int]:
_UpperCamelCase = ['''rouge1''', '''rouge2''', '''rougeL''']
_UpperCamelCase = calculate_rouge(a__ , a__ , newline_sep=a__ , rouge_keys=a__ )
_UpperCamelCase = calculate_rouge(a__ , a__ , newline_sep=a__ , rouge_keys=a__ )
assert score_sep == score_no_sep
def lowercase ( ) -> Optional[int]:
_UpperCamelCase = [
'''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
]
_UpperCamelCase = [
'''Margot Frank, died in 1945, a month earlier than previously thought.''',
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
''' the final seconds on board Flight 9525.''',
]
assert calculate_rouge(a__ , a__ , newline_sep=a__ ) == calculate_rouge(a__ , a__ , newline_sep=a__ )
def lowercase ( ) -> int:
_UpperCamelCase = [
'''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
]
_UpperCamelCase = [
''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
]
_UpperCamelCase = calculate_rouge(a__ , a__ , rouge_keys=['''rougeLsum'''] , newline_sep=a__ )['''rougeLsum''']
_UpperCamelCase = calculate_rouge(a__ , a__ , rouge_keys=['''rougeLsum'''] )['''rougeLsum''']
assert new_score > prev_score
def lowercase ( ) -> Any:
_UpperCamelCase = Path('''examples/seq2seq/test_data/wmt_en_ro''' )
_UpperCamelCase = calculate_rouge_path(data_dir.joinpath('''test.source''' ) , data_dir.joinpath('''test.target''' ) )
assert isinstance(a__ , a__ )
_UpperCamelCase = calculate_rouge_path(
data_dir.joinpath('''test.source''' ) , data_dir.joinpath('''test.target''' ) , bootstrap_aggregation=a__ )
assert isinstance(a__ , a__ )
| 361
|
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 54
| 0
|
"""simple docstring"""
import os
import numpy
import onnx
def a__ ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
lowerCAmelCase : str = a.name
lowerCAmelCase : int = b.name
lowerCAmelCase : List[Any] = ""
lowerCAmelCase : List[Any] = ""
lowerCAmelCase : List[str] = a == b
lowerCAmelCase : List[str] = name_a
lowerCAmelCase : Optional[int] = name_b
return res
def a__ ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
_graph_replace_input_with(node_proto.attribute[1].g , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def a__ ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
for n in graph_proto.node:
_node_replace_input_with(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def a__ ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
lowerCAmelCase : Tuple = list(model.graph.initializer )
lowerCAmelCase : Optional[int] = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowerCAmelCase : Dict = inits[i].name
lowerCAmelCase : Optional[int] = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def a__ ( SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = os.path.dirname(SCREAMING_SNAKE_CASE )
lowerCAmelCase : int = os.path.basename(SCREAMING_SNAKE_CASE )
lowerCAmelCase : Dict = onnx.load(os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
lowerCAmelCase : Union[str, Any] = list(model.graph.initializer )
lowerCAmelCase : Optional[Any] = set()
lowerCAmelCase : Any = {}
lowerCAmelCase : Optional[Any] = []
lowerCAmelCase : Any = 0
for i in range(len(SCREAMING_SNAKE_CASE ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(SCREAMING_SNAKE_CASE ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(SCREAMING_SNAKE_CASE )
dup_set.add(SCREAMING_SNAKE_CASE )
lowerCAmelCase : Dict = inits[j].data_type
lowerCAmelCase : Optional[int] = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 1_1:
mem_size *= 8
else:
print("unexpected data type: " , SCREAMING_SNAKE_CASE )
total_reduced_size += mem_size
lowerCAmelCase : Optional[int] = inits[i].name
lowerCAmelCase : List[str] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(SCREAMING_SNAKE_CASE )
else:
lowerCAmelCase : Tuple = [name_j]
ind_to_replace.append((j, i) )
print("total reduced size: " , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4 , "GB" )
lowerCAmelCase : Any = sorted(SCREAMING_SNAKE_CASE )
_remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowerCAmelCase : List[str] = "optimized_" + model_file_name
lowerCAmelCase : List[str] = os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
onnx.save(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return new_model
| 108
|
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
lowercase__ : Any = logging.get_logger(__name__)
class __lowerCAmelCase :
"""simple docstring"""
_snake_case : List[str] = None
@experimental
def a__ ( lowercase : Union[str, Any], lowercase : Optional[int], lowercase : Tuple, lowercase : List[Any], lowercase : Dict, lowercase : Union[str, Any], lowercase : Optional[Any] ) -> int:
"""simple docstring"""
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
lowercase, lowercase, lowercase, lowercase, lowercase, lowercase, lowercase )
return _map_with_joblib(lowercase, lowercase, lowercase, lowercase, lowercase, lowercase, lowercase )
def a__ ( lowercase : Dict, lowercase : str, lowercase : Union[str, Any], lowercase : Optional[Any], lowercase : Optional[int], lowercase : Optional[Any], lowercase : Optional[int] ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = num_proc if num_proc <= len(lowercase ) else len(lowercase )
_UpperCamelCase = [] # We organize the splits ourselve (contiguous splits)
for index in range(lowercase ):
_UpperCamelCase = len(lowercase ) // num_proc
_UpperCamelCase = len(lowercase ) % num_proc
_UpperCamelCase = div * index + min(lowercase, lowercase )
_UpperCamelCase = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(lowercase ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
F"""Error dividing inputs iterable among processes. """
F"""Total number of objects {len(lowercase )}, """
F"""length: {sum(len(i[1] ) for i in split_kwds )}""" )
logger.info(
F"""Spawning {num_proc} processes for {len(lowercase )} objects in slices of {[len(i[1] ) for i in split_kwds]}""" )
_UpperCamelCase , _UpperCamelCase = None, None
if not disable_tqdm:
_UpperCamelCase , _UpperCamelCase = (RLock(),), tqdm.set_lock
with Pool(lowercase, initargs=lowercase, initializer=lowercase ) as pool:
_UpperCamelCase = pool.map(lowercase, lowercase )
logger.info(F"""Finished {num_proc} processes""" )
_UpperCamelCase = [obj for proc_res in mapped for obj in proc_res]
logger.info(F"""Unpacked {len(lowercase )} objects""" )
return mapped
def a__ ( lowercase : str, lowercase : Tuple, lowercase : List[str], lowercase : List[str], lowercase : Any, lowercase : int, lowercase : Optional[Any] ) -> Any:
"""simple docstring"""
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name, n_jobs=lowercase ):
return joblib.Parallel()(
joblib.delayed(lowercase )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def a__ ( lowercase : str ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
_UpperCamelCase = None
| 324
| 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 tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self : Any ) -> str:
a_ : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' )
a_ : int = AutoTokenizer.from_pretrained('google/mt5-small' )
a_ : Dict = tokenizer('Hello there' , return_tensors='tf' ).input_ids
a_ : str = tokenizer('Hi I am' , return_tensors='tf' ).input_ids
a_ : int = model(SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ ).loss
a_ : List[str] = -tf.math.reduce_mean(SCREAMING_SNAKE_CASE__ ).numpy()
a_ : Optional[int] = -21.228168
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )
| 120
|
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__A , __A ):
return 0
elif n == 2:
return 1
else:
a_ : int = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> int:
"""simple docstring"""
a_ : Any = 0
a_ : Optional[Any] = 2
while digits < n:
index += 1
a_ : List[Any] = len(str(fibonacci(__A ) ) )
return index
def SCREAMING_SNAKE_CASE_ ( __A : int = 10_00 ) -> int:
"""simple docstring"""
return fibonacci_digits_index(__A )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 120
| 1
|
from string import ascii_uppercase
__snake_case :str = {str(ord(c) - 55): c for c in ascii_uppercase}
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('''int() can\'t convert non-string with explicit base''' )
if num < 0:
raise ValueError('''parameter must be positive int''' )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('''\'str\' object cannot be interpreted as an integer''' )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if base in (0, 1):
raise ValueError('''base must be >= 2''' )
if base > 36:
raise ValueError('''base must be <= 36''' )
__a = ''''''
__a = 0
__a = 0
while div != 1:
__a , __a = divmod(_UpperCAmelCase , _UpperCAmelCase )
if base >= 11 and 9 < mod < 36:
__a = ALPHABET_VALUES[str(_UpperCAmelCase )]
else:
__a = str(_UpperCAmelCase )
new_value += actual_value
__a = num // base
__a = div
if div == 0:
return str(new_value[::-1] )
elif div == 1:
new_value += str(_UpperCAmelCase )
return str(new_value[::-1] )
return new_value[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for base in range(2, 37):
for num in range(1000):
assert int(decimal_to_any(num, base), base) == num, (
num,
base,
decimal_to_any(num, base),
int(decimal_to_any(num, base), base),
)
| 49
|
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class A :
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase=1_3 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=9_9 , _UpperCAmelCase=3_2 , _UpperCAmelCase=2 , _UpperCAmelCase=4 , _UpperCAmelCase=3_7 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=5_1_2 , _UpperCAmelCase=1_6 , _UpperCAmelCase=2 , _UpperCAmelCase=0.02 , _UpperCAmelCase=3 , _UpperCAmelCase=4 , _UpperCAmelCase=None , ) -> Dict:
__UpperCamelCase : Optional[Any] = parent
__UpperCamelCase : List[str] = 1_3
__UpperCamelCase : List[Any] = 7
__UpperCamelCase : List[str] = True
__UpperCamelCase : Optional[Any] = True
__UpperCamelCase : Tuple = True
__UpperCamelCase : str = True
__UpperCamelCase : List[Any] = 9_9
__UpperCamelCase : Union[str, Any] = 3_8_4
__UpperCamelCase : str = 2
__UpperCamelCase : Optional[Any] = 4
__UpperCamelCase : Any = 3_7
__UpperCamelCase : str = "gelu"
__UpperCamelCase : Optional[Any] = 0.1
__UpperCamelCase : str = 0.1
__UpperCamelCase : str = 5_1_2
__UpperCamelCase : Optional[Any] = 1_6
__UpperCamelCase : Dict = 2
__UpperCamelCase : Optional[int] = 0.02
__UpperCamelCase : List[Any] = 3
__UpperCamelCase : Optional[Any] = 4
__UpperCamelCase : int = 1_2_8
__UpperCamelCase : Tuple = 2
__UpperCamelCase : str = 9
__UpperCamelCase : List[Any] = 1
__UpperCamelCase : Any = None
def a_ (self ) -> int:
__UpperCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase : str = None
if self.use_input_mask:
__UpperCamelCase : str = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase : int = None
if self.use_token_type_ids:
__UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCamelCase : List[Any] = None
__UpperCamelCase : Union[str, Any] = None
__UpperCamelCase : Optional[Any] = None
if self.use_labels:
__UpperCamelCase : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
__UpperCamelCase : str = ConvBertConfig(
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 , initializer_range=self.initializer_range , return_dict=_UpperCAmelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
__UpperCamelCase : Tuple = TFConvBertModel(config=_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
__UpperCamelCase : Optional[Any] = [input_ids, input_mask]
__UpperCamelCase : str = model(_UpperCAmelCase )
__UpperCamelCase : int = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]:
__UpperCamelCase : int = TFConvBertForMaskedLM(config=_UpperCAmelCase )
__UpperCamelCase : Dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
__UpperCamelCase : List[str] = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]:
__UpperCamelCase : Union[str, Any] = self.num_labels
__UpperCamelCase : Optional[Any] = TFConvBertForSequenceClassification(config=_UpperCAmelCase )
__UpperCamelCase : List[str] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
__UpperCamelCase : Optional[Any] = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
__UpperCamelCase : Optional[int] = self.num_choices
__UpperCamelCase : List[Any] = TFConvBertForMultipleChoice(config=_UpperCAmelCase )
__UpperCamelCase : Optional[int] = tf.tile(tf.expand_dims(_UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase : Optional[Any] = tf.tile(tf.expand_dims(_UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase : str = tf.tile(tf.expand_dims(_UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase : List[str] = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
__UpperCamelCase : int = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Any:
__UpperCamelCase : List[str] = self.num_labels
__UpperCamelCase : Tuple = TFConvBertForTokenClassification(config=_UpperCAmelCase )
__UpperCamelCase : Dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
__UpperCamelCase : Union[str, Any] = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Union[str, Any]:
__UpperCamelCase : int = TFConvBertForQuestionAnswering(config=_UpperCAmelCase )
__UpperCamelCase : Dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
__UpperCamelCase : Any = model(_UpperCAmelCase )
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 a_ (self ) -> str:
__UpperCamelCase : str = self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : Any = config_and_inputs
__UpperCamelCase : int = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class A ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
A = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
A = (
{
"feature-extraction": TFConvBertModel,
"fill-mask": TFConvBertForMaskedLM,
"question-answering": TFConvBertForQuestionAnswering,
"text-classification": TFConvBertForSequenceClassification,
"token-classification": TFConvBertForTokenClassification,
"zero-shot": TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
A = False
A = False
A = False
def a_ (self ) -> Optional[int]:
__UpperCamelCase : Tuple = TFConvBertModelTester(self )
__UpperCamelCase : Optional[Any] = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=3_7 )
def a_ (self ) -> Dict:
self.config_tester.run_common_tests()
def a_ (self ) -> Dict:
__UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def a_ (self ) -> Tuple:
__UpperCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )
def a_ (self ) -> Tuple:
__UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase )
def a_ (self ) -> Dict:
__UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase )
def a_ (self ) -> Dict:
__UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase )
def a_ (self ) -> Optional[int]:
__UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase )
@slow
def a_ (self ) -> Any:
__UpperCamelCase , __UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase : str = True
__UpperCamelCase : int = True
if hasattr(_UpperCAmelCase , "use_cache" ):
__UpperCamelCase : List[Any] = True
__UpperCamelCase : List[str] = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
__UpperCamelCase : Optional[Any] = getattr(self.model_tester , "key_length" , _UpperCAmelCase )
for model_class in self.all_model_classes:
__UpperCamelCase : Any = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase )
__UpperCamelCase : int = model_class(_UpperCAmelCase )
__UpperCamelCase : Any = len(model(_UpperCAmelCase ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_UpperCAmelCase , saved_model=_UpperCAmelCase )
__UpperCamelCase : List[str] = os.path.join(_UpperCAmelCase , "saved_model" , "1" )
__UpperCamelCase : List[str] = tf.keras.models.load_model(_UpperCAmelCase )
__UpperCamelCase : Dict = model(_UpperCAmelCase )
if self.is_encoder_decoder:
__UpperCamelCase : Any = outputs["encoder_hidden_states"]
__UpperCamelCase : Tuple = outputs["encoder_attentions"]
else:
__UpperCamelCase : Tuple = outputs["hidden_states"]
__UpperCamelCase : Optional[int] = outputs["attentions"]
self.assertEqual(len(_UpperCAmelCase ) , _UpperCAmelCase )
__UpperCamelCase : Any = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_UpperCAmelCase ) , _UpperCAmelCase )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(_UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def a_ (self ) -> Optional[Any]:
__UpperCamelCase : Tuple = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
self.assertIsNotNone(_UpperCAmelCase )
def a_ (self ) -> Tuple:
__UpperCamelCase , __UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase : str = True
__UpperCamelCase : Tuple = getattr(self.model_tester , "decoder_seq_length" , self.model_tester.seq_length )
__UpperCamelCase : Optional[int] = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
__UpperCamelCase : Any = getattr(self.model_tester , "key_length" , _UpperCAmelCase )
__UpperCamelCase : List[Any] = getattr(self.model_tester , "key_length" , _UpperCAmelCase )
def check_decoder_attentions_output(_UpperCAmelCase ):
__UpperCamelCase : Dict = len(_UpperCAmelCase )
self.assertEqual(out_len % 2 , 0 )
__UpperCamelCase : List[str] = outputs.decoder_attentions
self.assertEqual(len(_UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(_UpperCAmelCase ):
__UpperCamelCase : Any = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(_UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
__UpperCamelCase : Any = True
__UpperCamelCase : Dict = False
__UpperCamelCase : str = model_class(_UpperCAmelCase )
__UpperCamelCase : Tuple = model(self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
__UpperCamelCase : List[Any] = len(_UpperCAmelCase )
self.assertEqual(config.output_hidden_states , _UpperCAmelCase )
check_encoder_attentions_output(_UpperCAmelCase )
if self.is_encoder_decoder:
__UpperCamelCase : str = model_class(_UpperCAmelCase )
__UpperCamelCase : Dict = model(self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertEqual(config.output_hidden_states , _UpperCAmelCase )
check_decoder_attentions_output(_UpperCAmelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__UpperCamelCase : Optional[Any] = True
__UpperCamelCase : Tuple = model_class(_UpperCAmelCase )
__UpperCamelCase : int = model(self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertEqual(config.output_hidden_states , _UpperCAmelCase )
check_encoder_attentions_output(_UpperCAmelCase )
# Check attention is always last and order is fine
__UpperCamelCase : int = True
__UpperCamelCase : str = True
__UpperCamelCase : Optional[Any] = model_class(_UpperCAmelCase )
__UpperCamelCase : Optional[int] = model(self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_UpperCAmelCase ) )
self.assertEqual(model.config.output_hidden_states , _UpperCAmelCase )
check_encoder_attentions_output(_UpperCAmelCase )
@require_tf
class A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a_ (self ) -> str:
__UpperCamelCase : Dict = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
__UpperCamelCase : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
__UpperCamelCase : Optional[int] = model(_UpperCAmelCase )[0]
__UpperCamelCase : Tuple = [1, 6, 7_6_8]
self.assertEqual(output.shape , _UpperCAmelCase )
__UpperCamelCase : Any = tf.constant(
[
[
[-0.03_475_493, -0.4_686_034, -0.30_638_832],
[0.22_637_248, -0.26_988_646, -0.7_423_424],
[0.10_324_868, -0.45_013_508, -0.58_280_784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 )
| 298
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class _snake_case ( lowercase__):
UpperCamelCase__ : Optional[Any] ="""transfo-xl"""
UpperCamelCase__ : Dict =["""mems"""]
UpperCamelCase__ : Optional[int] ={
"""n_token""": """vocab_size""",
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Optional[Any], __lowercase : Optional[Any]=26_7735, __lowercase : int=[2_0000, 4_0000, 20_0000], __lowercase : Union[str, Any]=1024, __lowercase : Tuple=1024, __lowercase : Tuple=16, __lowercase : Optional[Any]=64, __lowercase : str=4096, __lowercase : Optional[int]=4, __lowercase : Union[str, Any]=False, __lowercase : Union[str, Any]=18, __lowercase : List[str]=1600, __lowercase : List[Any]=1000, __lowercase : Union[str, Any]=True, __lowercase : Tuple=True, __lowercase : Optional[Any]=0, __lowercase : List[str]=-1, __lowercase : int=True, __lowercase : Dict=0.1, __lowercase : Union[str, Any]=0.0, __lowercase : str=True, __lowercase : Optional[Any]="normal", __lowercase : str=0.01, __lowercase : Tuple=0.01, __lowercase : List[Any]=0.02, __lowercase : Any=1e-5, __lowercase : Union[str, Any]=0, **__lowercase : Union[str, Any], ):
lowercase__ = vocab_size
lowercase__ = []
self.cutoffs.extend(__lowercase )
if proj_share_all_but_first:
lowercase__ = [False] + [True] * len(self.cutoffs )
else:
lowercase__ = [False] + [False] * len(self.cutoffs )
lowercase__ = d_model
lowercase__ = d_embed
lowercase__ = d_head
lowercase__ = d_inner
lowercase__ = div_val
lowercase__ = pre_lnorm
lowercase__ = n_layer
lowercase__ = n_head
lowercase__ = mem_len
lowercase__ = same_length
lowercase__ = attn_type
lowercase__ = clamp_len
lowercase__ = sample_softmax
lowercase__ = adaptive
lowercase__ = dropout
lowercase__ = dropatt
lowercase__ = untie_r
lowercase__ = init
lowercase__ = init_range
lowercase__ = proj_init_std
lowercase__ = init_std
lowercase__ = layer_norm_epsilon
super().__init__(eos_token_id=__lowercase, **__lowercase )
@property
def A__ ( self : Optional[Any] ):
# Message copied from Transformer-XL documentation
logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
return -1
@max_position_embeddings.setter
def A__ ( self : List[str], __lowercase : Union[str, Any] ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
| 224
|
from collections import namedtuple
import requests
from lxml import html # type: ignore
lowercase_ = namedtuple("""covid_data""", """cases deaths recovered""")
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ = "https://www.worldometers.info/coronavirus/" ):
lowercase__ = "//div[@class = \"maincounter-number\"]/span/text()"
return covid_data(*html.fromstring(requests.get(SCREAMING_SNAKE_CASE_ ).content ).xpath(SCREAMING_SNAKE_CASE_ ) )
lowercase_ = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| 224
| 1
|
"""simple docstring"""
import random
from typing import Any
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
for _ in range(len(SCREAMING_SNAKE_CASE ) ):
lowerCAmelCase = random.randint(0 , len(SCREAMING_SNAKE_CASE ) - 1 )
lowerCAmelCase = random.randint(0 , len(SCREAMING_SNAKE_CASE ) - 1 )
lowerCAmelCase , lowerCAmelCase = data[b], data[a]
return data
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = [0, 1, 2, 3, 4, 5, 6, 7]
SCREAMING_SNAKE_CASE__ = ["python", "says", "hello", "!"]
print("Fisher-Yates Shuffle:")
print("List", integers, strings)
print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 46
|
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.distilbert.modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
class lowercase :
def __init__( self , lowercase , ) -> Optional[int]:
lowerCAmelCase = parent
lowerCAmelCase = 13
lowerCAmelCase = 7
lowerCAmelCase = True
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = True
lowerCAmelCase = 99
lowerCAmelCase = 32
lowerCAmelCase = 2
lowerCAmelCase = 4
lowerCAmelCase = 37
lowerCAmelCase = """gelu"""
lowerCAmelCase = 0.1
lowerCAmelCase = 0.1
lowerCAmelCase = 512
lowerCAmelCase = 16
lowerCAmelCase = 2
lowerCAmelCase = 0.02
lowerCAmelCase = 3
lowerCAmelCase = 4
lowerCAmelCase = None
def _snake_case ( self ) -> str:
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase = None
if self.use_input_mask:
lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = None
if self.use_labels:
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> List[Any]:
lowerCAmelCase = TFDistilBertModel(config=lowercase )
lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
lowerCAmelCase = model(lowercase )
lowerCAmelCase = [input_ids, input_mask]
lowerCAmelCase = model(lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
lowerCAmelCase = TFDistilBertForMaskedLM(config=lowercase )
lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
lowerCAmelCase = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> List[Any]:
lowerCAmelCase = TFDistilBertForQuestionAnswering(config=lowercase )
lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
}
lowerCAmelCase = model(lowercase )
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 _snake_case ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> List[Any]:
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFDistilBertForSequenceClassification(lowercase )
lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
lowerCAmelCase = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Any:
lowerCAmelCase = self.num_choices
lowerCAmelCase = TFDistilBertForMultipleChoice(lowercase )
lowerCAmelCase = tf.tile(tf.expand_dims(lowercase , 1 ) , (1, self.num_choices, 1) )
lowerCAmelCase = tf.tile(tf.expand_dims(lowercase , 1 ) , (1, self.num_choices, 1) )
lowerCAmelCase = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
}
lowerCAmelCase = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> int:
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFDistilBertForTokenClassification(lowercase )
lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
lowerCAmelCase = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _snake_case ( self ) -> Optional[Any]:
lowerCAmelCase = self.prepare_config_and_inputs()
((lowerCAmelCase) , (lowerCAmelCase) , (lowerCAmelCase) , (lowerCAmelCase) , (lowerCAmelCase) , (lowerCAmelCase)) = config_and_inputs
lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class lowercase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE = (
(
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForMultipleChoice,
)
if is_tf_available()
else None
)
_SCREAMING_SNAKE_CASE = (
{
'feature-extraction': TFDistilBertModel,
'fill-mask': TFDistilBertForMaskedLM,
'question-answering': TFDistilBertForQuestionAnswering,
'text-classification': TFDistilBertForSequenceClassification,
'token-classification': TFDistilBertForTokenClassification,
'zero-shot': TFDistilBertForSequenceClassification,
}
if is_tf_available()
else {}
)
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
def _snake_case ( self ) -> Dict:
lowerCAmelCase = TFDistilBertModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=lowercase , dim=37 )
def _snake_case ( self ) -> str:
self.config_tester.run_common_tests()
def _snake_case ( self ) -> int:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*lowercase )
def _snake_case ( self ) -> Any:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*lowercase )
def _snake_case ( self ) -> Optional[Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*lowercase )
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowercase )
def _snake_case ( self ) -> List[Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowercase )
def _snake_case ( self ) -> str:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*lowercase )
@slow
def _snake_case ( self ) -> List[str]:
for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1] ):
lowerCAmelCase = TFDistilBertModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@require_tf
class lowercase ( unittest.TestCase ):
@slow
def _snake_case ( self ) -> Any:
lowerCAmelCase = TFDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
lowerCAmelCase = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowerCAmelCase = model(lowercase )[0]
lowerCAmelCase = [1, 6, 768]
self.assertEqual(output.shape , lowercase )
lowerCAmelCase = tf.constant(
[
[
[0.19_261_885, -0.13_732_955, 0.4_119_799],
[0.22_150_156, -0.07_422_661, 0.39_037_204],
[0.22_756_018, -0.0_896_414, 0.3_701_467],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , lowercase , atol=1e-4 )
| 46
| 1
|
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase ( self : List[str] ) -> Dict:
__lowerCAmelCase = FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base' )
__lowerCAmelCase = AutoTokenizer.from_pretrained('xlm-roberta-base' )
__lowerCAmelCase = 'The dog is cute and lives in the garden house'
__lowerCAmelCase = jnp.array([tokenizer.encode(lowerCAmelCase_ )] )
__lowerCAmelCase = (1, 1_2, 7_6_8) # batch_size, sequence_length, embedding_vector_dim
__lowerCAmelCase = jnp.array(
[[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]] )
__lowerCAmelCase = model(lowerCAmelCase_ )['last_hidden_state']
self.assertEqual(output.shape , lowerCAmelCase_ )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , lowerCAmelCase_ , atol=1e-3 ) )
| 207
|
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : List[str] = logging.get_logger(__name__)
_snake_case : Dict = {
'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 _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
a_ = """encodec"""
def __init__( self : Optional[Any] , lowerCAmelCase_ : List[Any]=[1.5, 3.0, 6.0, 12.0, 24.0] , lowerCAmelCase_ : Optional[Any]=2_4_0_0_0 , lowerCAmelCase_ : int=1 , lowerCAmelCase_ : Union[str, Any]=False , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Optional[Any]=1_2_8 , lowerCAmelCase_ : List[Any]=3_2 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : Optional[int]=[8, 5, 4, 2] , lowerCAmelCase_ : Optional[Any]="weight_norm" , lowerCAmelCase_ : Tuple=7 , lowerCAmelCase_ : Optional[int]=7 , lowerCAmelCase_ : Dict=3 , lowerCAmelCase_ : Optional[Any]=2 , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Dict="reflect" , lowerCAmelCase_ : List[Any]=2 , lowerCAmelCase_ : int=2 , lowerCAmelCase_ : Dict=1.0 , lowerCAmelCase_ : str=1_0_2_4 , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : List[Any]=True , **lowerCAmelCase_ : Optional[Any] , ) -> Union[str, Any]:
__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 lowercase ( self : Union[str, Any] ) -> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def lowercase ( self : Optional[Any] ) -> Optional[int]:
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 lowercase ( self : Any ) -> int:
__lowerCAmelCase = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def lowercase ( self : str ) -> int:
return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0) )
| 207
| 1
|
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = """Alexander Joslin"""
import operator as op
from .stack import Stack
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> int:
"""simple docstring"""
_UpperCAmelCase : Tuple = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
_UpperCAmelCase : Stack[int] = Stack()
_UpperCAmelCase : Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(_UpperCAmelCase ) )
elif i in operators:
# RULE 2
operator_stack.push(_UpperCAmelCase )
elif i == ")":
# RULE 4
_UpperCAmelCase : str = operator_stack.peek()
operator_stack.pop()
_UpperCAmelCase : List[str] = operand_stack.peek()
operand_stack.pop()
_UpperCAmelCase : List[str] = operand_stack.peek()
operand_stack.pop()
_UpperCAmelCase : List[Any] = operators[opr](_UpperCAmelCase , _UpperCAmelCase )
operand_stack.push(_UpperCAmelCase )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : List[str] = """(5 + ((4 * 2) * (2 + 3)))"""
# answer = 45
print(F'{equation} = {dijkstras_two_stack_algorithm(equation)}')
| 31
|
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
a__ : Union[str, Any] = logging.get_logger(__name__)
def UpperCAmelCase__ ():
'''simple docstring'''
__SCREAMING_SNAKE_CASE = os.getenv("SM_HP_MP_PARAMETERS" , "{}" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
__SCREAMING_SNAKE_CASE = json.loads(lowerCAmelCase_ )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
__SCREAMING_SNAKE_CASE = os.getenv("SM_FRAMEWORK_PARAMS" , "{}" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
__SCREAMING_SNAKE_CASE = json.loads(lowerCAmelCase_ )
if not mpi_options.get("sagemaker_mpi_enabled" , lowerCAmelCase_ ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("smdistributed" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
snake_case__ : str = field(
default="" , metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"} , )
def UpperCAmelCase_ ( self : List[str] ) -> Any:
super().__post_init__()
warnings.warn(
"`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
"`TrainingArguments` instead." , UpperCAmelCase__ , )
@cached_property
def UpperCAmelCase_ ( self : List[str] ) -> "torch.device":
logger.info("PyTorch: setting up devices" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"torch.distributed process group is initialized, but local_rank == -1. "
"In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" )
if self.no_cuda:
__SCREAMING_SNAKE_CASE = torch.device("cpu" )
__SCREAMING_SNAKE_CASE = 0
elif is_sagemaker_model_parallel_available():
__SCREAMING_SNAKE_CASE = smp.local_rank()
__SCREAMING_SNAKE_CASE = torch.device("cuda" , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="smddp" , timeout=self.ddp_timeout_delta )
__SCREAMING_SNAKE_CASE = int(os.getenv("SMDATAPARALLEL_LOCAL_RANK" ) )
__SCREAMING_SNAKE_CASE = torch.device("cuda" , self.local_rank )
__SCREAMING_SNAKE_CASE = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
__SCREAMING_SNAKE_CASE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
__SCREAMING_SNAKE_CASE = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="nccl" , timeout=self.ddp_timeout_delta )
__SCREAMING_SNAKE_CASE = torch.device("cuda" , self.local_rank )
__SCREAMING_SNAKE_CASE = 1
if device.type == "cuda":
torch.cuda.set_device(UpperCAmelCase__ )
return device
@property
def UpperCAmelCase_ ( self : Dict ) -> Any:
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[Any]:
return not is_sagemaker_model_parallel_available()
@property
def UpperCAmelCase_ ( self : Tuple ) -> int:
return False
| 54
| 0
|
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _lowerCamelCase ( lowerCamelCase_ : str=None , lowerCamelCase_ : Optional[int]=None ):
"""simple docstring"""
return field(default_factory=lambda: default , metadata=lowerCAmelCase__ )
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase_ :str = field(
metadata={'''help''': '''The csv file to plot.'''} , )
lowerCamelCase_ :bool = field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''} , )
lowerCamelCase_ :bool = field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''} , )
lowerCamelCase_ :bool = field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''Disable logarithmic scale when plotting'''} , )
lowerCamelCase_ :bool = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
} , )
lowerCamelCase_ :Optional[str] = field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''} , )
lowerCamelCase_ :Optional[List[str]] = list_field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''} )
def _lowerCamelCase ( lowerCamelCase_ : Dict ):
"""simple docstring"""
try:
int(lowerCAmelCase__ )
return True
except ValueError:
return False
def _lowerCamelCase ( lowerCamelCase_ : Optional[int] ):
"""simple docstring"""
try:
float(lowerCAmelCase__ )
return True
except ValueError:
return False
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = args
UpperCAmelCase_ : Union[str, Any] = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )
with open(self.args.csv_file , newline='' ) as csv_file:
UpperCAmelCase_ : List[str] = csv.DictReader(_a )
for row in reader:
UpperCAmelCase_ : str = row['model']
self.result_dict[model_name]["bsz"].append(int(row['batch_size'] ) )
self.result_dict[model_name]["seq_len"].append(int(row['sequence_length'] ) )
if can_convert_to_int(row['result'] ):
# value is not None
UpperCAmelCase_ : Tuple = int(row['result'] )
elif can_convert_to_float(row['result'] ):
# value is not None
UpperCAmelCase_ : Any = float(row['result'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : int = plt.subplots()
UpperCAmelCase_ : int = 'Time usage' if self.args.is_time else 'Memory usage'
UpperCAmelCase_ : int = title_str + ' for training' if self.args.is_train else title_str + ' for inference'
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale('log' )
ax.set_yscale('log' )
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter() )
for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
UpperCAmelCase_ : Any = sorted(set(self.result_dict[model_name]['bsz'] ) )
UpperCAmelCase_ : Optional[Any] = sorted(set(self.result_dict[model_name]['seq_len'] ) )
UpperCAmelCase_ : Optional[int] = self.result_dict[model_name]['result']
((UpperCAmelCase_) , (UpperCAmelCase_)) : Dict = (
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
UpperCAmelCase_ : Any = (
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
UpperCAmelCase_ : int = np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] , dtype=_a , )
else:
UpperCAmelCase_ : Dict = np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] , dtype=np.floataa , )
((UpperCAmelCase_) , (UpperCAmelCase_)) : List[str] = (
('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
)
UpperCAmelCase_ : str = np.asarray(_a , _a )[: len(_a )]
plt.scatter(
_a , _a , label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}''' )
plt.plot(_a , _a , '--' )
title_str += F''' {label_model_name} vs.'''
UpperCAmelCase_ : Tuple = title_str[:-4]
UpperCAmelCase_ : List[str] = 'Time in s' if self.args.is_time else 'Memory in MB'
# plot
plt.title(_a )
plt.xlabel(_a )
plt.ylabel(_a )
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file )
else:
plt.show()
def _lowerCamelCase ( ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = HfArgumentParser(lowerCAmelCase__ )
UpperCAmelCase_ : int = parser.parse_args_into_dataclasses()[0]
UpperCAmelCase_ : Optional[Any] = Plot(args=lowerCAmelCase__ )
plot.plot()
if __name__ == "__main__":
main()
| 361
|
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str = " " ):
"""simple docstring"""
UpperCAmelCase_ : str = []
UpperCAmelCase_ : List[Any] = 0
for index, char in enumerate(lowerCamelCase_ ):
if char == separator:
split_words.append(string[last_index:index] )
UpperCAmelCase_ : Optional[Any] = index + 1
elif index + 1 == len(lowerCamelCase_ ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 274
| 0
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : Optional[int] = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
),
}
class __snake_case ( _SCREAMING_SNAKE_CASE):
"""simple docstring"""
lowercase = 'lilt'
def __init__( self : Tuple , lowerCamelCase : Union[str, Any]=3_05_22 , lowerCamelCase : Dict=7_68 , lowerCamelCase : List[str]=12 , lowerCamelCase : Dict=12 , lowerCamelCase : Any=30_72 , lowerCamelCase : Optional[int]="gelu" , lowerCamelCase : Optional[int]=0.1 , lowerCamelCase : List[str]=0.1 , lowerCamelCase : List[str]=5_12 , lowerCamelCase : str=2 , lowerCamelCase : str=0.02 , lowerCamelCase : Tuple=1E-12 , lowerCamelCase : List[str]=0 , lowerCamelCase : List[Any]="absolute" , lowerCamelCase : Tuple=None , lowerCamelCase : Dict=4 , lowerCamelCase : str=10_24 , **lowerCamelCase : int , ) -> Optional[int]:
super().__init__(pad_token_id=lowerCamelCase , **lowerCamelCase )
lowerCAmelCase_ : Optional[int] = vocab_size
lowerCAmelCase_ : Dict = hidden_size
lowerCAmelCase_ : Union[str, Any] = num_hidden_layers
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : Dict = hidden_act
lowerCAmelCase_ : str = intermediate_size
lowerCAmelCase_ : Tuple = hidden_dropout_prob
lowerCAmelCase_ : Union[str, Any] = attention_probs_dropout_prob
lowerCAmelCase_ : Any = max_position_embeddings
lowerCAmelCase_ : int = type_vocab_size
lowerCAmelCase_ : str = initializer_range
lowerCAmelCase_ : str = layer_norm_eps
lowerCAmelCase_ : List[Any] = position_embedding_type
lowerCAmelCase_ : Any = classifier_dropout
lowerCAmelCase_ : Dict = channel_shrink_ratio
lowerCAmelCase_ : int = max_ad_position_embeddings
| 120
|
'''simple docstring'''
def UpperCamelCase_ ( A__ : int = 1_00 ):
'''simple docstring'''
lowerCAmelCase_ : int = set()
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : str = n + 1 # maximum limit
for a in range(2 , A__ ):
for b in range(2 , A__ ):
lowerCAmelCase_ : str = a**b # calculates the current power
collect_powers.add(A__ ) # adds the result to the set
return len(A__ )
if __name__ == "__main__":
print("Number of terms ", solution(int(str(input()).strip())))
| 120
| 1
|
from collections.abc import Callable
class a__ :
def __init__( self , UpperCAmelCase = None ) -> None:
# Stores actual heap items.
__a = []
# Stores indexes of each item for supporting updates and deletion.
__a = {}
# Stores current size of heap.
__a = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
__a = key or (lambda UpperCAmelCase : x)
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> int | None:
return int((i - 1) / 2 ) if i > 0 else None
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> int | None:
__a = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> int | None:
__a = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase ) -> None:
__a , __a = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
__a , __a = self.arr[j], self.arr[i]
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase ) -> bool:
return self.arr[i][1] < self.arr[j][1]
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> int:
__a = self._left(UpperCAmelCase )
__a = self._right(UpperCAmelCase )
__a = i
if left is not None and not self._cmp(UpperCAmelCase , UpperCAmelCase ):
__a = left
if right is not None and not self._cmp(UpperCAmelCase , UpperCAmelCase ):
__a = right
return valid_parent
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> None:
__a = self._parent(UpperCAmelCase )
while parent is not None and not self._cmp(UpperCAmelCase , UpperCAmelCase ):
self._swap(UpperCAmelCase , UpperCAmelCase )
__a , __a = parent, self._parent(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> None:
__a = self._get_valid_parent(UpperCAmelCase )
while valid_parent != index:
self._swap(UpperCAmelCase , UpperCAmelCase )
__a , __a = valid_parent, self._get_valid_parent(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase ) -> None:
if item not in self.pos_map:
return
__a = self.pos_map[item]
__a = [item, self.key(UpperCAmelCase )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(UpperCAmelCase )
self._heapify_down(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> None:
if item not in self.pos_map:
return
__a = self.pos_map[item]
del self.pos_map[item]
__a = self.arr[self.size - 1]
__a = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(UpperCAmelCase )
self._heapify_down(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase ) -> None:
__a = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(UpperCAmelCase )] )
else:
__a = [item, self.key(UpperCAmelCase )]
__a = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __SCREAMING_SNAKE_CASE ( self ) -> tuple | None:
return self.arr[0] if self.size else None
def __SCREAMING_SNAKE_CASE ( self ) -> tuple | None:
__a = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def lowerCAmelCase( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 197
|
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 ):
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> Optional[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(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
__a = 'sshleifer/tiny-gpt2'
__a = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=UpperCAmelCase , multi_process=UpperCAmelCase , )
__a = TensorFlowBenchmark(UpperCAmelCase )
__a = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
__a = 'sgugger/tiny-distilbert-classification'
__a = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , only_pretrain_model=UpperCAmelCase , )
__a = TensorFlowBenchmark(UpperCAmelCase )
__a = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
__a = 'sshleifer/tiny-gpt2'
__a = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
__a = TensorFlowBenchmark(UpperCAmelCase )
__a = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
__a = 'sshleifer/tiny-gpt2'
__a = AutoConfig.from_pretrained(UpperCAmelCase )
__a = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=UpperCAmelCase , multi_process=UpperCAmelCase , )
__a = TensorFlowBenchmark(UpperCAmelCase , [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 __SCREAMING_SNAKE_CASE ( self ) -> Any:
__a = 'sshleifer/tiny-gpt2'
__a = AutoConfig.from_pretrained(UpperCAmelCase )
__a = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
__a = TensorFlowBenchmark(UpperCAmelCase , [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 __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
__a = 'sshleifer/tiny-gpt2'
__a = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
__a = TensorFlowBenchmark(UpperCAmelCase )
__a = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
__a = 'sshleifer/tiny-gpt2'
__a = AutoConfig.from_pretrained(UpperCAmelCase )
__a = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
__a = TensorFlowBenchmark(UpperCAmelCase , [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 __SCREAMING_SNAKE_CASE ( self ) -> List[str]:
__a = 'patrickvonplaten/t5-tiny-random'
__a = AutoConfig.from_pretrained(UpperCAmelCase )
__a = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
__a = TensorFlowBenchmark(UpperCAmelCase , 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 )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU' ) ) == 0 , 'Cannot do xla on CPU.' )
def __SCREAMING_SNAKE_CASE ( self ) -> str:
__a = 'sshleifer/tiny-gpt2'
__a = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , use_xla=UpperCAmelCase , multi_process=UpperCAmelCase , )
__a = TensorFlowBenchmark(UpperCAmelCase )
__a = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
__a = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
__a = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=UpperCAmelCase , save_to_csv=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(UpperCAmelCase , 'inf_time.csv' ) , inference_memory_csv_file=os.path.join(UpperCAmelCase , 'inf_mem.csv' ) , env_info_csv_file=os.path.join(UpperCAmelCase , 'env.csv' ) , multi_process=UpperCAmelCase , )
__a = TensorFlowBenchmark(UpperCAmelCase )
benchmark.run()
self.assertTrue(Path(os.path.join(UpperCAmelCase , 'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(UpperCAmelCase , 'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(UpperCAmelCase , 'env.csv' ) ).exists() )
def __SCREAMING_SNAKE_CASE ( self ) -> Tuple:
__a = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(UpperCAmelCase ):
self.assertTrue(hasattr(UpperCAmelCase , 'sequential' ) )
self.assertTrue(hasattr(UpperCAmelCase , 'cumulative' ) )
self.assertTrue(hasattr(UpperCAmelCase , 'current' ) )
self.assertTrue(hasattr(UpperCAmelCase , 'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
__a = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(UpperCAmelCase , 'log.txt' ) , log_print=UpperCAmelCase , trace_memory_line_by_line=UpperCAmelCase , eager_mode=UpperCAmelCase , multi_process=UpperCAmelCase , )
__a = TensorFlowBenchmark(UpperCAmelCase )
__a = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(UpperCAmelCase , 'log.txt' ) ).exists() )
| 197
| 1
|
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def UpperCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str = "cpu" , lowerCAmelCase__ : Union[str, None] = None ) -> None:
"""simple docstring"""
lowerCAmelCase_ : Any = torch.load(lowerCAmelCase__ , map_location=lowerCAmelCase__ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowerCAmelCase__ , torch.Tensor ):
raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin' )
lowerCAmelCase_ : str = v.half()
if save_path is None: # overwrite src_path
lowerCAmelCase_ : Dict = src_path
torch.save(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
fire.Fire(convert)
| 224
|
"""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 UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
"""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."""
)
_SCREAMING_SNAKE_CASE = """CIDAS/clipseg-rd64-refined"""
_SCREAMING_SNAKE_CASE = """image_segmenter"""
_SCREAMING_SNAKE_CASE = CLIPSegForImageSegmentation
_SCREAMING_SNAKE_CASE = ["""image""", """text"""]
_SCREAMING_SNAKE_CASE = ["""image"""]
def __init__( self : Dict , *SCREAMING_SNAKE_CASE_ : Tuple , **SCREAMING_SNAKE_CASE_ : Optional[int] ):
requires_backends(self , ['vision'] )
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : "Image" , SCREAMING_SNAKE_CASE_ : str ):
return self.pre_processor(text=[label] , images=[image] , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , SCREAMING_SNAKE_CASE_ : int ):
with torch.no_grad():
lowerCAmelCase_ : List[str] = self.model(**SCREAMING_SNAKE_CASE_ ).logits
return logits
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
lowerCAmelCase_ : Dict = outputs.cpu().detach().numpy()
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : Optional[Any] = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 224
| 1
|
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'''stable diffusion controlnet''',
'''0.22.0''',
'''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
standard_warn=False,
stacklevel=3,
)
| 228
|
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _snake_case ( A , A , A , A=5 ) -> List[str]:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('''<mask>''' ) == 1
lowerCAmelCase__ = torch.tensor(tokenizer.encode(A , add_special_tokens=A ) ).unsqueeze(0 ) # Batch size 1
lowerCAmelCase__ = model(A )[0] # The last hidden-state is the first element of the output tuple
lowerCAmelCase__ = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
lowerCAmelCase__ = logits[0, masked_index, :]
lowerCAmelCase__ = logits.softmax(dim=0 )
lowerCAmelCase__ , lowerCAmelCase__ = prob.topk(k=A , dim=0 )
lowerCAmelCase__ = ''' '''.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(A ) )] )
lowerCAmelCase__ = tokenizer.mask_token
lowerCAmelCase__ = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' ''' ) ):
lowerCAmelCase__ = predicted_token_bpe.replace('''\u2581''' , ''' ''' )
if " {0}".format(A ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(''' {0}'''.format(A ) , A ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(A , A ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
__UpperCAmelCase = CamembertTokenizer.from_pretrained('''camembert-base''')
__UpperCAmelCase = CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()
__UpperCAmelCase = '''Le camembert est <mask> :)'''
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 228
| 1
|
from math import factorial, radians
def a ( lowerCamelCase_ , lowerCamelCase_ = 18 , lowerCamelCase_ = 10 ):
'''simple docstring'''
lowercase__ = angle_in_degrees - ((angle_in_degrees // 3_60.0) * 3_60.0)
# Converting from degrees to radians
lowercase__ = radians(lowerCamelCase_ )
lowercase__ = angle_in_radians
lowercase__ = 3
lowercase__ = -1
for _ in range(lowerCamelCase_ ):
result += (b * (angle_in_radians**a)) / factorial(lowerCamelCase_ )
lowercase__ = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(lowerCamelCase_ , lowerCamelCase_ )
if __name__ == "__main__":
__import__('doctest').testmod()
| 207
|
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
A__ : Tuple = logging.get_logger(__name__)
A__ : int = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : int=None, lowerCamelCase : int=None, *lowerCamelCase : List[Any], **lowerCamelCase : Any ):
'''simple docstring'''
super().__init__(*lowerCamelCase, **lowerCamelCase )
if config is None:
assert isinstance(self.model, lowerCamelCase ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
lowercase__ = self.model.config
else:
lowercase__ = config
lowercase__ = data_args
lowercase__ = self.config.tgt_vocab_size if isinstance(self.config, lowerCamelCase ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
''' padding..''' )
if self.args.label_smoothing == 0:
lowercase__ = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
lowercase__ = label_smoothed_nll_loss
def lowercase__ ( self : List[Any], lowerCamelCase : int ):
'''simple docstring'''
if self.optimizer is None:
lowercase__ = ['''bias''', '''LayerNorm.weight''']
lowercase__ = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
lowercase__ = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
lowercase__ = Adafactor
lowercase__ = {'''scale_parameter''': False, '''relative_step''': False}
else:
lowercase__ = AdamW
lowercase__ = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
lowercase__ = self.args.learning_rate
if self.sharded_ddp:
lowercase__ = OSS(
params=lowerCamelCase, optim=lowerCamelCase, **lowerCamelCase, )
else:
lowercase__ = optimizer_cls(lowerCamelCase, **lowerCamelCase )
if self.lr_scheduler is None:
lowercase__ = self._get_lr_scheduler(lowerCamelCase )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def lowercase__ ( self : List[str], lowerCamelCase : Optional[int] ):
'''simple docstring'''
lowercase__ = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
lowercase__ = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
lowercase__ = schedule_func(self.optimizer, num_warmup_steps=self.args.warmup_steps )
else:
lowercase__ = schedule_func(
self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=lowerCamelCase )
return scheduler
def lowercase__ ( self : List[Any] ):
'''simple docstring'''
if isinstance(self.train_dataset, torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size, distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED), )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def lowercase__ ( self : Any, lowerCamelCase : List[Any], lowerCamelCase : str, lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
lowercase__ = model(**lowerCamelCase, use_cache=lowerCamelCase )[0]
lowercase__ = self.loss_fn(logits.view(-1, logits.shape[-1] ), labels.view(-1 ) )
else:
# compute usual loss via models
lowercase__ , lowercase__ = model(**lowerCamelCase, labels=lowerCamelCase, use_cache=lowerCamelCase )[:2]
else:
# compute label smoothed loss
lowercase__ = model(**lowerCamelCase, use_cache=lowerCamelCase )[0]
lowercase__ = torch.nn.functional.log_softmax(lowerCamelCase, dim=-1 )
lowercase__ , lowercase__ = self.loss_fn(lowerCamelCase, lowerCamelCase, self.args.label_smoothing, ignore_index=self.config.pad_token_id )
return loss, logits
def lowercase__ ( self : List[str], lowerCamelCase : Optional[Any], lowerCamelCase : Tuple ):
'''simple docstring'''
lowercase__ = inputs.pop('''labels''' )
lowercase__ , lowercase__ = self._compute_loss(lowerCamelCase, lowerCamelCase, lowerCamelCase )
return loss
def lowercase__ ( self : str, lowerCamelCase : nn.Module, lowerCamelCase : Dict[str, Union[torch.Tensor, Any]], lowerCamelCase : bool, lowerCamelCase : Optional[List[str]] = None, ):
'''simple docstring'''
lowercase__ = self._prepare_inputs(lowerCamelCase )
lowercase__ = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
lowercase__ = self.model.generate(
inputs['''input_ids'''], attention_mask=inputs['''attention_mask'''], **lowerCamelCase, )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
lowercase__ = self._pad_tensors_to_max_len(lowerCamelCase, gen_kwargs['''max_length'''] )
lowercase__ = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
lowercase__ , lowercase__ = self._compute_loss(lowerCamelCase, lowerCamelCase, lowerCamelCase )
lowercase__ = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
lowercase__ = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
lowercase__ = self._pad_tensors_to_max_len(lowerCamelCase, gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def lowercase__ ( self : List[Any], lowerCamelCase : str, lowerCamelCase : Any ):
'''simple docstring'''
# If PAD token is not defined at least EOS token has to be defined
lowercase__ = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
F""" padded to `max_length`={max_length}""" )
lowercase__ = pad_token_id * torch.ones(
(tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device )
lowercase__ = tensor
return padded_tensor
| 207
| 1
|
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __a ( unittest.TestCase ):
def __init__( self , a__ , a__=3 , a__=32 , a__=3 , a__=10 , a__=[10, 20, 30, 40] , a__=[1, 1, 2, 1] , a__=True , a__=True , a__="relu" , a__=3 , a__=None , ):
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = image_size
_lowerCamelCase = num_channels
_lowerCamelCase = embeddings_size
_lowerCamelCase = hidden_sizes
_lowerCamelCase = depths
_lowerCamelCase = is_training
_lowerCamelCase = use_labels
_lowerCamelCase = hidden_act
_lowerCamelCase = num_labels
_lowerCamelCase = scope
_lowerCamelCase = len(a__ )
def snake_case_ ( self ):
_lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase = self.get_config()
return config, pixel_values
def snake_case_ ( self ):
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def snake_case_ ( self , a__ , a__ ):
_lowerCamelCase = FlaxRegNetModel(config=a__ )
_lowerCamelCase = model(a__ )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def snake_case_ ( self , a__ , a__ ):
_lowerCamelCase = self.num_labels
_lowerCamelCase = FlaxRegNetForImageClassification(config=a__ )
_lowerCamelCase = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case_ ( self ):
_lowerCamelCase = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase = config_and_inputs
_lowerCamelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_flax
class __a ( lowerCAmelCase__ , unittest.TestCase ):
SCREAMING_SNAKE_CASE__ : int = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = False
SCREAMING_SNAKE_CASE__ : Tuple = False
SCREAMING_SNAKE_CASE__ : List[str] = False
def snake_case_ ( self ):
_lowerCamelCase = FlaxRegNetModelTester(self )
_lowerCamelCase = ConfigTester(self , config_class=a__ , has_text_modality=a__ )
def snake_case_ ( self ):
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 snake_case_ ( self ):
return
def snake_case_ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a__ )
def snake_case_ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a__ )
@unittest.skip(reason='RegNet does not use inputs_embeds' )
def snake_case_ ( self ):
pass
@unittest.skip(reason='RegNet does not support input and output embeddings' )
def snake_case_ ( self ):
pass
def snake_case_ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(a__ )
_lowerCamelCase = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase = [*signature.parameters.keys()]
_lowerCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , a__ )
def snake_case_ ( self ):
def check_hidden_states_output(a__ , a__ , a__ ):
_lowerCamelCase = model_class(a__ )
_lowerCamelCase = model(**self._prepare_for_class(a__ , a__ ) )
_lowerCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_lowerCamelCase = self.model_tester.num_stages
self.assertEqual(len(a__ ) , expected_num_stages + 1 )
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = True
check_hidden_states_output(a__ , a__ , a__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase = True
check_hidden_states_output(a__ , a__ , a__ )
def snake_case_ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_lowerCamelCase = self._prepare_for_class(a__ , a__ )
_lowerCamelCase = model_class(a__ )
@jax.jit
def model_jitted(a__ , **a__ ):
return model(pixel_values=a__ , **a__ )
with self.subTest('JIT Enabled' ):
_lowerCamelCase = model_jitted(**a__ ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
_lowerCamelCase = model_jitted(**a__ ).to_tuple()
self.assertEqual(len(a__ ) , len(a__ ) )
for jitted_output, output in zip(a__ , a__ ):
self.assertEqual(jitted_output.shape , output.shape )
def SCREAMING_SNAKE_CASE_ ( )-> List[str]:
_lowerCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_flax
class __a ( unittest.TestCase ):
@cached_property
def snake_case_ ( self ):
return AutoImageProcessor.from_pretrained('facebook/regnet-y-040' ) if is_vision_available() else None
@slow
def snake_case_ ( self ):
_lowerCamelCase = FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040' )
_lowerCamelCase = self.default_image_processor
_lowerCamelCase = prepare_img()
_lowerCamelCase = image_processor(images=a__ , return_tensors='np' )
_lowerCamelCase = model(**a__ )
# verify the logits
_lowerCamelCase = (1, 10_00)
self.assertEqual(outputs.logits.shape , a__ )
_lowerCamelCase = jnp.array([-0.4180, -1.5051, -3.4836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , a__ , atol=1e-4 ) )
| 80
|
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class __a ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : "DiagonalGaussianDistribution"
class __a ( lowerCAmelCase__ , lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : Any = True
@register_to_config
def __init__( self , a__ = 3 , a__ = 3 , a__ = ("DownEncoderBlock2D",) , a__ = ("UpDecoderBlock2D",) , a__ = (64,) , a__ = 1 , a__ = "silu" , a__ = 4 , a__ = 32 , a__ = 32 , a__ = 0.18215 , ):
super().__init__()
# pass init params to Encoder
_lowerCamelCase = Encoder(
in_channels=a__ , out_channels=a__ , down_block_types=a__ , block_out_channels=a__ , layers_per_block=a__ , act_fn=a__ , norm_num_groups=a__ , double_z=a__ , )
# pass init params to Decoder
_lowerCamelCase = Decoder(
in_channels=a__ , out_channels=a__ , up_block_types=a__ , block_out_channels=a__ , layers_per_block=a__ , norm_num_groups=a__ , act_fn=a__ , )
_lowerCamelCase = nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
_lowerCamelCase = nn.Convad(a__ , a__ , 1 )
_lowerCamelCase = False
_lowerCamelCase = False
# only relevant if vae tiling is enabled
_lowerCamelCase = self.config.sample_size
_lowerCamelCase = (
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
_lowerCamelCase = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
_lowerCamelCase = 0.25
def snake_case_ ( self , a__ , a__=False ):
if isinstance(a__ , (Encoder, Decoder) ):
_lowerCamelCase = value
def snake_case_ ( self , a__ = True ):
_lowerCamelCase = use_tiling
def snake_case_ ( self ):
self.enable_tiling(a__ )
def snake_case_ ( self ):
_lowerCamelCase = True
def snake_case_ ( self ):
_lowerCamelCase = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def snake_case_ ( self ):
_lowerCamelCase = {}
def fn_recursive_add_processors(a__ , a__ , a__ ):
if hasattr(a__ , 'set_processor' ):
_lowerCamelCase = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(F'{name}.{sub_name}' , a__ , a__ )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(a__ , a__ , a__ )
return processors
def snake_case_ ( self , a__ ):
_lowerCamelCase = len(self.attn_processors.keys() )
if isinstance(a__ , a__ ) and len(a__ ) != count:
raise ValueError(
F'A dict of processors was passed, but the number of processors {len(a__ )} does not match the'
F' number of attention layers: {count}. Please make sure to pass {count} processor classes.' )
def fn_recursive_attn_processor(a__ , a__ , a__ ):
if hasattr(a__ , 'set_processor' ):
if not isinstance(a__ , a__ ):
module.set_processor(a__ )
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}' , a__ , a__ )
for name, module in self.named_children():
fn_recursive_attn_processor(a__ , a__ , a__ )
def snake_case_ ( self ):
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def snake_case_ ( self , a__ , a__ = True ):
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(a__ , return_dict=a__ )
if self.use_slicing and x.shape[0] > 1:
_lowerCamelCase = [self.encoder(a__ ) for x_slice in x.split(1 )]
_lowerCamelCase = torch.cat(a__ )
else:
_lowerCamelCase = self.encoder(a__ )
_lowerCamelCase = self.quant_conv(a__ )
_lowerCamelCase = DiagonalGaussianDistribution(a__ )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=a__ )
def snake_case_ ( self , a__ , a__ = True ):
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(a__ , return_dict=a__ )
_lowerCamelCase = self.post_quant_conv(a__ )
_lowerCamelCase = self.decoder(a__ )
if not return_dict:
return (dec,)
return DecoderOutput(sample=a__ )
@apply_forward_hook
def snake_case_ ( self , a__ , a__ = True ):
if self.use_slicing and z.shape[0] > 1:
_lowerCamelCase = [self._decode(a__ ).sample for z_slice in z.split(1 )]
_lowerCamelCase = torch.cat(a__ )
else:
_lowerCamelCase = self._decode(a__ ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=a__ )
def snake_case_ ( self , a__ , a__ , a__ ):
_lowerCamelCase = min(a.shape[2] , b.shape[2] , a__ )
for y in range(a__ ):
_lowerCamelCase = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def snake_case_ ( self , a__ , a__ , a__ ):
_lowerCamelCase = min(a.shape[3] , b.shape[3] , a__ )
for x in range(a__ ):
_lowerCamelCase = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def snake_case_ ( self , a__ , a__ = True ):
_lowerCamelCase = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
_lowerCamelCase = int(self.tile_latent_min_size * self.tile_overlap_factor )
_lowerCamelCase = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
_lowerCamelCase = []
for i in range(0 , x.shape[2] , a__ ):
_lowerCamelCase = []
for j in range(0 , x.shape[3] , a__ ):
_lowerCamelCase = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
_lowerCamelCase = self.encoder(a__ )
_lowerCamelCase = self.quant_conv(a__ )
row.append(a__ )
rows.append(a__ )
_lowerCamelCase = []
for i, row in enumerate(a__ ):
_lowerCamelCase = []
for j, tile in enumerate(a__ ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
_lowerCamelCase = self.blend_v(rows[i - 1][j] , a__ , a__ )
if j > 0:
_lowerCamelCase = self.blend_h(row[j - 1] , a__ , a__ )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(a__ , dim=3 ) )
_lowerCamelCase = torch.cat(a__ , dim=2 )
_lowerCamelCase = DiagonalGaussianDistribution(a__ )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=a__ )
def snake_case_ ( self , a__ , a__ = True ):
_lowerCamelCase = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
_lowerCamelCase = int(self.tile_sample_min_size * self.tile_overlap_factor )
_lowerCamelCase = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
_lowerCamelCase = []
for i in range(0 , z.shape[2] , a__ ):
_lowerCamelCase = []
for j in range(0 , z.shape[3] , a__ ):
_lowerCamelCase = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
_lowerCamelCase = self.post_quant_conv(a__ )
_lowerCamelCase = self.decoder(a__ )
row.append(a__ )
rows.append(a__ )
_lowerCamelCase = []
for i, row in enumerate(a__ ):
_lowerCamelCase = []
for j, tile in enumerate(a__ ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
_lowerCamelCase = self.blend_v(rows[i - 1][j] , a__ , a__ )
if j > 0:
_lowerCamelCase = self.blend_h(row[j - 1] , a__ , a__ )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(a__ , dim=3 ) )
_lowerCamelCase = torch.cat(a__ , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=a__ )
def snake_case_ ( self , a__ , a__ = False , a__ = True , a__ = None , ):
_lowerCamelCase = sample
_lowerCamelCase = self.encode(a__ ).latent_dist
if sample_posterior:
_lowerCamelCase = posterior.sample(generator=a__ )
else:
_lowerCamelCase = posterior.mode()
_lowerCamelCase = self.decode(a__ ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=a__ )
| 80
| 1
|
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : str = (KDPMaDiscreteScheduler,)
lowerCamelCase : Optional[int] = 10
def __UpperCAmelCase ( self : List[Any] , **UpperCAmelCase__ : Optional[int] ) -> Any:
lowerCAmelCase = {
'num_train_timesteps': 1_1_0_0,
'beta_start': 0.0_001,
'beta_end': 0.02,
'beta_schedule': 'linear',
}
config.update(**UpperCAmelCase__ )
return config
def __UpperCAmelCase ( self : List[str] ) -> Tuple:
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase__ )
def __UpperCAmelCase ( self : Dict ) -> Optional[Any]:
for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] , [0.0_002, 0.002, 0.02] ):
self.check_over_configs(beta_start=UpperCAmelCase__ , beta_end=UpperCAmelCase__ )
def __UpperCAmelCase ( self : Optional[Any] ) -> int:
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=UpperCAmelCase__ )
def __UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase__ )
def __UpperCAmelCase ( self : int ) -> Dict:
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config(prediction_type='v_prediction' )
lowerCAmelCase = scheduler_class(**UpperCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase = self.dummy_model()
lowerCAmelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase = sample.to(UpperCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase = scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = model(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = output.prev_sample
lowerCAmelCase = torch.sum(torch.abs(UpperCAmelCase__ ) )
lowerCAmelCase = torch.mean(torch.abs(UpperCAmelCase__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.69_34E-07 ) < 1E-2
assert abs(result_mean.item() - 6.11_12E-10 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07 ) < 1E-2
assert abs(result_mean.item() - 0.0_002 ) < 1E-3
def __UpperCAmelCase ( self : Tuple ) -> Dict:
if torch_device == "mps":
return
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config()
lowerCAmelCase = scheduler_class(**UpperCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase = self.dummy_model()
lowerCAmelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase = sample.to(UpperCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase = scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = model(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = output.prev_sample
lowerCAmelCase = torch.sum(torch.abs(UpperCAmelCase__ ) )
lowerCAmelCase = torch.mean(torch.abs(UpperCAmelCase__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
def __UpperCAmelCase ( self : List[Any] ) -> Optional[Any]:
if torch_device == "mps":
return
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config()
lowerCAmelCase = scheduler_class(**UpperCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=UpperCAmelCase__ )
lowerCAmelCase = self.dummy_model()
lowerCAmelCase = self.dummy_sample_deter.to(UpperCAmelCase__ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
lowerCAmelCase = scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = model(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = output.prev_sample
lowerCAmelCase = torch.sum(torch.abs(UpperCAmelCase__ ) )
lowerCAmelCase = torch.mean(torch.abs(UpperCAmelCase__ ) )
if str(UpperCAmelCase__ ).startswith('cpu' ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
| 4
|
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __lowerCamelCase ( __a :Dict ) -> Any:
"""simple docstring"""
A__ = [
"""decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(__a , __a )
def __lowerCamelCase ( __a :str ) -> Union[str, Any]:
"""simple docstring"""
A__ , A__ = emb.weight.shape
A__ = nn.Linear(__a , __a , bias=__a )
A__ = emb.weight.data
return lin_layer
def __lowerCamelCase ( __a :str ) -> List[str]:
"""simple docstring"""
A__ = torch.load(__a , map_location="""cpu""" )
A__ = Namespace(**checkpoint["""cfg"""]["""model"""] )
A__ = checkpoint["""model"""]
remove_ignore_keys_(__a )
A__ = state_dict["""decoder.embed_tokens.weight"""].shape[0]
A__ = {key.replace("""decoder""" , """model""" ): val for key, val in state_dict.items()}
A__ = XGLMConfig(
vocab_size=__a , max_position_embeddings=args.max_target_positions , num_layers=args.decoder_layers , attention_heads=args.decoder_attention_heads , ffn_dim=args.decoder_ffn_embed_dim , d_model=args.decoder_embed_dim , layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""gelu""" , scale_embedding=not args.no_scale_embedding , tie_word_embeddings=args.share_decoder_input_output_embed , )
A__ = XGLMForCausalLM(__a )
A__ = model.load_state_dict(__a , strict=__a )
print(__a )
A__ = make_linear_from_emb(model.model.embed_tokens )
return model
if __name__ == "__main__":
A : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
A : str = parser.parse_args()
A : str = convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
model.save_pretrained(args.pytorch_dump_folder_path)
| 274
| 0
|
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require_flax
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self , lowercase , lowercase) -> Tuple:
'''simple docstring'''
return F'gaussian_noise_s={seed}_shape={"_".join([str(lowerCamelCase_) for s in shape])}.npy'
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
def __lowercase ( self , lowercase=0 , lowercase=(4, 4, 64, 64) , lowercase=False) -> List[str]:
'''simple docstring'''
a__ : Optional[Any] = jnp.bfloataa if fpaa else jnp.floataa
a__ : List[str] = jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase_ , lowerCamelCase_)) , dtype=lowerCamelCase_)
return image
def __lowercase ( self , lowercase=False , lowercase="CompVis/stable-diffusion-v1-4") -> Union[str, Any]:
'''simple docstring'''
a__ : int = jnp.bfloataa if fpaa else jnp.floataa
a__ : Optional[Any] = """bf16""" if fpaa else None
a__ : Any = FlaxUNetaDConditionModel.from_pretrained(
lowerCamelCase_ , subfolder='unet' , dtype=lowerCamelCase_ , revision=lowerCamelCase_)
return model, params
def __lowercase ( self , lowercase=0 , lowercase=(4, 77, 768) , lowercase=False) -> Union[str, Any]:
'''simple docstring'''
a__ : Union[str, Any] = jnp.bfloataa if fpaa else jnp.floataa
a__ : Any = jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase_ , lowerCamelCase_)) , dtype=lowerCamelCase_)
return hidden_states
@parameterized.expand(
[
# fmt: off
[83, 4, [-0.23_23, -0.13_04, 0.08_13, -0.30_93, -0.09_19, -0.15_71, -0.11_25, -0.58_06]],
[17, 0.55, [-0.08_31, -0.24_43, 0.09_01, -0.09_19, 0.33_96, 0.01_03, -0.37_43, 0.07_01]],
[8, 0.89, [-0.48_63, 0.08_59, 0.08_75, -0.16_58, 0.91_99, -0.01_14, 0.48_39, 0.46_39]],
[3, 1000, [-0.56_49, 0.24_02, -0.55_18, 0.12_48, 1.13_28, -0.24_43, -0.03_25, -1.00_78]],
# fmt: on
])
def __lowercase ( self , lowercase , lowercase , lowercase) -> Union[str, Any]:
'''simple docstring'''
a__ : List[str] = self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4' , fpaa=lowerCamelCase_)
a__ : Tuple = self.get_latents(lowerCamelCase_ , fpaa=lowerCamelCase_)
a__ : Union[str, Any] = self.get_encoder_hidden_states(lowerCamelCase_ , fpaa=lowerCamelCase_)
a__ : int = model.apply(
{'params': params} , lowerCamelCase_ , jnp.array(lowerCamelCase_ , dtype=jnp.intaa) , encoder_hidden_states=lowerCamelCase_ , ).sample
assert sample.shape == latents.shape
a__ : Any = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())) , dtype=jnp.floataa)
a__ : Dict = jnp.array(lowerCamelCase_ , dtype=jnp.floataa)
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
assert jnp.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1e-2)
@parameterized.expand(
[
# fmt: off
[83, 4, [0.15_14, 0.08_07, 0.16_24, 0.10_16, -0.18_96, 0.02_63, 0.06_77, 0.23_10]],
[17, 0.55, [0.11_64, -0.02_16, 0.01_70, 0.15_89, -0.31_20, 0.10_05, -0.05_81, -0.14_58]],
[8, 0.89, [-0.17_58, -0.01_69, 0.10_04, -0.14_11, 0.13_12, 0.11_03, -0.19_96, 0.21_39]],
[3, 1000, [0.12_14, 0.03_52, -0.07_31, -0.15_62, -0.09_94, -0.09_06, -0.23_40, -0.05_39]],
# fmt: on
])
def __lowercase ( self , lowercase , lowercase , lowercase) -> Union[str, Any]:
'''simple docstring'''
a__ : int = self.get_unet_model(model_id='stabilityai/stable-diffusion-2' , fpaa=lowerCamelCase_)
a__ : List[Any] = self.get_latents(lowerCamelCase_ , shape=(4, 4, 96, 96) , fpaa=lowerCamelCase_)
a__ : Dict = self.get_encoder_hidden_states(lowerCamelCase_ , shape=(4, 77, 1024) , fpaa=lowerCamelCase_)
a__ : List[Any] = model.apply(
{'params': params} , lowerCamelCase_ , jnp.array(lowerCamelCase_ , dtype=jnp.intaa) , encoder_hidden_states=lowerCamelCase_ , ).sample
assert sample.shape == latents.shape
a__ : List[str] = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())) , dtype=jnp.floataa)
a__ : Dict = jnp.array(lowerCamelCase_ , dtype=jnp.floataa)
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
assert jnp.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1e-2)
| 365
|
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_mvp import MvpTokenizer
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
# See all MVP models at https://huggingface.co/models?filter=mvp
lowercase : Optional[Any] = {
"""vocab_file""": {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
},
"""added_tokens.json""": {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
},
"""merges_file""": {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
},
}
lowercase : List[str] = {
"""RUCAIBox/mvp""": 1_0_2_4,
}
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
__A : Dict = VOCAB_FILES_NAMES
__A : str = PRETRAINED_VOCAB_FILES_MAP
__A : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A : Any = ['''input_ids''', '''attention_mask''']
__A : Tuple = MvpTokenizer
def __init__( self , lowercase=None , lowercase=None , lowercase=None , lowercase="replace" , lowercase="<s>" , lowercase="</s>" , lowercase="</s>" , lowercase="<s>" , lowercase="<unk>" , lowercase="<pad>" , lowercase="<mask>" , lowercase=False , lowercase=True , **lowercase , ) -> str:
'''simple docstring'''
super().__init__(
lowercase , lowercase , tokenizer_file=lowercase , errors=lowercase , bos_token=lowercase , eos_token=lowercase , sep_token=lowercase , cls_token=lowercase , unk_token=lowercase , pad_token=lowercase , mask_token=lowercase , add_prefix_space=lowercase , trim_offsets=lowercase , **lowercase , )
a__ : Optional[int] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get('add_prefix_space' , lowercase) != add_prefix_space:
a__ : Dict = getattr(lowercase , pre_tok_state.pop('type'))
a__ : str = add_prefix_space
a__ : Union[str, Any] = pre_tok_class(**lowercase)
a__ : List[Any] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
a__ : Optional[int] = 'post_processor'
a__ : Optional[int] = getattr(self.backend_tokenizer , lowercase , lowercase)
if tokenizer_component_instance:
a__ : str = 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__ : Any = tuple(state['sep'])
if "cls" in state:
a__ : str = tuple(state['cls'])
a__ : List[str] = False
if state.get('add_prefix_space' , lowercase) != add_prefix_space:
a__ : List[str] = add_prefix_space
a__ : List[str] = True
if state.get('trim_offsets' , lowercase) != trim_offsets:
a__ : Optional[int] = trim_offsets
a__ : List[str] = True
if changes_to_apply:
a__ : Optional[int] = getattr(lowercase , state.pop('type'))
a__ : Tuple = component_class(**lowercase)
setattr(self.backend_tokenizer , lowercase , lowercase)
@property
def __lowercase ( self) -> str:
'''simple docstring'''
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 __lowercase ( self , lowercase) -> Any:
'''simple docstring'''
a__ : Tuple = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase) if isinstance(lowercase , lowercase) else value
a__ : str = value
def __lowercase ( self , *lowercase , **lowercase) -> BatchEncoding:
'''simple docstring'''
a__ : Optional[Any] = kwargs.get('is_split_into_words' , lowercase)
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
'to use it with pretokenized inputs.')
return super()._batch_encode_plus(*lowercase , **lowercase)
def __lowercase ( self , *lowercase , **lowercase) -> BatchEncoding:
'''simple docstring'''
a__ : List[str] = kwargs.get('is_split_into_words' , lowercase)
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
'to use it with pretokenized inputs.')
return super()._encode_plus(*lowercase , **lowercase)
def __lowercase ( self , lowercase , lowercase = None) -> Tuple[str]:
'''simple docstring'''
a__ : List[str] = self._tokenizer.model.save(lowercase , name=lowercase)
return tuple(lowercase)
def __lowercase ( self , lowercase , lowercase=None) -> Union[str, Any]:
'''simple docstring'''
a__ : List[Any] = [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 __lowercase ( self , lowercase , lowercase = None) -> List[int]:
'''simple docstring'''
a__ : int = [self.sep_token_id]
a__ : Any = [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]
| 225
| 0
|
"""simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :int = 1_0_0_0 ) -> int:
'''simple docstring'''
lowercase = 2**power
lowercase = 0
while n:
lowercase , lowercase = r + n % 1_0, n // 1_0
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 197
|
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _A :
def A__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowercase = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowercase = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowercase = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowercase = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=__lowerCAmelCase , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def A__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowercase = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowercase = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowercase = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , class_embed_type="""timestep""" , mid_block_scale_factor=1.4_1_4 , time_embedding_act_fn="""gelu""" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowercase = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=__lowerCAmelCase , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowercase = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , )
torch.manual_seed(0 )
lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def A__ ( self ):
"""simple docstring"""
lowercase = self.get_dummy_components()
lowercase = self.pipeline_class(**__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase = self.get_dummy_inputs(__lowerCAmelCase )
lowercase = inputs["""prompt"""]
lowercase = inputs["""generator"""]
lowercase = inputs["""num_inference_steps"""]
lowercase = inputs["""output_type"""]
if "image" in inputs:
lowercase = inputs["""image"""]
else:
lowercase = None
if "mask_image" in inputs:
lowercase = inputs["""mask_image"""]
else:
lowercase = None
if "original_image" in inputs:
lowercase = inputs["""original_image"""]
else:
lowercase = None
lowercase , lowercase = pipe.encode_prompt(__lowerCAmelCase )
# inputs with prompt converted to embeddings
lowercase = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
lowercase = image
if mask_image is not None:
lowercase = mask_image
if original_image is not None:
lowercase = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
lowercase = pipe(**__lowerCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__lowerCAmelCase )
lowercase = self.pipeline_class.from_pretrained(__lowerCAmelCase )
pipe_loaded.to(__lowerCAmelCase )
pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(__lowerCAmelCase , __lowerCAmelCase ) is None , f'`{optional_component}` did not stay set to None after loading.' , )
lowercase = self.get_dummy_inputs(__lowerCAmelCase )
lowercase = inputs["""generator"""]
lowercase = inputs["""num_inference_steps"""]
lowercase = inputs["""output_type"""]
# inputs with prompt converted to embeddings
lowercase = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
lowercase = image
if mask_image is not None:
lowercase = mask_image
if original_image is not None:
lowercase = original_image
lowercase = pipe_loaded(**__lowerCAmelCase )[0]
lowercase = np.abs(to_np(__lowerCAmelCase ) - to_np(__lowerCAmelCase ) ).max()
self.assertLess(__lowerCAmelCase , 1E-4 )
def A__ ( self ):
"""simple docstring"""
lowercase = self.get_dummy_components()
lowercase = self.pipeline_class(**__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase = self.get_dummy_inputs(__lowerCAmelCase )
lowercase = pipe(**__lowerCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__lowerCAmelCase )
lowercase = self.pipeline_class.from_pretrained(__lowerCAmelCase )
pipe_loaded.to(__lowerCAmelCase )
pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowercase = self.get_dummy_inputs(__lowerCAmelCase )
lowercase = pipe_loaded(**__lowerCAmelCase )[0]
lowercase = np.abs(to_np(__lowerCAmelCase ) - to_np(__lowerCAmelCase ) ).max()
self.assertLess(__lowerCAmelCase , 1E-4 )
| 197
| 1
|
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def UpperCAmelCase_ ( _lowercase : List[str] , _lowercase : List[Any] , _lowercase : Dict ) -> str:
if isinstance(lowerCAmelCase_ , torch.Tensor ):
return image
elif isinstance(lowerCAmelCase_ , PIL.Image.Image ):
SCREAMING_SNAKE_CASE_ = [image]
if isinstance(image[0] , PIL.Image.Image ):
SCREAMING_SNAKE_CASE_ = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
SCREAMING_SNAKE_CASE_ = np.concatenate(lowerCAmelCase_ , axis=0 )
SCREAMING_SNAKE_CASE_ = np.array(lowerCAmelCase_ ).astype(np.floataa ) / 2_5_5.0
SCREAMING_SNAKE_CASE_ = image.transpose(0 , 3 , 1 , 2 )
SCREAMING_SNAKE_CASE_ = 2.0 * image - 1.0
SCREAMING_SNAKE_CASE_ = torch.from_numpy(lowerCAmelCase_ )
elif isinstance(image[0] , torch.Tensor ):
SCREAMING_SNAKE_CASE_ = torch.cat(lowerCAmelCase_ , dim=0 )
return image
def UpperCAmelCase_ ( _lowercase : str , _lowercase : List[str] , _lowercase : Tuple , _lowercase : Optional[int]=0.9_9_9_5 ) -> Optional[int]:
if not isinstance(lowerCAmelCase_ , np.ndarray ):
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = va.device
SCREAMING_SNAKE_CASE_ = va.cpu().numpy()
SCREAMING_SNAKE_CASE_ = va.cpu().numpy()
SCREAMING_SNAKE_CASE_ = np.sum(va * va / (np.linalg.norm(lowerCAmelCase_ ) * np.linalg.norm(lowerCAmelCase_ )) )
if np.abs(lowerCAmelCase_ ) > DOT_THRESHOLD:
SCREAMING_SNAKE_CASE_ = (1 - t) * va + t * va
else:
SCREAMING_SNAKE_CASE_ = np.arccos(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ = np.sin(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ = theta_a * t
SCREAMING_SNAKE_CASE_ = np.sin(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ = np.sin(theta_a - theta_t ) / sin_theta_a
SCREAMING_SNAKE_CASE_ = sin_theta_t / sin_theta_a
SCREAMING_SNAKE_CASE_ = sa * va + sa * va
if inputs_are_torch:
SCREAMING_SNAKE_CASE_ = torch.from_numpy(lowerCAmelCase_ ).to(lowerCAmelCase_ )
return va
def UpperCAmelCase_ ( _lowercase : Any , _lowercase : List[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = F.normalize(lowerCAmelCase_ , dim=-1 )
SCREAMING_SNAKE_CASE_ = F.normalize(lowerCAmelCase_ , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def UpperCAmelCase_ ( _lowercase : int , _lowercase : Optional[Any] ) -> Union[str, Any]:
for param in model.parameters():
SCREAMING_SNAKE_CASE_ = value
class lowerCamelCase_ ( _UpperCamelCase ):
'''simple docstring'''
def __init__( self : Tuple , _lowerCAmelCase : AutoencoderKL , _lowerCAmelCase : CLIPTextModel , _lowerCAmelCase : CLIPModel , _lowerCAmelCase : CLIPTokenizer , _lowerCAmelCase : UNetaDConditionModel , _lowerCAmelCase : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , _lowerCAmelCase : CLIPFeatureExtractor , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : str=None , _lowerCAmelCase : List[Any]=None , ):
super().__init__()
self.register_modules(
vae=_UpperCAmelCase , text_encoder=_UpperCAmelCase , clip_model=_UpperCAmelCase , tokenizer=_UpperCAmelCase , unet=_UpperCAmelCase , scheduler=_UpperCAmelCase , feature_extractor=_UpperCAmelCase , coca_model=_UpperCAmelCase , coca_tokenizer=_UpperCAmelCase , coca_transform=_UpperCAmelCase , )
SCREAMING_SNAKE_CASE_ = (
feature_extractor.size
if isinstance(feature_extractor.size , _UpperCAmelCase )
else feature_extractor.size['shortest_edge']
)
SCREAMING_SNAKE_CASE_ = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , _UpperCAmelCase )
set_requires_grad(self.clip_model , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Tuple , _lowerCAmelCase : Optional[Union[str, int]] = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
SCREAMING_SNAKE_CASE_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_UpperCAmelCase )
def lowerCAmelCase_ ( self : int ):
self.enable_attention_slicing(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
set_requires_grad(self.vae , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Any ):
set_requires_grad(self.vae , _UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] ):
set_requires_grad(self.unet , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
set_requires_grad(self.unet , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Dict ):
# get the original timestep using init_timestep
SCREAMING_SNAKE_CASE_ = min(int(num_inference_steps * strength ) , _UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = max(num_inference_steps - init_timestep , 0 )
SCREAMING_SNAKE_CASE_ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowerCAmelCase_ ( self : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Dict , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int]=None ):
if not isinstance(_UpperCAmelCase , torch.Tensor ):
raise ValueError(F"`image` has to be of type `torch.Tensor` but is {type(_UpperCAmelCase )}" )
SCREAMING_SNAKE_CASE_ = image.to(device=_UpperCAmelCase , dtype=_UpperCAmelCase )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(_UpperCAmelCase )
]
SCREAMING_SNAKE_CASE_ = torch.cat(_UpperCAmelCase , dim=0 )
else:
SCREAMING_SNAKE_CASE_ = self.vae.encode(_UpperCAmelCase ).latent_dist.sample(_UpperCAmelCase )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
SCREAMING_SNAKE_CASE_ = 0.1_8215 * init_latents
SCREAMING_SNAKE_CASE_ = init_latents.repeat_interleave(_UpperCAmelCase , dim=0 )
SCREAMING_SNAKE_CASE_ = randn_tensor(init_latents.shape , generator=_UpperCAmelCase , device=_UpperCAmelCase , dtype=_UpperCAmelCase )
# get latents
SCREAMING_SNAKE_CASE_ = self.scheduler.add_noise(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = init_latents
return latents
def lowerCAmelCase_ ( self : int , _lowerCAmelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE_ = self.coca_transform(_UpperCAmelCase ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
SCREAMING_SNAKE_CASE_ = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
SCREAMING_SNAKE_CASE_ = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('<end_of_text>' )[0].replace('<start_of_text>' , '' ).rstrip(' .,' )
def lowerCAmelCase_ ( self : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : List[str] ):
SCREAMING_SNAKE_CASE_ = self.feature_extractor.preprocess(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = torch.from_numpy(clip_image_input['pixel_values'][0] ).unsqueeze(0 ).to(self.device ).half()
SCREAMING_SNAKE_CASE_ = self.clip_model.get_image_features(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=_UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = image_embeddings_clip.repeat_interleave(_UpperCAmelCase , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def lowerCAmelCase_ ( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any , ):
SCREAMING_SNAKE_CASE_ = latents.detach().requires_grad_()
SCREAMING_SNAKE_CASE_ = self.scheduler.scale_model_input(_UpperCAmelCase , _UpperCAmelCase )
# predict the noise residual
SCREAMING_SNAKE_CASE_ = self.unet(_UpperCAmelCase , _UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
SCREAMING_SNAKE_CASE_ = self.scheduler.alphas_cumprod[timestep]
SCREAMING_SNAKE_CASE_ = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
SCREAMING_SNAKE_CASE_ = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
SCREAMING_SNAKE_CASE_ = torch.sqrt(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ = self.scheduler.sigmas[index]
SCREAMING_SNAKE_CASE_ = latents - sigma * noise_pred
else:
raise ValueError(F"scheduler type {type(self.scheduler )} not supported" )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
SCREAMING_SNAKE_CASE_ = 1 / 0.1_8215 * sample
SCREAMING_SNAKE_CASE_ = self.vae.decode(_UpperCAmelCase ).sample
SCREAMING_SNAKE_CASE_ = (image / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE_ = transforms.Resize(self.feature_extractor_size )(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = self.normalize(_UpperCAmelCase ).to(latents.dtype )
SCREAMING_SNAKE_CASE_ = self.clip_model.get_image_features(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=_UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = spherical_dist_loss(_UpperCAmelCase , _UpperCAmelCase ).mean() * clip_guidance_scale
SCREAMING_SNAKE_CASE_ = -torch.autograd.grad(_UpperCAmelCase , _UpperCAmelCase )[0]
if isinstance(self.scheduler , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ = latents.detach() + grads * (sigma**2)
SCREAMING_SNAKE_CASE_ = noise_pred_original
else:
SCREAMING_SNAKE_CASE_ = noise_pred_original - torch.sqrt(_UpperCAmelCase ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self : Union[str, Any] , _lowerCAmelCase : Union[torch.FloatTensor, PIL.Image.Image] , _lowerCAmelCase : Union[torch.FloatTensor, PIL.Image.Image] , _lowerCAmelCase : Optional[str] = None , _lowerCAmelCase : Optional[str] = None , _lowerCAmelCase : Optional[int] = 512 , _lowerCAmelCase : Optional[int] = 512 , _lowerCAmelCase : float = 0.6 , _lowerCAmelCase : Optional[int] = 50 , _lowerCAmelCase : Optional[float] = 7.5 , _lowerCAmelCase : Optional[int] = 1 , _lowerCAmelCase : float = 0.0 , _lowerCAmelCase : Optional[float] = 100 , _lowerCAmelCase : Optional[torch.Generator] = None , _lowerCAmelCase : Optional[str] = "pil" , _lowerCAmelCase : bool = True , _lowerCAmelCase : float = 0.8 , _lowerCAmelCase : float = 0.1 , _lowerCAmelCase : float = 0.1 , ):
if isinstance(_UpperCAmelCase , _UpperCAmelCase ) and len(_UpperCAmelCase ) != batch_size:
raise ValueError(F"You have passed {batch_size} batch_size, but only {len(_UpperCAmelCase )} generators." )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}." )
if isinstance(_UpperCAmelCase , torch.Generator ) and batch_size > 1:
SCREAMING_SNAKE_CASE_ = [generator] + [None] * (batch_size - 1)
SCREAMING_SNAKE_CASE_ = [
('model', self.coca_model is None),
('tokenizer', self.coca_tokenizer is None),
('transform', self.coca_transform is None),
]
SCREAMING_SNAKE_CASE_ = [x[0] for x in coca_is_none if x[1]]
SCREAMING_SNAKE_CASE_ = ', '.join(_UpperCAmelCase )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(_UpperCAmelCase ):
raise ValueError(
F"Content prompt is None and CoCa [{coca_is_none_str}] is None."
F"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
SCREAMING_SNAKE_CASE_ = self.get_image_description(_UpperCAmelCase )
if style_prompt is None:
if len(_UpperCAmelCase ):
raise ValueError(
F"Style prompt is None and CoCa [{coca_is_none_str}] is None."
F" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
SCREAMING_SNAKE_CASE_ = self.get_image_description(_UpperCAmelCase )
# get prompt text embeddings for content and style
SCREAMING_SNAKE_CASE_ = self.tokenizer(
_UpperCAmelCase , padding='max_length' , max_length=self.tokenizer.model_max_length , truncation=_UpperCAmelCase , return_tensors='pt' , )
SCREAMING_SNAKE_CASE_ = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
SCREAMING_SNAKE_CASE_ = self.tokenizer(
_UpperCAmelCase , padding='max_length' , max_length=self.tokenizer.model_max_length , truncation=_UpperCAmelCase , return_tensors='pt' , )
SCREAMING_SNAKE_CASE_ = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
SCREAMING_SNAKE_CASE_ = slerp(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# duplicate text embeddings for each generation per prompt
SCREAMING_SNAKE_CASE_ = text_embeddings.repeat_interleave(_UpperCAmelCase , dim=0 )
# set timesteps
SCREAMING_SNAKE_CASE_ = 'offset' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
SCREAMING_SNAKE_CASE_ = {}
if accepts_offset:
SCREAMING_SNAKE_CASE_ = 1
self.scheduler.set_timesteps(_UpperCAmelCase , **_UpperCAmelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
SCREAMING_SNAKE_CASE_ = self.get_timesteps(_UpperCAmelCase , _UpperCAmelCase , self.device )
SCREAMING_SNAKE_CASE_ = timesteps[:1].repeat(_UpperCAmelCase )
# Preprocess image
SCREAMING_SNAKE_CASE_ = preprocess(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = self.prepare_latents(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , text_embeddings.dtype , self.device , _UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = preprocess(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = self.prepare_latents(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , text_embeddings.dtype , self.device , _UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = slerp(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if clip_guidance_scale > 0:
SCREAMING_SNAKE_CASE_ = self.get_clip_image_embeddings(_UpperCAmelCase , _UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = self.get_clip_image_embeddings(_UpperCAmelCase , _UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = slerp(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
SCREAMING_SNAKE_CASE_ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE_ = content_text_input.input_ids.shape[-1]
SCREAMING_SNAKE_CASE_ = self.tokenizer([''] , padding='max_length' , max_length=_UpperCAmelCase , return_tensors='pt' )
SCREAMING_SNAKE_CASE_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
SCREAMING_SNAKE_CASE_ = uncond_embeddings.repeat_interleave(_UpperCAmelCase , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
SCREAMING_SNAKE_CASE_ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
SCREAMING_SNAKE_CASE_ = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
SCREAMING_SNAKE_CASE_ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
SCREAMING_SNAKE_CASE_ = torch.randn(_UpperCAmelCase , generator=_UpperCAmelCase , device='cpu' , dtype=_UpperCAmelCase ).to(
self.device )
else:
SCREAMING_SNAKE_CASE_ = torch.randn(_UpperCAmelCase , generator=_UpperCAmelCase , device=self.device , dtype=_UpperCAmelCase )
else:
if latents.shape != latents_shape:
raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
SCREAMING_SNAKE_CASE_ = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
SCREAMING_SNAKE_CASE_ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
SCREAMING_SNAKE_CASE_ = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
SCREAMING_SNAKE_CASE_ = {}
if accepts_eta:
SCREAMING_SNAKE_CASE_ = eta
# check if the scheduler accepts generator
SCREAMING_SNAKE_CASE_ = 'generator' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
SCREAMING_SNAKE_CASE_ = generator
with self.progress_bar(total=_UpperCAmelCase ):
for i, t in enumerate(_UpperCAmelCase ):
# expand the latents if we are doing classifier free guidance
SCREAMING_SNAKE_CASE_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
SCREAMING_SNAKE_CASE_ = self.scheduler.scale_model_input(_UpperCAmelCase , _UpperCAmelCase )
# predict the noise residual
SCREAMING_SNAKE_CASE_ = self.unet(_UpperCAmelCase , _UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE_ = noise_pred.chunk(2 )
SCREAMING_SNAKE_CASE_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
SCREAMING_SNAKE_CASE_ = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
SCREAMING_SNAKE_CASE_ = self.cond_fn(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE_ = self.scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
SCREAMING_SNAKE_CASE_ = 1 / 0.1_8215 * latents
SCREAMING_SNAKE_CASE_ = self.vae.decode(_UpperCAmelCase ).sample
SCREAMING_SNAKE_CASE_ = (image / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE_ = self.numpy_to_pil(_UpperCAmelCase )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=_UpperCAmelCase , nsfw_content_detected=_UpperCAmelCase )
| 369
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Dict = logging.get_logger(__name__)
lowerCamelCase__ : Any = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "speech_to_text"
lowercase_ = ["past_key_values"]
lowercase_ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Union[str, Any] , _lowerCAmelCase : Optional[int]=10_000 , _lowerCAmelCase : str=12 , _lowerCAmelCase : Tuple=2_048 , _lowerCAmelCase : str=4 , _lowerCAmelCase : Tuple=6 , _lowerCAmelCase : Optional[int]=2_048 , _lowerCAmelCase : Dict=4 , _lowerCAmelCase : List[Any]=0.0 , _lowerCAmelCase : int=0.0 , _lowerCAmelCase : Optional[int]=True , _lowerCAmelCase : Union[str, Any]=True , _lowerCAmelCase : Any="relu" , _lowerCAmelCase : Any=256 , _lowerCAmelCase : List[str]=0.1 , _lowerCAmelCase : Optional[Any]=0.0 , _lowerCAmelCase : Optional[int]=0.0 , _lowerCAmelCase : Tuple=0.02 , _lowerCAmelCase : List[str]=2 , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : List[str]=1 , _lowerCAmelCase : Dict=0 , _lowerCAmelCase : Tuple=2 , _lowerCAmelCase : Optional[int]=6_000 , _lowerCAmelCase : Tuple=1_024 , _lowerCAmelCase : Any=2 , _lowerCAmelCase : str=(5, 5) , _lowerCAmelCase : Optional[int]=1_024 , _lowerCAmelCase : List[Any]=80 , _lowerCAmelCase : List[Any]=1 , **_lowerCAmelCase : List[Any] , ):
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE_ = max_source_positions
SCREAMING_SNAKE_CASE_ = max_target_positions
SCREAMING_SNAKE_CASE_ = num_conv_layers
SCREAMING_SNAKE_CASE_ = list(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = conv_channels
SCREAMING_SNAKE_CASE_ = input_feat_per_channel
SCREAMING_SNAKE_CASE_ = input_channels
if len(self.conv_kernel_sizes ) != self.num_conv_layers:
raise ValueError(
'Configuration for convolutional module is incorrect. '
'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
F"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes )}`, "
F"`config.num_conv_layers = {self.num_conv_layers}`." )
super().__init__(
pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , is_encoder_decoder=_lowerCAmelCase , decoder_start_token_id=_lowerCAmelCase , **_lowerCAmelCase , )
| 210
| 0
|
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class __lowerCAmelCase ( unittest.TestCase ):
UpperCamelCase__ = MODEL_FOR_CAUSAL_LM_MAPPING
UpperCamelCase__ = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
a = pipeline(task="""text-generation""" , model="""sshleifer/tiny-ctrl""" , framework="""pt""" )
# Using `do_sample=False` to force deterministic output
a = text_generator("""This is a test""" , do_sample=__magic_name__ )
self.assertEqual(
__magic_name__ , [
{
"""generated_text""": (
"""This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
""" oscope. FiliFili@@"""
)
}
] , )
a = text_generator(["""This is a test""", """This is a second test"""] )
self.assertEqual(
__magic_name__ , [
[
{
"""generated_text""": (
"""This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
""" oscope. FiliFili@@"""
)
}
],
[
{
"""generated_text""": (
"""This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"""
""" oscope. oscope. FiliFili@@"""
)
}
],
] , )
a = text_generator("""This is a test""" , do_sample=__magic_name__ , num_return_sequences=2 , return_tensors=__magic_name__ )
self.assertEqual(
__magic_name__ , [
{"""generated_token_ids""": ANY(__magic_name__ )},
{"""generated_token_ids""": ANY(__magic_name__ )},
] , )
a = text_generator.model.config.eos_token_id
a = """<pad>"""
a = text_generator(
["""This is a test""", """This is a second test"""] , do_sample=__magic_name__ , num_return_sequences=2 , batch_size=2 , return_tensors=__magic_name__ , )
self.assertEqual(
__magic_name__ , [
[
{"""generated_token_ids""": ANY(__magic_name__ )},
{"""generated_token_ids""": ANY(__magic_name__ )},
],
[
{"""generated_token_ids""": ANY(__magic_name__ )},
{"""generated_token_ids""": ANY(__magic_name__ )},
],
] , )
@require_tf
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
a = pipeline(task="""text-generation""" , model="""sshleifer/tiny-ctrl""" , framework="""tf""" )
# Using `do_sample=False` to force deterministic output
a = text_generator("""This is a test""" , do_sample=__magic_name__ )
self.assertEqual(
__magic_name__ , [
{
"""generated_text""": (
"""This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
""" please,"""
)
}
] , )
a = text_generator(["""This is a test""", """This is a second test"""] , do_sample=__magic_name__ )
self.assertEqual(
__magic_name__ , [
[
{
"""generated_text""": (
"""This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
""" please,"""
)
}
],
[
{
"""generated_text""": (
"""This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"""
""" Cannes 閲閲Cannes Cannes Cannes 攵 please,"""
)
}
],
] , )
def lowerCamelCase__ ( self :List[str] , __magic_name__ :str , __magic_name__ :int , __magic_name__ :Any ):
'''simple docstring'''
a = TextGenerationPipeline(model=__magic_name__ , tokenizer=__magic_name__ )
return text_generator, ["This is a test", "Another test"]
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = """Hello I believe in"""
a = pipeline("""text-generation""" , model="""hf-internal-testing/tiny-random-gpt2""" )
a = text_generator(__magic_name__ )
self.assertEqual(
__magic_name__ , [{"""generated_text""": """Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"""}] , )
a = text_generator(__magic_name__ , stop_sequence=""" fe""" )
self.assertEqual(__magic_name__ , [{"""generated_text""": """Hello I believe in fe"""}] )
def lowerCamelCase__ ( self :Optional[Any] , __magic_name__ :str , __magic_name__ :int ):
'''simple docstring'''
a = text_generator.model
a = text_generator.tokenizer
a = text_generator("""This is a test""" )
self.assertEqual(__magic_name__ , [{"""generated_text""": ANY(__magic_name__ )}] )
self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test""" ) )
a = text_generator("""This is a test""" , return_full_text=__magic_name__ )
self.assertEqual(__magic_name__ , [{"""generated_text""": ANY(__magic_name__ )}] )
self.assertNotIn("""This is a test""" , outputs[0]["""generated_text"""] )
a = pipeline(task="""text-generation""" , model=__magic_name__ , tokenizer=__magic_name__ , return_full_text=__magic_name__ )
a = text_generator("""This is a test""" )
self.assertEqual(__magic_name__ , [{"""generated_text""": ANY(__magic_name__ )}] )
self.assertNotIn("""This is a test""" , outputs[0]["""generated_text"""] )
a = text_generator("""This is a test""" , return_full_text=__magic_name__ )
self.assertEqual(__magic_name__ , [{"""generated_text""": ANY(__magic_name__ )}] )
self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test""" ) )
a = text_generator(["""This is great !""", """Something else"""] , num_return_sequences=2 , do_sample=__magic_name__ )
self.assertEqual(
__magic_name__ , [
[{"""generated_text""": ANY(__magic_name__ )}, {"""generated_text""": ANY(__magic_name__ )}],
[{"""generated_text""": ANY(__magic_name__ )}, {"""generated_text""": ANY(__magic_name__ )}],
] , )
if text_generator.tokenizer.pad_token is not None:
a = text_generator(
["""This is great !""", """Something else"""] , num_return_sequences=2 , batch_size=2 , do_sample=__magic_name__ )
self.assertEqual(
__magic_name__ , [
[{"""generated_text""": ANY(__magic_name__ )}, {"""generated_text""": ANY(__magic_name__ )}],
[{"""generated_text""": ANY(__magic_name__ )}, {"""generated_text""": ANY(__magic_name__ )}],
] , )
with self.assertRaises(__magic_name__ ):
a = text_generator("""test""" , return_full_text=__magic_name__ , return_text=__magic_name__ )
with self.assertRaises(__magic_name__ ):
a = text_generator("""test""" , return_full_text=__magic_name__ , return_tensors=__magic_name__ )
with self.assertRaises(__magic_name__ ):
a = text_generator("""test""" , return_text=__magic_name__ , return_tensors=__magic_name__ )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
a = text_generator("""""" )
self.assertEqual(__magic_name__ , [{"""generated_text""": ANY(__magic_name__ )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
a = text_generator("""""" )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
a = ["""RwkvForCausalLM""", """XGLMForCausalLM""", """GPTNeoXForCausalLM"""]
if (
tokenizer.model_max_length < 1_0000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator("""This is a test""" * 500 , max_new_tokens=20 )
a = text_generator("""This is a test""" * 500 , handle_long_generation="""hole""" , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(__magic_name__ ):
text_generator(
"""This is a test""" * 500 , handle_long_generation="""hole""" , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
import torch
# Classic `model_kwargs`
a = pipeline(
model="""hf-internal-testing/tiny-random-bloom""" , model_kwargs={"""device_map""": """auto""", """torch_dtype""": torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
a = pipe("""This is a test""" )
self.assertEqual(
__magic_name__ , [
{
"""generated_text""": (
"""This is a test test test test test test test test test test test test test test test test"""
""" test"""
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
a = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""" , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
a = pipe("""This is a test""" )
self.assertEqual(
__magic_name__ , [
{
"""generated_text""": (
"""This is a test test test test test test test test test test test test test test test test"""
""" test"""
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
a = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""" )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
a = pipe("""This is a test""" )
self.assertEqual(
__magic_name__ , [
{
"""generated_text""": (
"""This is a test test test test test test test test test test test test test test test test"""
""" test"""
)
}
] , )
@require_torch
@require_torch_gpu
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
import torch
a = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device=0 , torch_dtype=torch.floataa )
pipe("""This is a test""" )
@require_torch
@require_accelerate
@require_torch_gpu
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
import torch
a = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""" , torch_dtype=torch.floataa )
pipe("""This is a test""" , do_sample=__magic_name__ , top_p=0.5 )
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
a = """Hello world"""
a = pipeline("""text-generation""" , model="""hf-internal-testing/tiny-random-gpt2""" )
if text_generator.model.framework == "tf":
a = logging.get_logger("""transformers.generation.tf_utils""" )
else:
a = logging.get_logger("""transformers.generation.utils""" )
a = """Both `max_new_tokens`""" # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(__magic_name__ ) as cl:
a = text_generator(__magic_name__ , max_length=10 , max_new_tokens=1 )
self.assertIn(__magic_name__ , cl.out )
# The user only sets one -> no warning
with CaptureLogger(__magic_name__ ) as cl:
a = text_generator(__magic_name__ , max_new_tokens=1 )
self.assertNotIn(__magic_name__ , cl.out )
with CaptureLogger(__magic_name__ ) as cl:
a = text_generator(__magic_name__ , max_length=10 )
self.assertNotIn(__magic_name__ , cl.out )
| 228
|
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase = None ) -> list[list[str]]:
a = word_bank or []
# create a table
a = len(__lowerCamelCase ) + 1
a = []
for _ in range(__lowerCamelCase ):
table.append([] )
# seed value
a = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(__lowerCamelCase ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(__lowerCamelCase )] == word:
a = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(__lowerCamelCase )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(__lowerCamelCase )]:
combination.reverse()
return table[len(__lowerCamelCase )]
if __name__ == "__main__":
print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
print(
all_construct(
"hexagonosaurus",
["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
)
)
| 228
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE :Union[str, Any] = """xglm"""
__SCREAMING_SNAKE_CASE :Union[str, Any] = ["""past_key_values"""]
__SCREAMING_SNAKE_CASE :str = {
"""num_attention_heads""": """attention_heads""",
"""hidden_size""": """d_model""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : Dict , a__ : Optional[int]=25_6008 , a__ : Dict=2048 , a__ : List[Any]=1024 , a__ : Optional[Any]=4096 , a__ : Optional[int]=24 , a__ : str=16 , a__ : int="gelu" , a__ : Any=0.1 , a__ : Optional[int]=0.1 , a__ : str=0.0 , a__ : Union[str, Any]=0.0 , a__ : List[Any]=0.02 , a__ : Tuple=True , a__ : Tuple=True , a__ : Dict=2 , a__ : List[str]=1 , a__ : str=0 , a__ : List[str]=2 , **a__ : List[str] , ):
__magic_name__ = vocab_size
__magic_name__ = max_position_embeddings
__magic_name__ = d_model
__magic_name__ = ffn_dim
__magic_name__ = num_layers
__magic_name__ = attention_heads
__magic_name__ = activation_function
__magic_name__ = dropout
__magic_name__ = attention_dropout
__magic_name__ = activation_dropout
__magic_name__ = layerdrop
__magic_name__ = init_std
__magic_name__ = scale_embedding # scale factor will be sqrt(d_model) if True
__magic_name__ = use_cache
super().__init__(
pad_token_id=a__ , bos_token_id=a__ , eos_token_id=a__ , decoder_start_token_id=a__ , **a__ , )
| 355
|
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object()
def UpperCamelCase ( a , a ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ = tuple((re.compile(x + '''$''' ) for x in qs) )
for i in range(len(a ) - len(a ) + 1 ):
__magic_name__ = [x.match(a ) for x, y in zip(a , ks[i:] )]
if matches and all(a ):
return True
return False
def UpperCamelCase ( a ) -> Tuple:
'''simple docstring'''
def replace(a , a ):
for rule, replacement in rules:
if _match(a , a ):
return replacement
return val
return replace
def UpperCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
return [
# embeddings
(("transformer", "wpe", "embedding"), P('''mp''' , a )),
(("transformer", "wte", "embedding"), P('''mp''' , a )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(a , '''mp''' )),
(("attention", "out_proj", "kernel"), P('''mp''' , a )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(a , '''mp''' )),
(("mlp", "c_fc", "bias"), P('''mp''' )),
(("mlp", "c_proj", "kernel"), P('''mp''' , a )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def UpperCamelCase ( a ) -> str:
'''simple docstring'''
__magic_name__ = _get_partition_rules()
__magic_name__ = _replacement_rules(a )
__magic_name__ = {k: _unmatched for k in flatten_dict(a )}
__magic_name__ = {k: replace(a , a ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(a ) )
| 98
| 0
|
'''simple docstring'''
import os
import sys
a__ : Tuple = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
a__ : Optional[int] = [
'torch',
'numpy',
'tokenizers',
'filelock',
'requests',
'tqdm',
'regex',
'sentencepiece',
'sacremoses',
'importlib_metadata',
'huggingface_hub',
]
@add_start_docstrings(AutoConfig.__doc__ )
def _UpperCamelCase ( *__A , **__A ) -> str:
'''simple docstring'''
return AutoConfig.from_pretrained(*__A , **__A )
@add_start_docstrings(AutoTokenizer.__doc__ )
def _UpperCamelCase ( *__A , **__A ) -> List[str]:
'''simple docstring'''
return AutoTokenizer.from_pretrained(*__A , **__A )
@add_start_docstrings(AutoModel.__doc__ )
def _UpperCamelCase ( *__A , **__A ) -> Dict:
'''simple docstring'''
return AutoModel.from_pretrained(*__A , **__A )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def _UpperCamelCase ( *__A , **__A ) -> List[Any]:
'''simple docstring'''
return AutoModelForCausalLM.from_pretrained(*__A , **__A )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def _UpperCamelCase ( *__A , **__A ) -> str:
'''simple docstring'''
return AutoModelForMaskedLM.from_pretrained(*__A , **__A )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def _UpperCamelCase ( *__A , **__A ) -> Tuple:
'''simple docstring'''
return AutoModelForSequenceClassification.from_pretrained(*__A , **__A )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def _UpperCamelCase ( *__A , **__A ) -> Dict:
'''simple docstring'''
return AutoModelForQuestionAnswering.from_pretrained(*__A , **__A )
| 80
|
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __A , __A , __A ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise ValueError("Resistance cannot be negative" )
if voltage == 0:
return {"voltage": float(current * resistance )}
elif current == 0:
return {"current": voltage / resistance}
elif resistance == 0:
return {"resistance": voltage / current}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80
| 1
|
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __a ( UpperCAmelCase ):
_a : Optional[int] = 'MCTCTFeatureExtractor'
_a : int = 'AutoTokenizer'
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.feature_extractor
_UpperCAmelCase = False
def __call__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
_UpperCAmelCase = kwargs.pop('raw_speech' )
else:
_UpperCAmelCase = kwargs.pop('audio' , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = kwargs.pop('sampling_rate' , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = kwargs.pop('text' , _SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
_UpperCAmelCase = args[0]
_UpperCAmelCase = args[1:]
if audio is None and text is None:
raise ValueError('You need to specify either an `audio` or `text` input to process.' )
if audio is not None:
_UpperCAmelCase = self.feature_extractor(_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , sampling_rate=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if text is not None:
_UpperCAmelCase = self.tokenizer(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_UpperCAmelCase = encodings['input_ids']
return inputs
def UpperCAmelCase__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor.pad(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = kwargs.pop('input_features' , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = kwargs.pop('labels' , _SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
_UpperCAmelCase = args[0]
_UpperCAmelCase = args[1:]
if input_features is not None:
_UpperCAmelCase = self.feature_extractor.pad(_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if labels is not None:
_UpperCAmelCase = self.tokenizer.pad(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
_UpperCAmelCase = labels['input_ids']
return input_features
def UpperCAmelCase__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@contextmanager
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your audio inputs, or in a separate call.' )
_UpperCAmelCase = True
_UpperCAmelCase = self.tokenizer
yield
_UpperCAmelCase = self.feature_extractor
_UpperCAmelCase = False
| 362
|
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def lowerCAmelCase__ ( a__: str , a__: List[Any] ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
_UpperCAmelCase = Image.open(requests.get(a__ , stream=a__ ).raw ).convert('RGB' )
_UpperCAmelCase = transforms.Compose(
[
transforms.Resize((image_size, image_size) , interpolation=InterpolationMode.BICUBIC ),
transforms.ToTensor(),
transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073) , (0.26_862_954, 0.26_130_258, 0.27_577_711) ),
] )
_UpperCAmelCase = transform(a__ ).unsqueeze(0 ).to(a__ )
return image
def lowerCAmelCase__ ( a__: Optional[int] ) -> int:
'''simple docstring'''
if "visual_encoder" in key:
_UpperCAmelCase = re.sub('visual_encoder*' , 'vision_model.encoder' , a__ )
if "blocks" in key:
_UpperCAmelCase = re.sub(R'blocks' , 'layers' , a__ )
if "attn" in key:
_UpperCAmelCase = re.sub(R'attn' , 'self_attn' , a__ )
if "norm1" in key:
_UpperCAmelCase = re.sub(R'norm1' , 'layer_norm1' , a__ )
if "norm2" in key:
_UpperCAmelCase = re.sub(R'norm2' , 'layer_norm2' , a__ )
if "encoder.norm" in key:
_UpperCAmelCase = re.sub(R'encoder.norm' , 'post_layernorm' , a__ )
if "encoder.patch_embed.proj" in key:
_UpperCAmelCase = re.sub(R'encoder.patch_embed.proj' , 'embeddings.patch_embedding' , a__ )
if "encoder.pos_embed" in key:
_UpperCAmelCase = re.sub(R'encoder.pos_embed' , 'embeddings.position_embedding' , a__ )
if "encoder.cls_token" in key:
_UpperCAmelCase = re.sub(R'encoder.cls_token' , 'embeddings.class_embedding' , a__ )
if "self_attn" in key:
_UpperCAmelCase = re.sub(R'self_attn.proj' , 'self_attn.projection' , a__ )
return key
@torch.no_grad()
def lowerCAmelCase__ ( a__: Optional[Any] , a__: List[str]=None ) -> Optional[Any]:
'''simple docstring'''
if config_path is not None:
_UpperCAmelCase = BlipConfig.from_pretrained(a__ )
else:
_UpperCAmelCase = BlipConfig(projection_dim=5_1_2 , text_config={} , vision_config={} )
_UpperCAmelCase = BlipForConditionalGeneration(a__ ).eval()
_UpperCAmelCase = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'
_UpperCAmelCase = blip_decoder(pretrained=a__ , image_size=3_8_4 , vit='base' )
_UpperCAmelCase = pt_model.eval()
_UpperCAmelCase = pt_model.state_dict()
for key in modified_state_dict.copy():
_UpperCAmelCase = modified_state_dict.pop(a__ )
_UpperCAmelCase = rename_key(a__ )
_UpperCAmelCase = value
hf_model.load_state_dict(a__ )
_UpperCAmelCase = 3_8_4
_UpperCAmelCase = load_demo_image(image_size=a__ , device='cpu' )
_UpperCAmelCase = BertTokenizer.from_pretrained('bert-base-uncased' )
_UpperCAmelCase = tokenizer(['a picture of'] ).input_ids
_UpperCAmelCase = hf_model.generate(a__ , a__ )
assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]
_UpperCAmelCase = hf_model.generate(a__ )
assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]
if pytorch_dump_folder_path is not None:
hf_model.save_pretrained(a__ )
# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
_UpperCAmelCase = (
'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
)
_UpperCAmelCase = blip_vqa(pretrained=a__ , image_size=a__ , vit='base' )
vqa_model.eval()
_UpperCAmelCase = vqa_model.state_dict()
for key in modified_state_dict.copy():
_UpperCAmelCase = modified_state_dict.pop(a__ )
_UpperCAmelCase = rename_key(a__ )
_UpperCAmelCase = value
_UpperCAmelCase = BlipForQuestionAnswering(a__ )
hf_vqa_model.load_state_dict(a__ )
_UpperCAmelCase = ['How many dogs are in this image?']
_UpperCAmelCase = tokenizer(a__ , return_tensors='pt' ).input_ids
_UpperCAmelCase = hf_vqa_model.generate(a__ , a__ )
print(tokenizer.decode(answer[0] ) )
assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa' )
_UpperCAmelCase = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'
_UpperCAmelCase = blip_itm(pretrained=a__ , image_size=a__ , vit='base' )
itm_model.eval()
_UpperCAmelCase = itm_model.state_dict()
for key in modified_state_dict.copy():
_UpperCAmelCase = modified_state_dict.pop(a__ )
_UpperCAmelCase = rename_key(a__ )
_UpperCAmelCase = value
_UpperCAmelCase = BlipForImageTextRetrieval(a__ )
_UpperCAmelCase = ['A picture of a woman with a dog sitting in a beach']
_UpperCAmelCase = tokenizer(
a__ , return_tensors='pt' , padding='max_length' , truncation=a__ , max_length=3_5 , ).input_ids
hf_itm_model.load_state_dict(a__ )
hf_itm_model.eval()
_UpperCAmelCase = hf_itm_model(a__ , a__ , use_itm_head=a__ )
_UpperCAmelCase = hf_itm_model(a__ , a__ , use_itm_head=a__ )
assert out[0].item() == 0.2_110_687_494_277_954
assert torch.nn.functional.softmax(out_itm[0] , dim=1 )[:, 1].item() == 0.45_698_845_386_505_127
if pytorch_dump_folder_path is not None:
hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm' )
if __name__ == "__main__":
lowerCAmelCase__ :int = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
lowerCAmelCase__ :List[Any] = parser.parse_args()
convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 185
| 0
|
"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a__ : int = 6_37_81_37.0
a__ : Union[str, Any] = 6_35_67_52.31_42_45
a__ : Dict = 6_3_7_8_1_3_7
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
__SCREAMING_SNAKE_CASE = atan((1 - flattening) * tan(radians(lowerCAmelCase_ ) ) )
__SCREAMING_SNAKE_CASE = atan((1 - flattening) * tan(radians(lowerCAmelCase_ ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
__SCREAMING_SNAKE_CASE = haversine_distance(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
__SCREAMING_SNAKE_CASE = (b_lata + b_lata) / 2
__SCREAMING_SNAKE_CASE = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
__SCREAMING_SNAKE_CASE = (sin(lowerCAmelCase_ ) ** 2) * (cos(lowerCAmelCase_ ) ** 2)
__SCREAMING_SNAKE_CASE = cos(sigma / 2 ) ** 2
__SCREAMING_SNAKE_CASE = (sigma - sin(lowerCAmelCase_ )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
__SCREAMING_SNAKE_CASE = (cos(lowerCAmelCase_ ) ** 2) * (sin(lowerCAmelCase_ ) ** 2)
__SCREAMING_SNAKE_CASE = sin(sigma / 2 ) ** 2
__SCREAMING_SNAKE_CASE = (sigma + sin(lowerCAmelCase_ )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 54
|
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 lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Any ):
SCREAMING_SNAKE_CASE_ = 0
def lowerCAmelCase_ ( self : int ):
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32' )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE_ = Path(_lowerCAmelCase ) / 'preprocessor_config.json'
SCREAMING_SNAKE_CASE_ = Path(_lowerCAmelCase ) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_lowerCAmelCase , 'w' ) , )
json.dump({'model_type': 'clip'} , open(_lowerCAmelCase , 'w' ) )
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : str ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE_ = Path(_lowerCAmelCase ) / 'preprocessor_config.json'
SCREAMING_SNAKE_CASE_ = Path(_lowerCAmelCase ) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(_lowerCAmelCase , 'w' ) , )
json.dump({'model_type': 'clip'} , open(_lowerCAmelCase , 'w' ) )
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE_ = CLIPConfig()
# Create a dummy config file with image_proceesor_type
SCREAMING_SNAKE_CASE_ = Path(_lowerCAmelCase ) / 'preprocessor_config.json'
SCREAMING_SNAKE_CASE_ = Path(_lowerCAmelCase ) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_lowerCAmelCase , 'w' ) , )
json.dump({'model_type': 'clip'} , open(_lowerCAmelCase , 'w' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(_lowerCAmelCase ).to_dict()
config_dict.pop('image_processor_type' )
SCREAMING_SNAKE_CASE_ = CLIPImageProcessor(**_lowerCAmelCase )
# save in new folder
model_config.save_pretrained(_lowerCAmelCase )
config.save_pretrained(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(_lowerCAmelCase )
# make sure private variable is not incorrectly saved
SCREAMING_SNAKE_CASE_ = json.loads(config.to_json_string() )
self.assertTrue('_processor_class' not in dict_as_saved )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : str ):
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE_ = Path(_lowerCAmelCase ) / 'preprocessor_config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_lowerCAmelCase , 'w' ) , )
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : List[Any] ):
with self.assertRaisesRegex(
_lowerCAmelCase , 'clip-base is not a local folder and is not a valid model identifier' ):
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained('clip-base' )
def lowerCAmelCase_ ( self : List[Any] ):
with self.assertRaisesRegex(
_lowerCAmelCase , R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(_lowerCAmelCase , revision='aaaaaa' )
def lowerCAmelCase_ ( self : str ):
with self.assertRaisesRegex(
_lowerCAmelCase , 'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.' , ):
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model' )
def lowerCAmelCase_ ( self : Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_lowerCAmelCase )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(_lowerCAmelCase , trust_remote_code=_lowerCAmelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , 'NewImageProcessor' )
def lowerCAmelCase_ ( self : Optional[Any] ):
try:
AutoConfig.register('custom' , _lowerCAmelCase )
AutoImageProcessor.register(_lowerCAmelCase , _lowerCAmelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCAmelCase ):
AutoImageProcessor.register(_lowerCAmelCase , _lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE_ = Path(_lowerCAmelCase ) / 'preprocessor_config.json'
SCREAMING_SNAKE_CASE_ = Path(_lowerCAmelCase ) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(_lowerCAmelCase , 'w' ) , )
json.dump({'model_type': 'clip'} , open(_lowerCAmelCase , 'w' ) )
SCREAMING_SNAKE_CASE_ = CustomImageProcessor.from_pretrained(_lowerCAmelCase )
# 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(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(_lowerCAmelCase )
self.assertIsInstance(_lowerCAmelCase , _lowerCAmelCase )
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 lowerCAmelCase_ ( self : Union[str, Any] ):
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = True
try:
AutoConfig.register('custom' , _lowerCAmelCase )
AutoImageProcessor.register(_lowerCAmelCase , _lowerCAmelCase )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE_ = 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.
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_lowerCAmelCase )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_lowerCAmelCase )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
self.assertTrue(not hasattr(_lowerCAmelCase , '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]
| 225
| 0
|
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCAmelCase__ = logging.getLogger(__name__)
class lowercase_ :
'''simple docstring'''
def __init__( self : str ) ->Any:
"""simple docstring"""
a = False
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[str] ) ->List[str]:
"""simple docstring"""
if not self.initialized:
a = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=__UpperCAmelCase , generator_tokenizer=__UpperCAmelCase , index=__UpperCAmelCase , init_retrieval=__UpperCAmelCase , )
a = True
def __lowerCAmelCase ( self : Any ) ->Tuple:
"""simple docstring"""
self.retriever.index.init_index()
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : str , __UpperCAmelCase : Tuple ) ->List[str]:
"""simple docstring"""
a , a = self.retriever._main_retrieve(__UpperCAmelCase , __UpperCAmelCase )
return doc_ids, retrieved_doc_embeds
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : int , __UpperCAmelCase : Tuple , __UpperCAmelCase : Tuple , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict=None ) ->Optional[Any]:
"""simple docstring"""
if index is not None and index.is_initialized() and len(__UpperCAmelCase ) > 0:
raise ValueError(
'''When using Ray for distributed fine-tuning, '''
'''you\'ll need to provide the paths instead, '''
'''as the dataset and the index are loaded '''
'''separately. More info in examples/rag/use_own_knowledge_dataset.py ''' )
super().__init__(
__UpperCAmelCase , question_encoder_tokenizer=__UpperCAmelCase , generator_tokenizer=__UpperCAmelCase , index=__UpperCAmelCase , init_retrieval=__UpperCAmelCase , )
a = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
for worker in self.retrieval_workers
] )
def __lowerCAmelCase ( self : List[Any] ) ->Tuple:
"""simple docstring"""
logger.info('''initializing retrieval''' )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : Any , __UpperCAmelCase : Tuple ) ->Dict:
"""simple docstring"""
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
a = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
a , a = ray.get(random_worker.retrieve.remote(__UpperCAmelCase , __UpperCAmelCase ) )
else:
a , a = self._main_retrieve(__UpperCAmelCase , __UpperCAmelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__UpperCAmelCase )
@classmethod
def __lowerCAmelCase ( cls : List[str] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Tuple=None , **__UpperCAmelCase : Optional[int] ) ->List[Any]:
"""simple docstring"""
return super(__UpperCAmelCase , cls ).get_tokenizers(__UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )
@classmethod
def __lowerCAmelCase ( cls : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : int ) ->str:
"""simple docstring"""
a = kwargs.pop('''config''' , __UpperCAmelCase ) or RagConfig.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
a = RagTokenizer.from_pretrained(__UpperCAmelCase , config=__UpperCAmelCase )
a = rag_tokenizer.question_encoder
a = rag_tokenizer.generator
if indexed_dataset is not None:
a = '''custom'''
a = CustomHFIndex(config.retrieval_vector_size , __UpperCAmelCase )
else:
a = cls._build_index(__UpperCAmelCase )
return cls(
__UpperCAmelCase , question_encoder_tokenizer=__UpperCAmelCase , generator_tokenizer=__UpperCAmelCase , retrieval_workers=__UpperCAmelCase , index=__UpperCAmelCase , )
| 354
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTBigCodeForSequenceClassification",
"GPTBigCodeForTokenClassification",
"GPTBigCodeForCausalLM",
"GPTBigCodeModel",
"GPTBigCodePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 26
| 0
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , *a_ , **a_ ):
'''simple docstring'''
warnings.warn(
'''The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DPTImageProcessor instead.''' , a_ , )
super().__init__(*a_ , **a_ )
| 102
|
from ..utils import DummyObject, requires_backends
class _UpperCamelCase ( metaclass=_UpperCAmelCase ):
"""simple docstring"""
__a : Dict = ['''keras_nlp''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['''keras_nlp'''] )
| 210
| 0
|
from __future__ import annotations
from typing import Any
def __lowerCamelCase ( lowerCAmelCase__ ):
create_state_space_tree(lowerCAmelCase__ , [] , 0 )
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
if index == len(lowerCAmelCase__ ):
print(lowerCAmelCase__ )
return
create_state_space_tree(lowerCAmelCase__ , lowerCAmelCase__ , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(lowerCAmelCase__ , lowerCAmelCase__ , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
lowerCAmelCase__ = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(['A', 'B', 'C'])
generate_all_subsequences(seq)
| 119
|
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 __lowerCamelCase ( ):
lowerCAmelCase__ = 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__ = parser.parse_args()
return args
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
if not len(lowerCAmelCase__ ) == rows * cols:
raise ValueError('The specified number of rows and columns are not correct.' )
lowerCAmelCase__ , lowerCAmelCase__ = imgs[0].size
lowerCAmelCase__ = Image.new('RGB' , size=(cols * w, rows * h) )
lowerCAmelCase__ , lowerCAmelCase__ = grid.size
for i, img in enumerate(lowerCAmelCase__ ):
grid.paste(lowerCAmelCase__ , box=(i % cols * w, i // cols * h) )
return grid
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__="robotic cat with wings" , lowerCAmelCase__=7.5 , lowerCAmelCase__=5_0 , lowerCAmelCase__=1 , lowerCAmelCase__=4_2 , ):
lowerCAmelCase__ = torch.Generator(pipeline.device ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase__ = pipeline(
lowerCAmelCase__ , guidance_scale=lowerCAmelCase__ , num_inference_steps=lowerCAmelCase__ , generator=lowerCAmelCase__ , num_images_per_prompt=lowerCAmelCase__ , ).images
lowerCAmelCase__ = int(math.sqrt(lowerCAmelCase__ ) )
lowerCAmelCase__ = image_grid(lowerCAmelCase__ , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
lowerCAmelCase__ = parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase__ = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
lowerCAmelCase__ = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
lowerCAmelCase__ = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
lowerCAmelCase__ = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')
lowerCAmelCase__ = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase__ = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
lowerCAmelCase__ = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, 'unet', unet)
else:
lowerCAmelCase__ = unet.to(torch.device('cuda', args.cuda_id))
lowerCAmelCase__ = pipeline.to(unet.device)
lowerCAmelCase__ , lowerCAmelCase__ = 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()))))
lowerCAmelCase__ = 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)))
| 119
| 1
|
'''simple docstring'''
import re
import subprocess
import sys
UpperCAmelCase : Tuple = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
UpperCAmelCase : int = subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode('utf-8').split()
UpperCAmelCase : Any = '|'.join(sys.argv[1:])
UpperCAmelCase : List[Any] = re.compile(Rf"""^({joined_dirs}).*?\.py$""")
UpperCAmelCase : Optional[Any] = [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')
| 267
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
snake_case__ = ShapEImgaImgPipeline
snake_case__ = ["image"]
snake_case__ = ["image"]
snake_case__ = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
snake_case__ = False
@property
def __lowerCAmelCase ( self : List[str] ):
return 32
@property
def __lowerCAmelCase ( self : str ):
return 32
@property
def __lowerCAmelCase ( self : int ):
return self.time_input_dim * 4
@property
def __lowerCAmelCase ( self : List[Any] ):
return 8
@property
def __lowerCAmelCase ( self : Optional[int] ):
torch.manual_seed(0 )
UpperCAmelCase__ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size ,image_size=64 ,projection_dim=self.text_embedder_hidden_size ,intermediate_size=37 ,num_attention_heads=4 ,num_channels=3 ,num_hidden_layers=5 ,patch_size=1 ,)
UpperCAmelCase__ = CLIPVisionModel(lowerCamelCase__ )
return model
@property
def __lowerCAmelCase ( self : Optional[Any] ):
UpperCAmelCase__ = CLIPImageProcessor(
crop_size=224 ,do_center_crop=lowerCamelCase__ ,do_normalize=lowerCamelCase__ ,do_resize=lowerCamelCase__ ,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] ,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] ,resample=3 ,size=224 ,)
return image_processor
@property
def __lowerCAmelCase ( self : str ):
torch.manual_seed(0 )
UpperCAmelCase__ = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'embedding_proj_norm_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
UpperCAmelCase__ = PriorTransformer(**lowerCamelCase__ )
return model
@property
def __lowerCAmelCase ( self : Tuple ):
torch.manual_seed(0 )
UpperCAmelCase__ = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
UpperCAmelCase__ = ShapERenderer(**lowerCamelCase__ )
return model
def __lowerCAmelCase ( self : Any ):
UpperCAmelCase__ = self.dummy_prior
UpperCAmelCase__ = self.dummy_image_encoder
UpperCAmelCase__ = self.dummy_image_processor
UpperCAmelCase__ = self.dummy_renderer
UpperCAmelCase__ = HeunDiscreteScheduler(
beta_schedule='exp' ,num_train_timesteps=1_024 ,prediction_type='sample' ,use_karras_sigmas=lowerCamelCase__ ,clip_sample=lowerCamelCase__ ,clip_sample_range=1.0 ,)
UpperCAmelCase__ = {
'prior': prior,
'image_encoder': image_encoder,
'image_processor': image_processor,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def __lowerCAmelCase ( self : Optional[int] ,lowerCamelCase__ : Any ,lowerCamelCase__ : str=0 ):
UpperCAmelCase__ = floats_tensor((1, 3, 64, 64) ,rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
if str(lowerCamelCase__ ).startswith('mps' ):
UpperCAmelCase__ = torch.manual_seed(lowerCamelCase__ )
else:
UpperCAmelCase__ = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
UpperCAmelCase__ = {
'image': input_image,
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def __lowerCAmelCase ( self : Optional[int] ):
UpperCAmelCase__ = 'cpu'
UpperCAmelCase__ = self.get_dummy_components()
UpperCAmelCase__ = self.pipeline_class(**lowerCamelCase__ )
UpperCAmelCase__ = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCAmelCase__ = pipe(**self.get_dummy_inputs(lowerCamelCase__ ) )
UpperCAmelCase__ = output.images[0]
UpperCAmelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
UpperCAmelCase__ = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __lowerCAmelCase ( self : Tuple ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __lowerCAmelCase ( self : Tuple ):
UpperCAmelCase__ = torch_device == 'cpu'
UpperCAmelCase__ = True
self._test_inference_batch_single_identical(
batch_size=2 ,test_max_difference=lowerCamelCase__ ,relax_max_difference=lowerCamelCase__ ,)
def __lowerCAmelCase ( self : List[Any] ):
UpperCAmelCase__ = self.get_dummy_components()
UpperCAmelCase__ = self.pipeline_class(**lowerCamelCase__ )
UpperCAmelCase__ = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCAmelCase__ = 1
UpperCAmelCase__ = 2
UpperCAmelCase__ = self.get_dummy_inputs(lowerCamelCase__ )
for key in inputs.keys():
if key in self.batch_params:
UpperCAmelCase__ = batch_size * [inputs[key]]
UpperCAmelCase__ = pipe(**lowerCamelCase__ ,num_images_per_prompt=lowerCamelCase__ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : int ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self : Any ):
UpperCAmelCase__ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png' )
UpperCAmelCase__ = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_img2img_out.npy' )
UpperCAmelCase__ = ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img' )
UpperCAmelCase__ = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCAmelCase__ = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
UpperCAmelCase__ = pipe(
lowerCamelCase__ ,generator=lowerCamelCase__ ,guidance_scale=3.0 ,num_inference_steps=64 ,frame_size=64 ,output_type='np' ,).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(lowerCamelCase__ ,lowerCamelCase__ )
| 98
| 0
|
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> list[float]:
snake_case_ , snake_case_ = coefficient_matrix.shape
snake_case_ , snake_case_ = constant_matrix.shape
if rowsa != colsa:
snake_case_ = f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
raise ValueError(_SCREAMING_SNAKE_CASE )
if colsa != 1:
snake_case_ = f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
raise ValueError(_SCREAMING_SNAKE_CASE )
if rowsa != rowsa:
snake_case_ = (
"""Coefficient and constant matrices dimensions must be nxn and nx1 but """
f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
)
raise ValueError(_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) != rowsa:
snake_case_ = (
"""Number of initial values must be equal to number of rows in coefficient """
f"""matrix but received {len(_SCREAMING_SNAKE_CASE )} and {rowsa}"""
)
raise ValueError(_SCREAMING_SNAKE_CASE )
if iterations <= 0:
raise ValueError("""Iterations must be at least 1""" )
snake_case_ = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
snake_case_ , snake_case_ = table.shape
strictly_diagonally_dominant(_SCREAMING_SNAKE_CASE )
# Iterates the whole matrix for given number of times
for _ in range(_SCREAMING_SNAKE_CASE ):
snake_case_ = []
for row in range(_SCREAMING_SNAKE_CASE ):
snake_case_ = 0
for col in range(_SCREAMING_SNAKE_CASE ):
if col == row:
snake_case_ = table[row][col]
elif col == cols - 1:
snake_case_ = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
snake_case_ = (temp + val) / denom
new_val.append(_SCREAMING_SNAKE_CASE )
snake_case_ = new_val
return [float(_SCREAMING_SNAKE_CASE ) for i in new_val]
def _a ( _SCREAMING_SNAKE_CASE ) -> bool:
snake_case_ , snake_case_ = table.shape
snake_case_ = True
for i in range(0 , _SCREAMING_SNAKE_CASE ):
snake_case_ = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("""Coefficient matrix is not strictly diagonally dominant""" )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 362
|
"""simple docstring"""
from collections import defaultdict
from math import gcd
def _a ( _SCREAMING_SNAKE_CASE = 1_500_000 ) -> int:
snake_case_ = defaultdict(_SCREAMING_SNAKE_CASE )
snake_case_ = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , _SCREAMING_SNAKE_CASE , 2 ):
if gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) > 1:
continue
snake_case_ = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(_SCREAMING_SNAKE_CASE , limit + 1 , _SCREAMING_SNAKE_CASE ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 233
| 0
|
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def snake_case_ ( A_ : BertModel, A_ : str, A_ : str ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')
_lowerCamelCase : Any = (
('layer.', 'layer_'),
('word_embeddings.weight', 'word_embeddings'),
('position_embeddings.weight', 'position_embeddings'),
('token_type_embeddings.weight', 'token_type_embeddings'),
('.', '/'),
('LayerNorm/weight', 'LayerNorm/gamma'),
('LayerNorm/bias', 'LayerNorm/beta'),
('weight', 'kernel'),
)
if not os.path.isdir(UpperCAmelCase_ ):
os.makedirs(UpperCAmelCase_ )
_lowerCamelCase : str = model.state_dict()
def to_tf_var_name(A_ : str ):
for patt, repl in iter(UpperCAmelCase_ ):
_lowerCamelCase : str = name.replace(UpperCAmelCase_, UpperCAmelCase_ )
return F'''bert/{name}'''
def create_tf_var(A_ : np.ndarray, A_ : str, A_ : tf.Session ):
_lowerCamelCase : str = tf.dtypes.as_dtype(tensor.dtype )
_lowerCamelCase : Dict = tf.get_variable(dtype=UpperCAmelCase_, shape=tensor.shape, name=UpperCAmelCase_, initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(UpperCAmelCase_ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
_lowerCamelCase : List[Any] = to_tf_var_name(UpperCAmelCase_ )
_lowerCamelCase : List[str] = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
_lowerCamelCase : List[str] = torch_tensor.T
_lowerCamelCase : str = create_tf_var(tensor=UpperCAmelCase_, name=UpperCAmelCase_, session=UpperCAmelCase_ )
tf.keras.backend.set_value(UpperCAmelCase_, UpperCAmelCase_ )
_lowerCamelCase : int = session.run(UpperCAmelCase_ )
print(F'''Successfully created {tf_name}: {np.allclose(UpperCAmelCase_, UpperCAmelCase_ )}''' )
_lowerCamelCase : List[str] = tf.train.Saver(tf.trainable_variables() )
saver.save(UpperCAmelCase_, os.path.join(UpperCAmelCase_, model_name.replace('''-''', '''_''' ) + '''.ckpt''' ) )
def snake_case_ ( A_ : List[Any]=None ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--model_name''', type=UpperCAmelCase_, required=UpperCAmelCase_, help='''model name e.g. bert-base-uncased''' )
parser.add_argument(
'''--cache_dir''', type=UpperCAmelCase_, default=UpperCAmelCase_, required=UpperCAmelCase_, help='''Directory containing pytorch model''' )
parser.add_argument('''--pytorch_model_path''', type=UpperCAmelCase_, required=UpperCAmelCase_, help='''/path/to/<pytorch-model-name>.bin''' )
parser.add_argument('''--tf_cache_dir''', type=UpperCAmelCase_, required=UpperCAmelCase_, help='''Directory in which to save tensorflow model''' )
_lowerCamelCase : Optional[int] = parser.parse_args(UpperCAmelCase_ )
_lowerCamelCase : int = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name, state_dict=torch.load(args.pytorch_model_path ), cache_dir=args.cache_dir, )
convert_pytorch_checkpoint_to_tf(model=UpperCAmelCase_, ckpt_dir=args.tf_cache_dir, model_name=args.model_name )
if __name__ == "__main__":
main()
| 72
|
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class UpperCAmelCase_ (_UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class UpperCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> int:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowercase_ ( self ) -> Dict:
__lowerCamelCase : int = ort.SessionOptions()
__lowerCamelCase : Optional[Any] = False
return options
def lowercase_ ( self ) -> Union[str, Any]:
__lowerCamelCase : Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
__lowerCamelCase : Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
__lowerCamelCase : Any = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting' , revision='onnx' , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Tuple = 'A red cat sitting on a park bench'
__lowerCamelCase : Union[str, Any] = np.random.RandomState(0 )
__lowerCamelCase : Union[str, Any] = pipe(
prompt=SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , mask_image=SCREAMING_SNAKE_CASE_ , guidance_scale=7.5 , num_inference_steps=10 , generator=SCREAMING_SNAKE_CASE_ , output_type='np' , )
__lowerCamelCase : Optional[Any] = output.images
__lowerCamelCase : Tuple = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
__lowerCamelCase : Union[str, Any] = np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowercase_ ( self ) -> Optional[Any]:
__lowerCamelCase : Union[str, Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
__lowerCamelCase : List[str] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
__lowerCamelCase : Dict = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-inpainting' , subfolder='scheduler' , revision='onnx' )
__lowerCamelCase : Tuple = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting' , revision='onnx' , scheduler=SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : str = 'A red cat sitting on a park bench'
__lowerCamelCase : Tuple = np.random.RandomState(0 )
__lowerCamelCase : Optional[int] = pipe(
prompt=SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , mask_image=SCREAMING_SNAKE_CASE_ , guidance_scale=7.5 , num_inference_steps=20 , generator=SCREAMING_SNAKE_CASE_ , output_type='np' , )
__lowerCamelCase : List[str] = output.images
__lowerCamelCase : Dict = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
__lowerCamelCase : Tuple = np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 185
| 0
|
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
@register_to_config
def __init__( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ = False , ):
"""simple docstring"""
super().__init__()
lowerCAmelCase : Optional[Any] = nn.Embedding(snake_case__ , snake_case__ )
lowerCAmelCase : int = nn.Embedding(snake_case__ , snake_case__ )
lowerCAmelCase : Any = False
lowerCAmelCase : Optional[Any] = nn.Dropout(p=snake_case__ )
lowerCAmelCase : Optional[Any] = TaConfig(
vocab_size=snake_case__ , d_model=snake_case__ , num_heads=snake_case__ , d_kv=snake_case__ , d_ff=snake_case__ , dropout_rate=snake_case__ , feed_forward_proj=snake_case__ , is_decoder=snake_case__ , is_encoder_decoder=snake_case__ , )
lowerCAmelCase : str = nn.ModuleList()
for lyr_num in range(snake_case__ ):
lowerCAmelCase : Union[str, Any] = TaBlock(snake_case__ )
self.encoders.append(snake_case__ )
lowerCAmelCase : Any = TaLayerNorm(snake_case__ )
lowerCAmelCase : List[str] = nn.Dropout(p=snake_case__ )
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Tuple = self.token_embedder(snake_case__ )
lowerCAmelCase : Dict = encoder_input_tokens.shape[1]
lowerCAmelCase : Union[str, Any] = torch.arange(snake_case__ , device=encoder_input_tokens.device )
x += self.position_encoding(snake_case__ )
lowerCAmelCase : List[str] = self.dropout_pre(snake_case__ )
# inverted the attention mask
lowerCAmelCase : Optional[Any] = encoder_input_tokens.size()
lowerCAmelCase : Tuple = self.get_extended_attention_mask(snake_case__ , snake_case__ )
for lyr in self.encoders:
lowerCAmelCase : Any = lyr(snake_case__ , snake_case__ )[0]
lowerCAmelCase : Optional[int] = self.layer_norm(snake_case__ )
return self.dropout_post(snake_case__ ), encoder_inputs_mask
| 359
|
"""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 SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
@staticmethod
def lowercase__ ( *snake_case__ , **snake_case__ ):
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
a : Optional[Any] =MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowercase__ ( self , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : str = pipeline(
"zero-shot-object-detection" , model="hf-internal-testing/tiny-random-owlvit-object-detection" )
lowerCAmelCase : Dict = [
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"candidate_labels": ["cat", "remote", "couch"],
}
]
return object_detector, examples
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = object_detector(examples[0] , threshold=0.0 )
lowerCAmelCase : Dict = len(snake_case__ )
self.assertGreater(snake_case__ , 0 )
self.assertEqual(
snake_case__ , [
{
"score": ANY(snake_case__ ),
"label": ANY(snake_case__ ),
"box": {"xmin": ANY(snake_case__ ), "ymin": ANY(snake_case__ ), "xmax": ANY(snake_case__ ), "ymax": ANY(snake_case__ )},
}
for i in range(snake_case__ )
] , )
@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 : str = pipeline(
"zero-shot-object-detection" , model="hf-internal-testing/tiny-random-owlvit-object-detection" )
lowerCAmelCase : Tuple = object_detector(
"./tests/fixtures/tests_samples/COCO/000000039769.png" , candidate_labels=["cat", "remote", "couch"] , threshold=0.64 , )
self.assertEqual(
nested_simplify(snake_case__ , decimals=4 ) , [
{"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
{"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
{"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
] , )
lowerCAmelCase : Optional[Any] = object_detector(
[
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"candidate_labels": ["cat", "remote", "couch"],
}
] , threshold=0.64 , )
self.assertEqual(
nested_simplify(snake_case__ , decimals=4 ) , [
[
{"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
{"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
{"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
]
] , )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Dict = pipeline("zero-shot-object-detection" )
lowerCAmelCase : Dict = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , )
self.assertEqual(
nested_simplify(snake_case__ , decimals=4 ) , [
{"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
{"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
{"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
] , )
lowerCAmelCase : Dict = 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(snake_case__ , decimals=4 ) , [
[
{"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
{"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
{"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
],
[
{"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
{"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
{"score": 0.1208, "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 : Dict = 0.2
lowerCAmelCase : List[Any] = pipeline("zero-shot-object-detection" )
lowerCAmelCase : Union[str, Any] = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , threshold=snake_case__ , )
self.assertEqual(
nested_simplify(snake_case__ , decimals=4 ) , [
{"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
] , )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : int = 2
lowerCAmelCase : Any = pipeline("zero-shot-object-detection" )
lowerCAmelCase : Any = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , top_k=snake_case__ , )
self.assertEqual(
nested_simplify(snake_case__ , decimals=4 ) , [
{"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
] , )
| 133
| 0
|
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ):
def __init__( self : Optional[int] , A : List[Any] , A : Union[str, Any] , A : int , A : int , ) ->int:
super().__init__()
lowerCamelCase__ : List[Any] = value_function
lowerCamelCase__ : Union[str, Any] = unet
lowerCamelCase__ : str = scheduler
lowerCamelCase__ : Union[str, Any] = env
lowerCamelCase__ : Tuple = env.get_dataset()
lowerCamelCase__ : Optional[int] = {}
for key in self.data.keys():
try:
lowerCamelCase__ : Any = self.data[key].mean()
except: # noqa: E722
pass
lowerCamelCase__ : Union[str, Any] = {}
for key in self.data.keys():
try:
lowerCamelCase__ : Optional[Any] = self.data[key].std()
except: # noqa: E722
pass
lowerCamelCase__ : str = env.observation_space.shape[0]
lowerCamelCase__ : Union[str, Any] = env.action_space.shape[0]
def __lowerCamelCase ( self : Optional[Any] , A : str , A : Any ) ->Union[str, Any]:
return (x_in - self.means[key]) / self.stds[key]
def __lowerCamelCase ( self : int , A : Tuple , A : Tuple ) ->Optional[int]:
return x_in * self.stds[key] + self.means[key]
def __lowerCamelCase ( self : Optional[int] , A : Union[str, Any] ) ->List[str]:
if type(_a ) is dict:
return {k: self.to_torch(_a ) for k, v in x_in.items()}
elif torch.is_tensor(_a ):
return x_in.to(self.unet.device )
return torch.tensor(_a , device=self.unet.device )
def __lowerCamelCase ( self : List[str] , A : Any , A : Dict , A : Dict ) ->Optional[Any]:
for key, val in cond.items():
lowerCamelCase__ : int = val.clone()
return x_in
def __lowerCamelCase ( self : Optional[int] , A : Tuple , A : List[str] , A : int , A : Optional[int] ) ->Optional[int]:
lowerCamelCase__ : Union[str, Any] = x.shape[0]
lowerCamelCase__ : Any = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
lowerCamelCase__ : Dict = torch.full((batch_size,) , _a , device=self.unet.device , dtype=torch.long )
for _ in range(_a ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
lowerCamelCase__ : List[Any] = self.value_function(x.permute(0 , 2 , 1 ) , _a ).sample
lowerCamelCase__ : Optional[Any] = torch.autograd.grad([y.sum()] , [x] )[0]
lowerCamelCase__ : Optional[int] = self.scheduler._get_variance(_a )
lowerCamelCase__ : List[Any] = torch.exp(0.5 * posterior_variance )
lowerCamelCase__ : Tuple = model_std * grad
lowerCamelCase__ : Optional[Any] = 0
lowerCamelCase__ : Any = x.detach()
lowerCamelCase__ : List[Any] = x + scale * grad
lowerCamelCase__ : Any = self.reset_xa(_a , _a , self.action_dim )
lowerCamelCase__ : str = self.unet(x.permute(0 , 2 , 1 ) , _a ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
lowerCamelCase__ : Dict = self.scheduler.step(_a , _a , _a , predict_epsilon=_a )["""prev_sample"""]
# apply conditions to the trajectory (set the initial state)
lowerCamelCase__ : str = self.reset_xa(_a , _a , self.action_dim )
lowerCamelCase__ : List[Any] = self.to_torch(_a )
return x, y
def __call__( self : Tuple , A : int , A : Any=6_4 , A : Union[str, Any]=3_2 , A : Optional[Any]=2 , A : Optional[int]=0.1 ) ->Any:
# normalize the observations and create batch dimension
lowerCamelCase__ : Any = self.normalize(_a , '''observations''' )
lowerCamelCase__ : Optional[int] = obs[None].repeat(_a , axis=0 )
lowerCamelCase__ : List[str] = {0: self.to_torch(_a )}
lowerCamelCase__ : int = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
lowerCamelCase__ : Optional[int] = randn_tensor(_a , device=self.unet.device )
lowerCamelCase__ : int = self.reset_xa(_a , _a , self.action_dim )
lowerCamelCase__ : int = self.to_torch(_a )
# run the diffusion process
lowerCamelCase__ : List[Any] = self.run_diffusion(_a , _a , _a , _a )
# sort output trajectories by value
lowerCamelCase__ : Optional[int] = y.argsort(0 , descending=_a ).squeeze()
lowerCamelCase__ : Tuple = x[sorted_idx]
lowerCamelCase__ : Optional[int] = sorted_values[:, :, : self.action_dim]
lowerCamelCase__ : Union[str, Any] = actions.detach().cpu().numpy()
lowerCamelCase__ : Tuple = self.de_normalize(_a , key='''actions''' )
# select the action with the highest value
if y is not None:
lowerCamelCase__ : int = 0
else:
# if we didn't run value guiding, select a random action
lowerCamelCase__ : Optional[Any] = np.random.randint(0 , _a )
lowerCamelCase__ : Any = denorm_actions[selected_index, 0]
return denorm_actions
| 142
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
"facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
"facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
"facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
"facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
"facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
"facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
"facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
"facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}
class lowercase ( UpperCamelCase__ ):
_a = "xmod"
def __init__( self , _a=3_0522 , _a=768 , _a=12 , _a=12 , _a=3072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=2 , _a=0.02 , _a=1e-12 , _a=1 , _a=0 , _a=2 , _a="absolute" , _a=True , _a=None , _a=False , _a=2 , _a=False , _a=True , _a=True , _a=("en_XX",) , _a=None , **_a , ) -> str:
super().__init__(pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a )
_A : Tuple = vocab_size
_A : Union[str, Any] = hidden_size
_A : Dict = num_hidden_layers
_A : Dict = num_attention_heads
_A : List[Any] = hidden_act
_A : Optional[Any] = intermediate_size
_A : Any = hidden_dropout_prob
_A : str = attention_probs_dropout_prob
_A : Dict = max_position_embeddings
_A : Any = type_vocab_size
_A : List[Any] = initializer_range
_A : int = layer_norm_eps
_A : int = position_embedding_type
_A : Any = use_cache
_A : int = classifier_dropout
_A : int = pre_norm
_A : Optional[Any] = adapter_reduction_factor
_A : List[Any] = adapter_layer_norm
_A : Optional[int] = adapter_reuse_layer_norm
_A : Any = ln_before_adapter
_A : Union[str, Any] = list(_a )
_A : List[Any] = default_language
class lowercase ( UpperCamelCase__ ):
@property
def a__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A : Dict = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_A : Dict = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 26
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : Optional[Any] = {
"configuration_xmod": [
"XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XmodConfig",
"XmodOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : List[Any] = [
"XMOD_PRETRAINED_MODEL_ARCHIVE_LIST",
"XmodForCausalLM",
"XmodForMaskedLM",
"XmodForMultipleChoice",
"XmodForQuestionAnswering",
"XmodForSequenceClassification",
"XmodForTokenClassification",
"XmodModel",
"XmodPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 366
|
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def UpperCamelCase_( snake_case : str , snake_case : complex , snake_case : str = "x" , snake_case : float = 1_0**-1_0 , snake_case : int = 1 , ):
'''simple docstring'''
snake_case_ = symbols(snake_case )
snake_case_ = lambdify(snake_case , snake_case )
snake_case_ = lambdify(snake_case , diff(snake_case , snake_case ) )
snake_case_ = starting_point
while True:
if diff_function(snake_case ) != 0:
snake_case_ = prev_guess - multiplicity * func(snake_case ) / diff_function(
snake_case )
else:
raise ZeroDivisionError("Could not find root" ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
snake_case_ = next_guess
# 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
# Find fourth Root of 5
print(F"The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}")
# Find value of e
print(
"The root of log(y) - 1 = 0 is ",
F"{newton_raphson('log(y) - 1', 2, variable='y')}",
)
# Exponential Roots
print(
"The root of exp(x) - 1 = 0 is",
F"{newton_raphson('exp(x) - 1', 10, precision=0.0_0_5)}",
)
# Find root of cos(x)
print(F"The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}")
| 92
| 0
|
import unittest
import numpy as np
def UpperCamelCase ( snake_case__ : np.ndarray , snake_case__ : np.ndarray , snake_case__ : np.ndarray , snake_case__ : np.ndarray | None = None , ) -> np.ndarray:
UpperCamelCase : Optional[Any] = np.shape(snake_case__ )
UpperCamelCase : List[str] = np.shape(snake_case__ )
UpperCamelCase : Optional[Any] = np.shape(snake_case__ )
if shape_a[0] != shape_b[0]:
UpperCamelCase : str = (
'Expected the same number of rows for A and B. '
F"""Instead found A of size {shape_a} and B of size {shape_b}"""
)
raise ValueError(snake_case__ )
if shape_b[1] != shape_c[1]:
UpperCamelCase : str = (
'Expected the same number of columns for B and C. '
F"""Instead found B of size {shape_b} and C of size {shape_c}"""
)
raise ValueError(snake_case__ )
UpperCamelCase : List[str] = pseudo_inv
if a_inv is None:
try:
UpperCamelCase : str = np.linalg.inv(snake_case__ )
except np.linalg.LinAlgError:
raise ValueError(
'Input matrix A is not invertible. Cannot compute Schur complement.' )
return mat_c - mat_b.T @ a_inv @ mat_b
class lowerCAmelCase_ ( unittest.TestCase ):
def snake_case_ ( self ) -> None:
UpperCamelCase : str = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCamelCase : List[Any] = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCamelCase : Tuple = np.array([[2, 1], [6, 3]] )
UpperCamelCase : Optional[Any] = schur_complement(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.block([[a, b], [b.T, c]] )
UpperCamelCase : Optional[int] = np.linalg.det(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = np.linalg.det(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = np.linalg.det(SCREAMING_SNAKE_CASE_ )
self.assertAlmostEqual(SCREAMING_SNAKE_CASE_, det_a * det_s )
def snake_case_ ( self ) -> None:
UpperCamelCase : Tuple = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCamelCase : Dict = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCamelCase : str = np.array([[2, 1], [6, 3]] )
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
schur_complement(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ) -> None:
UpperCamelCase : Dict = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCamelCase : Optional[Any] = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCamelCase : str = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
schur_complement(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 119
|
import argparse
import json
from tqdm import tqdm
def UpperCamelCase ( ) -> Optional[int]:
UpperCamelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=snake_case__ , default='biencoder-nq-dev.json' , help='Path to raw DPR training data' , )
parser.add_argument(
'--evaluation_set' , type=snake_case__ , help='where to store parsed evaluation_set file' , )
parser.add_argument(
'--gold_data_path' , type=snake_case__ , help='where to store parsed gold_data_path file' , )
UpperCamelCase : int = parser.parse_args()
with open(args.src_path , 'r' ) as src_file, open(args.evaluation_set , 'w' ) as eval_file, open(
args.gold_data_path , 'w' ) as gold_file:
UpperCamelCase : int = json.load(snake_case__ )
for dpr_record in tqdm(snake_case__ ):
UpperCamelCase : Union[str, Any] = dpr_record['question']
UpperCamelCase : Dict = [context['title'] for context in dpr_record['positive_ctxs']]
eval_file.write(question + '\n' )
gold_file.write('\t'.join(snake_case__ ) + '\n' )
if __name__ == "__main__":
main()
| 119
| 1
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( __a , __a , unittest.TestCase ):
snake_case : List[Any] = StableDiffusionXLImgaImgPipeline
snake_case : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
snake_case : List[str] = PipelineTesterMixin.required_optional_params - {'''latents'''}
snake_case : int = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
snake_case : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
snake_case : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def snake_case_ (self ):
torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , attention_head_dim=(2, 4) , use_linear_projection=_snake_case , addition_embed_type="""text_time""" , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=8_0 , cross_attention_dim=6_4 , )
_UpperCAmelCase : Dict = EulerDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , steps_offset=1 , beta_schedule="""scaled_linear""" , timestep_spacing="""leading""" , )
torch.manual_seed(0 )
_UpperCAmelCase : Union[str, Any] = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
_UpperCAmelCase : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act="""gelu""" , projection_dim=3_2 , )
_UpperCAmelCase : Union[str, Any] = CLIPTextModel(_snake_case )
_UpperCAmelCase : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=_snake_case )
_UpperCAmelCase : int = CLIPTextModelWithProjection(_snake_case )
_UpperCAmelCase : List[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=_snake_case )
_UpperCAmelCase : List[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""text_encoder_2""": text_encoder_a,
"""tokenizer_2""": tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__=0 ):
_UpperCAmelCase : Any = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(_snake_case ) ).to(_snake_case )
_UpperCAmelCase : List[Any] = image / 2 + 0.5
if str(_snake_case ).startswith("""mps""" ):
_UpperCAmelCase : Any = torch.manual_seed(_snake_case )
else:
_UpperCAmelCase : List[Any] = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_UpperCAmelCase : Any = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 5.0,
"""output_type""": """numpy""",
"""strength""": 0.7_5,
}
return inputs
def snake_case_ (self ):
_UpperCAmelCase : int = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : str = self.get_dummy_components()
_UpperCAmelCase : List[Any] = StableDiffusionXLImgaImgPipeline(**_snake_case )
_UpperCAmelCase : int = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
_UpperCAmelCase : int = self.get_dummy_inputs(_snake_case )
_UpperCAmelCase : Optional[Any] = sd_pipe(**_snake_case ).images
_UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
_UpperCAmelCase : Union[str, Any] = np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case_ (self ):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )
def snake_case_ (self ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def snake_case_ (self ):
pass
def snake_case_ (self ):
_UpperCAmelCase : Dict = self.get_dummy_components()
_UpperCAmelCase : List[str] = StableDiffusionXLImgaImgPipeline(**_snake_case )
_UpperCAmelCase : int = sd_pipe.to(_snake_case )
_UpperCAmelCase : Optional[int] = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
# forward without prompt embeds
_UpperCAmelCase : str = self.get_dummy_inputs(_snake_case )
_UpperCAmelCase : Optional[Any] = 3 * ["""this is a negative prompt"""]
_UpperCAmelCase : Union[str, Any] = negative_prompt
_UpperCAmelCase : Any = 3 * [inputs["""prompt"""]]
_UpperCAmelCase : Optional[int] = sd_pipe(**_snake_case )
_UpperCAmelCase : Union[str, Any] = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
_UpperCAmelCase : List[str] = self.get_dummy_inputs(_snake_case )
_UpperCAmelCase : str = 3 * ["""this is a negative prompt"""]
_UpperCAmelCase : int = 3 * [inputs.pop("""prompt""" )]
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : List[str] = sd_pipe.encode_prompt(_snake_case , negative_prompt=_snake_case )
_UpperCAmelCase : Optional[int] = sd_pipe(
**_snake_case , prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , pooled_prompt_embeds=_snake_case , negative_pooled_prompt_embeds=_snake_case , )
_UpperCAmelCase : Dict = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case_ (self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__="cpu" , lowerCAmelCase__=torch.floataa , lowerCAmelCase__=0 ):
_UpperCAmelCase : Optional[Any] = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_UpperCAmelCase : int = np.random.RandomState(_snake_case ).standard_normal((1, 4, 6_4, 6_4) )
_UpperCAmelCase : List[Any] = torch.from_numpy(_snake_case ).to(device=_snake_case , dtype=_snake_case )
_UpperCAmelCase : Union[str, Any] = {
"""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 ):
_UpperCAmelCase : Any = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""" )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
_UpperCAmelCase : Dict = self.get_inputs(_snake_case )
_UpperCAmelCase : Tuple = pipe(**_snake_case ).images
_UpperCAmelCase : Dict = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_UpperCAmelCase : List[Any] = np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6] )
assert np.abs(image_slice - expected_slice ).max() < 7e-3
| 357
|
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
lowerCAmelCase_ : Any = logging.getLogger(__name__)
class __lowerCAmelCase ( __a ):
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=None ):
_UpperCAmelCase : str = self.layer[current_layer](lowerCAmelCase__ , lowerCAmelCase__ , head_mask[current_layer] )
_UpperCAmelCase : List[Any] = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"""The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.""" , __a , )
class __lowerCAmelCase ( __a ):
def __init__(self , lowerCAmelCase__ ):
super().__init__(lowerCAmelCase__ )
_UpperCAmelCase : Tuple = BertEncoderWithPabee(lowerCAmelCase__ )
self.init_weights()
_UpperCAmelCase : List[str] = 0
_UpperCAmelCase : Union[str, Any] = 0
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : int = 0
def snake_case_ (self , lowerCAmelCase__ ):
_UpperCAmelCase : Union[str, Any] = threshold
def snake_case_ (self , lowerCAmelCase__ ):
_UpperCAmelCase : Union[str, Any] = patience
def snake_case_ (self ):
_UpperCAmelCase : int = 0
_UpperCAmelCase : Optional[Any] = 0
def snake_case_ (self ):
_UpperCAmelCase : Union[str, Any] = self.inference_layers_num / self.inference_instances_num
_UpperCAmelCase : Optional[int] = (
F"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="
F" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"
)
print(lowerCAmelCase__ )
@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
def snake_case_ (self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=False , ):
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""" )
elif input_ids is not None:
_UpperCAmelCase : Optional[Any] = input_ids.size()
elif inputs_embeds is not None:
_UpperCAmelCase : str = inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""" )
_UpperCAmelCase : Optional[Any] = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
_UpperCAmelCase : Optional[Any] = torch.ones(lowerCAmelCase__ , device=lowerCAmelCase__ )
if token_type_ids is None:
_UpperCAmelCase : Optional[int] = torch.zeros(lowerCAmelCase__ , dtype=torch.long , device=lowerCAmelCase__ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
_UpperCAmelCase : torch.Tensor = self.get_extended_attention_mask(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Optional[Any] = encoder_hidden_states.size()
_UpperCAmelCase : List[str] = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
_UpperCAmelCase : Tuple = torch.ones(lowerCAmelCase__ , device=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = self.invert_attention_mask(lowerCAmelCase__ )
else:
_UpperCAmelCase : List[str] = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
_UpperCAmelCase : Any = self.get_head_mask(lowerCAmelCase__ , self.config.num_hidden_layers )
_UpperCAmelCase : int = self.embeddings(
input_ids=lowerCAmelCase__ , position_ids=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , inputs_embeds=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = embedding_output
if self.training:
_UpperCAmelCase : Union[str, Any] = []
for i in range(self.config.num_hidden_layers ):
_UpperCAmelCase : Tuple = self.encoder.adaptive_forward(
lowerCAmelCase__ , current_layer=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , head_mask=lowerCAmelCase__ )
_UpperCAmelCase : Any = self.pooler(lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = output_layers[i](output_dropout(lowerCAmelCase__ ) )
res.append(lowerCAmelCase__ )
elif self.patience == 0: # Use all layers for inference
_UpperCAmelCase : int = self.encoder(
lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , head_mask=lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , encoder_attention_mask=lowerCAmelCase__ , )
_UpperCAmelCase : List[str] = self.pooler(encoder_outputs[0] )
_UpperCAmelCase : List[Any] = [output_layers[self.config.num_hidden_layers - 1](lowerCAmelCase__ )]
else:
_UpperCAmelCase : Union[str, Any] = 0
_UpperCAmelCase : Optional[Any] = None
_UpperCAmelCase : int = 0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
_UpperCAmelCase : int = self.encoder.adaptive_forward(
lowerCAmelCase__ , current_layer=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , head_mask=lowerCAmelCase__ )
_UpperCAmelCase : Tuple = self.pooler(lowerCAmelCase__ )
_UpperCAmelCase : int = output_layers[i](lowerCAmelCase__ )
if regression:
_UpperCAmelCase : List[Any] = logits.detach()
if patient_result is not None:
_UpperCAmelCase : Union[str, Any] = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
_UpperCAmelCase : List[str] = 0
else:
_UpperCAmelCase : Optional[int] = logits.detach().argmax(dim=1 )
if patient_result is not None:
_UpperCAmelCase : str = patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase__ ) ):
patient_counter += 1
else:
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : List[str] = logits
if patient_counter == self.patience:
break
_UpperCAmelCase : List[str] = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"""Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """ , __a , )
class __lowerCAmelCase ( __a ):
def __init__(self , lowerCAmelCase__ ):
super().__init__(lowerCAmelCase__ )
_UpperCAmelCase : int = config.num_labels
_UpperCAmelCase : List[Any] = BertModelWithPabee(lowerCAmelCase__ )
_UpperCAmelCase : int = nn.Dropout(config.hidden_dropout_prob )
_UpperCAmelCase : str = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
def snake_case_ (self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , ):
_UpperCAmelCase : Optional[int] = self.bert(
input_ids=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , position_ids=lowerCAmelCase__ , head_mask=lowerCAmelCase__ , inputs_embeds=lowerCAmelCase__ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
_UpperCAmelCase : Any = (logits[-1],)
if labels is not None:
_UpperCAmelCase : Union[str, Any] = None
_UpperCAmelCase : int = 0
for ix, logits_item in enumerate(lowerCAmelCase__ ):
if self.num_labels == 1:
# We are doing regression
_UpperCAmelCase : Dict = MSELoss()
_UpperCAmelCase : List[Any] = loss_fct(logits_item.view(-1 ) , labels.view(-1 ) )
else:
_UpperCAmelCase : Optional[Any] = CrossEntropyLoss()
_UpperCAmelCase : Union[str, Any] = loss_fct(logits_item.view(-1 , self.num_labels ) , labels.view(-1 ) )
if total_loss is None:
_UpperCAmelCase : Any = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
_UpperCAmelCase : Tuple = (total_loss / total_weights,) + outputs
return outputs
| 170
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|
"""simple docstring"""
from functools import reduce
A_ = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def UpperCAmelCase__ (snake_case__ : str = N ):
"""simple docstring"""
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda snake_case__ , snake_case__ : str(int(lowerCAmelCase_ ) * int(lowerCAmelCase_ ) ) , n[i : i + 13] ) )
for i in range(len(lowerCAmelCase_ ) - 12 ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 64
|
from itertools import permutations
def snake_case_ ( lowerCAmelCase_ : tuple ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
__lowercase : Dict = [7, 11, 13, 17]
for i, test in enumerate(lowerCAmelCase_ ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def snake_case_ ( lowerCAmelCase_ : int = 10 ):
return sum(
int("""""".join(map(lowerCAmelCase_ , lowerCAmelCase_ ) ) )
for num in permutations(range(lowerCAmelCase_ ) )
if is_substring_divisible(lowerCAmelCase_ ) )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 233
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|
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
_A = ['bert-base-uncased', 'bert-base-cased']
_A = 'hf-internal-testing/tiny-bert-tf-only'
if is_tf_available():
class _lowercase ( tf.keras.Model ):
def __init__( self , UpperCAmelCase_ ) -> str:
super().__init__()
lowerCamelCase : List[Any] = tokenizer
lowerCamelCase : int = AutoConfig.from_pretrained(lowerCAmelCase__ )
lowerCamelCase : List[str] = TFAutoModel.from_config(lowerCAmelCase__ )
def _UpperCamelCase ( self , UpperCAmelCase_ ) -> Tuple:
lowerCamelCase : int = self.tokenizer(lowerCAmelCase__ )
lowerCamelCase : List[Any] = self.bert(**lowerCAmelCase__ )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class _lowercase ( unittest.TestCase ):
def _UpperCamelCase ( self ) -> Optional[int]:
super().setUp()
lowerCamelCase : Union[str, Any] = [
BertTokenizer.from_pretrained(lowerCAmelCase__ ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
lowerCamelCase : List[Any] = [TFBertTokenizer.from_pretrained(lowerCAmelCase__ ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(lowerCAmelCase__ , use_fast_bert_tokenizer=lowerCAmelCase__ )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
lowerCamelCase : Union[str, Any] = [
'This is a straightforward English test sentence.',
'This one has some weird characters\rto\nsee\r\nif those\u00E9break things.',
'Now we\'re going to add some Chinese: 一 二 三 一二三',
'And some much more rare Chinese: 齉 堃 齉堃',
'Je vais aussi écrire en français pour tester les accents',
'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
]
lowerCamelCase : Optional[Any] = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def _UpperCamelCase ( self ) -> Any:
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
lowerCamelCase : str = tokenizer(lowerCAmelCase__ , return_tensors='tf' , padding='longest' )
lowerCamelCase : Union[str, Any] = tf_tokenizer(lowerCAmelCase__ )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def _UpperCamelCase ( self ) -> Union[str, Any]:
for tf_tokenizer in self.tf_tokenizers:
lowerCamelCase : List[str] = tf_tokenizer(self.paired_sentences )
lowerCamelCase : Union[str, Any] = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def _UpperCamelCase ( self ) -> List[Any]:
for tf_tokenizer in self.tf_tokenizers:
lowerCamelCase : List[Any] = tf.function(lowerCAmelCase__ )
for test_inputs in (self.test_sentences, self.paired_sentences):
lowerCamelCase : List[str] = tf.constant(lowerCAmelCase__ )
lowerCamelCase : Optional[int] = compiled_tokenizer(lowerCAmelCase__ )
lowerCamelCase : Dict = tf_tokenizer(lowerCAmelCase__ )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def _UpperCamelCase ( self ) -> Dict:
for tf_tokenizer in self.tf_tokenizers:
lowerCamelCase : List[str] = ModelToSave(tokenizer=lowerCAmelCase__ )
lowerCamelCase : Optional[Any] = tf.convert_to_tensor(self.test_sentences )
lowerCamelCase : str = model(lowerCAmelCase__ ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
lowerCamelCase : List[str] = Path(lowerCAmelCase__ ) / 'saved.model'
model.save(lowerCAmelCase__ )
lowerCamelCase : Optional[int] = tf.keras.models.load_model(lowerCAmelCase__ )
lowerCamelCase : Optional[int] = loaded_model(lowerCAmelCase__ )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1E-5 )
| 366
|
"""simple docstring"""
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
_A = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
_A = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F"""{len(upper_files)} files contain uppercase characters:""")
print('\n'.join(upper_files) + '\n')
_A = [file for file in filepaths if ' ' in file]
if space_files:
print(F"""{len(space_files)} files contain space characters:""")
print('\n'.join(space_files) + '\n')
_A = [file for file in filepaths if '-' in file]
if hyphen_files:
print(F"""{len(hyphen_files)} files contain hyphen characters:""")
print('\n'.join(hyphen_files) + '\n')
_A = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F"""{len(nodir_files)} files are not in a directory:""")
print('\n'.join(nodir_files) + '\n')
_A = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 205
| 0
|
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def lowerCAmelCase_ ( _lowerCamelCase: Optional[Any]=None , _lowerCamelCase: List[str]=None ):
return field(default_factory=lambda: default , metadata=snake_case_ )
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : str = field(
metadata={'''help''': '''The csv file to plot.'''} , )
_A : bool = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''} , )
_A : bool = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''} , )
_A : bool = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Disable logarithmic scale when plotting'''} , )
_A : bool = field(
default=UpperCAmelCase__ , metadata={
'''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
} , )
_A : Optional[str] = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''} , )
_A : Optional[List[str]] = list_field(
default=UpperCAmelCase__ , metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''} )
def lowerCAmelCase_ ( _lowerCamelCase: Union[str, Any] ):
try:
int(snake_case_ )
return True
except ValueError:
return False
def lowerCAmelCase_ ( _lowerCamelCase: List[Any] ):
try:
float(snake_case_ )
return True
except ValueError:
return False
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , lowerCAmelCase__ : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = args
__SCREAMING_SNAKE_CASE : Dict = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )
with open(self.args.csv_file , newline="""""" ) as csv_file:
__SCREAMING_SNAKE_CASE : List[str] = csv.DictReader(snake_case__ )
for row in reader:
__SCREAMING_SNAKE_CASE : str = row["""model"""]
self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""] ) )
self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""] ) )
if can_convert_to_int(row["""result"""] ):
# value is not None
__SCREAMING_SNAKE_CASE : Any = int(row["""result"""] )
elif can_convert_to_float(row["""result"""] ):
# value is not None
__SCREAMING_SNAKE_CASE : Union[str, Any] = float(row["""result"""] )
def UpperCamelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = plt.subplots()
__SCREAMING_SNAKE_CASE : Dict = """Time usage""" if self.args.is_time else """Memory usage"""
__SCREAMING_SNAKE_CASE : List[str] = title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale("""log""" )
ax.set_yscale("""log""" )
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter() )
for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
__SCREAMING_SNAKE_CASE : str = sorted(set(self.result_dict[model_name]["""bsz"""] ) )
__SCREAMING_SNAKE_CASE : Dict = sorted(set(self.result_dict[model_name]["""seq_len"""] ) )
__SCREAMING_SNAKE_CASE : List[str] = self.result_dict[model_name]["""result"""]
((__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE)) : List[str] = (
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
__SCREAMING_SNAKE_CASE : List[str] = (
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
__SCREAMING_SNAKE_CASE : Any = np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] , dtype=snake_case__ , )
else:
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] , dtype=np.floataa , )
((__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE)) : List[Any] = (
("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
)
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(snake_case__ , snake_case__ )[: len(snake_case__ )]
plt.scatter(
snake_case__ , snake_case__ , label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}" )
plt.plot(snake_case__ , snake_case__ , """--""" )
title_str += F" {label_model_name} vs."
__SCREAMING_SNAKE_CASE : List[str] = title_str[:-4]
__SCREAMING_SNAKE_CASE : Tuple = """Time in s""" if self.args.is_time else """Memory in MB"""
# plot
plt.title(snake_case__ )
plt.xlabel(snake_case__ )
plt.ylabel(snake_case__ )
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file )
else:
plt.show()
def lowerCAmelCase_ ( ):
__SCREAMING_SNAKE_CASE : List[str] = HfArgumentParser(snake_case_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args_into_dataclasses()[0]
__SCREAMING_SNAKE_CASE : List[Any] = Plot(args=snake_case_ )
plot.plot()
if __name__ == "__main__":
main()
| 112
|
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class __lowerCAmelCase ( UpperCAmelCase__ ):
snake_case_ : str = "Wav2Vec2FeatureExtractor"
snake_case_ : Dict = "AutoTokenizer"
def __init__( self : Tuple , snake_case__ : Optional[Any] , snake_case__ : Dict ):
"""simple docstring"""
super().__init__(snake_case__ , snake_case__ )
_UpperCAmelCase = self.feature_extractor
_UpperCAmelCase = False
@classmethod
def UpperCamelCase ( cls : List[Any] , snake_case__ : Optional[Any] , **snake_case__ : Any ):
"""simple docstring"""
try:
return super().from_pretrained(snake_case__ , **snake_case__ )
except OSError:
warnings.warn(
F"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
" include a `tokenizer_class` attribute is deprecated and will be "
"removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
" attribute to either your `config.json` or `tokenizer_config.json` "
"file to suppress this warning: " , snake_case__ , )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(snake_case__ , **snake_case__ )
_UpperCAmelCase = WavaVecaCTCTokenizer.from_pretrained(snake_case__ , **snake_case__ )
return cls(feature_extractor=snake_case__ , tokenizer=snake_case__ )
def __call__( self : int , *snake_case__ : Tuple , **snake_case__ : List[str] ):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor(*snake_case__ , **snake_case__ )
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
_UpperCAmelCase = kwargs.pop("raw_speech" )
else:
_UpperCAmelCase = kwargs.pop("audio" , snake_case__ )
_UpperCAmelCase = kwargs.pop("sampling_rate" , snake_case__ )
_UpperCAmelCase = kwargs.pop("text" , snake_case__ )
if len(snake_case__ ) > 0:
_UpperCAmelCase = args[0]
_UpperCAmelCase = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if audio is not None:
_UpperCAmelCase = self.feature_extractor(snake_case__ , *snake_case__ , sampling_rate=snake_case__ , **snake_case__ )
if text is not None:
_UpperCAmelCase = self.tokenizer(snake_case__ , **snake_case__ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_UpperCAmelCase = encodings["input_ids"]
return inputs
def UpperCamelCase ( self : List[str] , *snake_case__ : Any , **snake_case__ : Dict ):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor.pad(*snake_case__ , **snake_case__ )
_UpperCAmelCase = kwargs.pop("input_features" , snake_case__ )
_UpperCAmelCase = kwargs.pop("labels" , snake_case__ )
if len(snake_case__ ) > 0:
_UpperCAmelCase = args[0]
_UpperCAmelCase = args[1:]
if input_features is not None:
_UpperCAmelCase = self.feature_extractor.pad(snake_case__ , *snake_case__ , **snake_case__ )
if labels is not None:
_UpperCAmelCase = self.tokenizer.pad(snake_case__ , **snake_case__ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
_UpperCAmelCase = labels["input_ids"]
return input_features
def UpperCamelCase ( self : str , *snake_case__ : List[str] , **snake_case__ : Dict ):
"""simple docstring"""
return self.tokenizer.batch_decode(*snake_case__ , **snake_case__ )
def UpperCamelCase ( self : Union[str, Any] , *snake_case__ : List[str] , **snake_case__ : Union[str, Any] ):
"""simple docstring"""
return self.tokenizer.decode(*snake_case__ , **snake_case__ )
@contextmanager
def UpperCamelCase ( self : Tuple ):
"""simple docstring"""
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your audio inputs, or in a separate call." )
_UpperCAmelCase = True
_UpperCAmelCase = self.tokenizer
yield
_UpperCAmelCase = self.feature_extractor
_UpperCAmelCase = False
| 133
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ['MobileViTFeatureExtractor']
_snake_case = ['MobileViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileViTForImageClassification',
'MobileViTForSemanticSegmentation',
'MobileViTModel',
'MobileViTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileViTForImageClassification',
'TFMobileViTForSemanticSegmentation',
'TFMobileViTModel',
'TFMobileViTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 371
|
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_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_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 324
| 0
|
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowerCAmelCase: Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase: List[Any] = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class a__( snake_case__ ):
lowercase__ = """dpt"""
def __init__( self : Union[str, Any] , __snake_case : Union[str, Any]=7_68 , __snake_case : Optional[int]=12 , __snake_case : int=12 , __snake_case : Dict=30_72 , __snake_case : List[Any]="gelu" , __snake_case : List[str]=0.0 , __snake_case : Any=0.0 , __snake_case : Dict=0.02 , __snake_case : int=1e-1_2 , __snake_case : Any=3_84 , __snake_case : Optional[Any]=16 , __snake_case : Union[str, Any]=3 , __snake_case : Optional[int]=False , __snake_case : List[Any]=True , __snake_case : Any=[2, 5, 8, 11] , __snake_case : Optional[int]="project" , __snake_case : List[Any]=[4, 2, 1, 0.5] , __snake_case : Any=[96, 1_92, 3_84, 7_68] , __snake_case : Tuple=2_56 , __snake_case : Optional[Any]=-1 , __snake_case : Tuple=False , __snake_case : List[str]=True , __snake_case : Optional[Any]=0.4 , __snake_case : str=2_55 , __snake_case : int=0.1 , __snake_case : Any=[1, 10_24, 24, 24] , __snake_case : str=[0, 1] , __snake_case : Union[str, Any]=None , **__snake_case : List[Any] , ):
super().__init__(**_A )
a : Tuple = hidden_size
a : Any = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info('Initializing the config with a `BiT` backbone.' )
a : str = {
'global_padding': 'same',
'layer_type': 'bottleneck',
'depths': [3, 4, 9],
'out_features': ['stage1', 'stage2', 'stage3'],
'embedding_dynamic_padding': True,
}
a : Tuple = BitConfig(**_A )
elif isinstance(_A , _A ):
logger.info('Initializing the config with a `BiT` backbone.' )
a : Tuple = BitConfig(**_A )
elif isinstance(_A , _A ):
a : Optional[int] = backbone_config
else:
raise ValueError(
F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.""" )
a : Optional[Any] = backbone_featmap_shape
a : List[str] = neck_ignore_stages
if readout_type != "project":
raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.' )
else:
a : str = None
a : str = None
a : Tuple = []
a : Tuple = num_hidden_layers
a : List[Any] = num_attention_heads
a : str = intermediate_size
a : Optional[Any] = hidden_act
a : Optional[Any] = hidden_dropout_prob
a : Optional[Any] = attention_probs_dropout_prob
a : List[str] = initializer_range
a : List[Any] = layer_norm_eps
a : Tuple = image_size
a : int = patch_size
a : Union[str, Any] = num_channels
a : List[Any] = qkv_bias
a : Optional[Any] = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']' )
a : int = readout_type
a : List[str] = reassemble_factors
a : Optional[int] = neck_hidden_sizes
a : Any = fusion_hidden_size
a : str = head_in_index
a : List[Any] = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
a : Union[str, Any] = use_auxiliary_head
a : Tuple = auxiliary_loss_weight
a : Union[str, Any] = semantic_loss_ignore_index
a : str = semantic_classifier_dropout
def lowercase_ ( self : int ):
a : Optional[int] = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
a : Union[str, Any] = self.backbone_config.to_dict()
a : Optional[Any] = self.__class__.model_type
return output
| 297
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class a__ ( snake_case__ ):
_a : Optional[int] = """decision_transformer"""
_a : Optional[int] = ["""past_key_values"""]
_a : Dict = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , _A=1_7 , _A=4 , _A=1_2_8 , _A=4_0_9_6 , _A=True , _A=1 , _A=1_0_2_4 , _A=3 , _A=1 , _A=None , _A="relu" , _A=0.1 , _A=0.1 , _A=0.1 , _A=1E-5 , _A=0.02 , _A=True , _A=True , _A=5_0_2_5_6 , _A=5_0_2_5_6 , _A=False , _A=False , **_A , ):
"""simple docstring"""
__lowerCAmelCase = state_dim
__lowerCAmelCase = act_dim
__lowerCAmelCase = hidden_size
__lowerCAmelCase = max_ep_len
__lowerCAmelCase = action_tanh
__lowerCAmelCase = vocab_size
__lowerCAmelCase = n_positions
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
__lowerCAmelCase = n_inner
__lowerCAmelCase = activation_function
__lowerCAmelCase = resid_pdrop
__lowerCAmelCase = embd_pdrop
__lowerCAmelCase = attn_pdrop
__lowerCAmelCase = layer_norm_epsilon
__lowerCAmelCase = initializer_range
__lowerCAmelCase = scale_attn_weights
__lowerCAmelCase = use_cache
__lowerCAmelCase = scale_attn_by_inverse_layer_idx
__lowerCAmelCase = reorder_and_upcast_attn
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=_A , eos_token_id=_A , **_A )
| 92
| 0
|
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {"vocab_file": "vocab.txt"}
lowerCamelCase_ = {
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
lowerCamelCase_ = {
"facebook/esm2_t6_8M_UR50D": 1_0_2_4,
"facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}
def __lowerCamelCase ( a_ : List[Any] ) -> Optional[Any]:
with open(a_ , '''r''' ) as f:
__SCREAMING_SNAKE_CASE :List[Any] = f.read().splitlines()
return [l.strip() for l in lines]
class _SCREAMING_SNAKE_CASE( A ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ : List[str] = ['''input_ids''', '''attention_mask''']
def __init__( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__="<unk>" ,SCREAMING_SNAKE_CASE__="<cls>" ,SCREAMING_SNAKE_CASE__="<pad>" ,SCREAMING_SNAKE_CASE__="<mask>" ,SCREAMING_SNAKE_CASE__="<eos>" ,**SCREAMING_SNAKE_CASE__ ,) -> Optional[Any]:
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Optional[int] = load_vocab_file(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Any = dict(enumerate(self.all_tokens ) )
__SCREAMING_SNAKE_CASE :Tuple = {tok: ind for ind, tok in enumerate(self.all_tokens )}
__SCREAMING_SNAKE_CASE :List[str] = unk_token
__SCREAMING_SNAKE_CASE :int = cls_token
__SCREAMING_SNAKE_CASE :int = pad_token
__SCREAMING_SNAKE_CASE :Tuple = mask_token
__SCREAMING_SNAKE_CASE :Any = eos_token
__SCREAMING_SNAKE_CASE :Optional[int] = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ) -> str:
"""simple docstring"""
return self._id_to_token.get(SCREAMING_SNAKE_CASE__ ,self.unk_token )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
return self._token_to_id.get(SCREAMING_SNAKE_CASE__ ,self._token_to_id.get(self.unk_token ) )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
return text.split()
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__=False ) -> List[Any]:
"""simple docstring"""
return len(self._id_to_token )
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
return {token: i for i, token in enumerate(self.all_tokens )}
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
return self._token_to_id.get(SCREAMING_SNAKE_CASE__ ,self._token_to_id.get(self.unk_token ) )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ) -> str:
"""simple docstring"""
return self._id_to_token.get(SCREAMING_SNAKE_CASE__ ,self.unk_token )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ = None ) -> List[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Optional[Any] = [self.cls_token_id]
__SCREAMING_SNAKE_CASE :Optional[Any] = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!''' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ = None ,SCREAMING_SNAKE_CASE__ = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
__SCREAMING_SNAKE_CASE :Dict = [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
if token_ids_a is not None:
mask += [0] * len(SCREAMING_SNAKE_CASE__ ) + [1]
return mask
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Any = os.path.join(SCREAMING_SNAKE_CASE__ ,(filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''' )
with open(SCREAMING_SNAKE_CASE__ ,'''w''' ) as f:
f.write('''\n'''.join(self.all_tokens ) )
return (vocab_file,)
@property
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
return self.get_vocab_size(with_added_tokens=SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ = False ) -> int:
"""simple docstring"""
return super()._add_tokens(SCREAMING_SNAKE_CASE__ ,special_tokens=SCREAMING_SNAKE_CASE__ )
| 370
|
"""simple docstring"""
def __lowerCamelCase ( a_ : int = 10 , a_ : int = 22 ) -> int:
__SCREAMING_SNAKE_CASE :Optional[int] = range(1 , a_ )
__SCREAMING_SNAKE_CASE :List[Any] = range(1 , a_ )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) == power )
if __name__ == "__main__":
print(f'{solution(1_0, 2_2) = }')
| 239
| 0
|
'''simple docstring'''
from collections.abc import Callable
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
A : float = a
A : float = b
if function(snake_case__ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case__ ) == 0:
return b
elif (
function(snake_case__ ) * function(snake_case__ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('''could not find root in given interval.''' )
else:
A : float = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(snake_case__ ) == 0:
return mid
elif function(snake_case__ ) * function(snake_case__ ) < 0:
A : Union[str, Any] = mid
else:
A : Optional[Any] = mid
A : Optional[int] = start + (end - start) / 2.0
return mid
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 10_00))
import doctest
doctest.testmod()
| 3
|
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 snake_case__ (A__ ):
"""simple docstring"""
__lowerCAmelCase :Optional[int] = "openai-gpt"
__lowerCAmelCase :Any = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , __lowercase=4_0_4_7_8 , __lowercase=5_1_2 , __lowercase=7_6_8 , __lowercase=1_2 , __lowercase=1_2 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=1E-5 , __lowercase=0.0_2 , __lowercase="cls_index" , __lowercase=True , __lowercase=None , __lowercase=True , __lowercase=0.1 , **__lowercase , ) -> Optional[int]:
"""simple docstring"""
a__ : Tuple = vocab_size
a__ : Union[str, Any] = n_positions
a__ : int = n_embd
a__ : Dict = n_layer
a__ : Dict = n_head
a__ : List[str] = afn
a__ : List[str] = resid_pdrop
a__ : List[Any] = embd_pdrop
a__ : List[str] = attn_pdrop
a__ : Dict = layer_norm_epsilon
a__ : List[str] = initializer_range
a__ : Tuple = summary_type
a__ : Union[str, Any] = summary_use_proj
a__ : Optional[Any] = summary_activation
a__ : Union[str, Any] = summary_first_dropout
a__ : Optional[Any] = summary_proj_to_labels
super().__init__(**__lowercase )
| 170
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|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase__ = (3, 9, -11, 0, 7, 5, 1, -1)
lowercase__ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A_ :
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = 42
UpperCAmelCase_ : List[str] = 42
class A_ :
'''simple docstring'''
def __init__( self : Dict , lowercase_ : List[Any] ) -> None:
UpperCAmelCase : Node | None = None
for i in sorted(__lowerCamelCase , reverse=__lowerCamelCase ):
UpperCAmelCase : Any = Node(__lowerCamelCase , self.head )
def __iter__( self : Union[str, Any] ) -> Iterator[int]:
UpperCAmelCase : int = self.head
while node:
yield node.data
UpperCAmelCase : int = node.next_node
def __len__( self : str ) -> int:
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
return " -> ".join([str(__lowerCamelCase ) for node in self] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
return SortedLinkedList(list(lowerCamelCase_ ) + list(lowerCamelCase_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase__ = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 356
|
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : int = tmp_path / 'cache'
UpperCAmelCase : List[str] = {'text': 'string'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCAmelCase : Tuple = TextDatasetReader(UpperCAmelCase_ , cache_dir=UpperCAmelCase_ , keep_in_memory=UpperCAmelCase_ ).read()
_check_text_dataset(UpperCAmelCase_ , UpperCAmelCase_ )
@pytest.mark.parametrize(
'features' , [
None,
{'text': 'string'},
{'text': 'int32'},
{'text': 'float32'},
] , )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Optional[int] = tmp_path / 'cache'
UpperCAmelCase : List[str] = {'text': 'string'}
UpperCAmelCase : Optional[int] = features.copy() if features else default_expected_features
UpperCAmelCase : int = (
Features({feature: Value(UpperCAmelCase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : Union[str, Any] = TextDatasetReader(UpperCAmelCase_ , features=UpperCAmelCase_ , cache_dir=UpperCAmelCase_ ).read()
_check_text_dataset(UpperCAmelCase_ , UpperCAmelCase_ )
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Optional[int] = tmp_path / 'cache'
UpperCAmelCase : Tuple = {'text': 'string'}
UpperCAmelCase : List[str] = TextDatasetReader(UpperCAmelCase_ , cache_dir=UpperCAmelCase_ , split=UpperCAmelCase_ ).read()
_check_text_dataset(UpperCAmelCase_ , UpperCAmelCase_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('path_type' , [str, list] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if issubclass(UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Tuple = text_path
elif issubclass(UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Optional[Any] = [text_path]
UpperCAmelCase : List[Any] = tmp_path / 'cache'
UpperCAmelCase : Union[str, Any] = {'text': 'string'}
UpperCAmelCase : List[Any] = TextDatasetReader(UpperCAmelCase_ , cache_dir=UpperCAmelCase_ ).read()
_check_text_dataset(UpperCAmelCase_ , UpperCAmelCase_ )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=("train",) ):
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
for split in splits:
UpperCAmelCase : Union[str, Any] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Any = tmp_path / 'cache'
UpperCAmelCase : List[str] = {'text': 'string'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCAmelCase : int = TextDatasetReader({'train': text_path} , cache_dir=UpperCAmelCase_ , keep_in_memory=UpperCAmelCase_ ).read()
_check_text_datasetdict(UpperCAmelCase_ , UpperCAmelCase_ )
@pytest.mark.parametrize(
'features' , [
None,
{'text': 'string'},
{'text': 'int32'},
{'text': 'float32'},
] , )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = tmp_path / 'cache'
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
UpperCAmelCase : Tuple = {'text': 'string'}
UpperCAmelCase : Union[str, Any] = features.copy() if features else default_expected_features
UpperCAmelCase : int = (
Features({feature: Value(UpperCAmelCase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : List[Any] = TextDatasetReader({'train': text_path} , features=UpperCAmelCase_ , cache_dir=UpperCAmelCase_ ).read()
_check_text_datasetdict(UpperCAmelCase_ , UpperCAmelCase_ )
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if split:
UpperCAmelCase : int = {split: text_path}
else:
UpperCAmelCase : int = 'train'
UpperCAmelCase : Any = {'train': text_path, 'test': text_path}
UpperCAmelCase : Dict = tmp_path / 'cache'
UpperCAmelCase : Any = {'text': 'string'}
UpperCAmelCase : List[str] = TextDatasetReader(UpperCAmelCase_ , cache_dir=UpperCAmelCase_ ).read()
_check_text_datasetdict(UpperCAmelCase_ , UpperCAmelCase_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
| 280
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|
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def snake_case_ (_a : Tuple ):
return {key.lstrip('''-''' ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def snake_case_ ():
UpperCAmelCase = ArgumentParser(
'''HuggingFace Datasets CLI tool''' , usage='''datasets-cli <command> [<args>]''' , allow_abbrev=_a )
UpperCAmelCase = parser.add_subparsers(help='''datasets-cli command helpers''' )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(_a )
EnvironmentCommand.register_subcommand(_a )
TestCommand.register_subcommand(_a )
RunBeamCommand.register_subcommand(_a )
DummyDataCommand.register_subcommand(_a )
# Parse args
UpperCAmelCase , UpperCAmelCase = parser.parse_known_args()
if not hasattr(_a , '''func''' ):
parser.print_help()
exit(1 )
UpperCAmelCase = parse_unknown_args(_a )
# Run
UpperCAmelCase = args.func(_a , **_a )
service.run()
if __name__ == "__main__":
main()
| 34
|
import os
def a ( A__ : str = "input.txt" ) -> int:
"""simple docstring"""
with open(os.path.join(os.path.dirname(A__ ) , A__ ) ) as input_file:
_lowercase =[
[int(A__ ) for element in line.split(',' )]
for line in input_file.readlines()
]
_lowercase =len(A__ )
_lowercase =len(matrix[0] )
_lowercase =[[-1 for _ in range(A__ )] for _ in range(A__ )]
for i in range(A__ ):
_lowercase =matrix[i][0]
for j in range(1 , A__ ):
for i in range(A__ ):
_lowercase =minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , A__ ):
_lowercase =min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
_lowercase =min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(f"{solution() = }")
| 205
| 0
|
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@parameterized.expand([(None,), ('''foo.json''',)])
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowercase_ : Any):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[str] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ , config_name=lowercase_)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = GenerationConfig.from_pretrained(lowercase_ , config_name=lowercase_)
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowercase_)
self.assertEqual(loaded_config.temperature , 0.7)
self.assertEqual(loaded_config.length_penalty , 1.0)
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]])
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50)
self.assertEqual(loaded_config.max_length , 20)
self.assertEqual(loaded_config.max_time , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : List[str]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = AutoConfig.from_pretrained('''gpt2''')
SCREAMING_SNAKE_CASE_ : Union[str, Any] = GenerationConfig.from_model_config(lowercase_)
SCREAMING_SNAKE_CASE_ : str = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowercase_ , lowercase_)
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id)
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id)
def _SCREAMING_SNAKE_CASE ( self : int):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[str] = GenerationConfig()
SCREAMING_SNAKE_CASE_ : Tuple = {
'''max_new_tokens''': 1024,
'''foo''': '''bar''',
}
SCREAMING_SNAKE_CASE_ : List[Any] = copy.deepcopy(lowercase_)
SCREAMING_SNAKE_CASE_ : Optional[int] = generation_config.update(**lowercase_)
# update_kwargs was not modified (no side effects)
self.assertEqual(lowercase_ , lowercase_)
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024)
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowercase_ , {'''foo''': '''bar'''})
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[int] = GenerationConfig()
SCREAMING_SNAKE_CASE_ : Optional[int] = '''bar'''
with tempfile.TemporaryDirectory('''test-generation-config''') as tmp_dir:
generation_config.save_pretrained(lowercase_)
SCREAMING_SNAKE_CASE_ : Any = GenerationConfig.from_pretrained(lowercase_)
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , '''bar''')
SCREAMING_SNAKE_CASE_ : Dict = GenerationConfig.from_model_config(lowercase_)
assert not hasattr(lowercase_ , '''foo''') # no new kwargs should be initialized if from config
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0)
self.assertEqual(default_config.do_sample , lowercase_)
self.assertEqual(default_config.num_beams , 1)
SCREAMING_SNAKE_CASE_ : Tuple = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7)
self.assertEqual(config.do_sample , lowercase_)
self.assertEqual(config.num_beams , 1)
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_)
SCREAMING_SNAKE_CASE_ : Any = GenerationConfig.from_pretrained(lowercase_ , temperature=1.0)
self.assertEqual(loaded_config.temperature , 1.0)
self.assertEqual(loaded_config.do_sample , lowercase_)
self.assertEqual(loaded_config.num_beams , 1) # default value
@is_staging_test
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Union[str, Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] = TOKEN
HfFolder.save_token(lowercase_)
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Tuple):
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='''test-generation-config''')
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-generation-config-org''')
except HTTPError:
pass
def _SCREAMING_SNAKE_CASE ( self : int):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''test-generation-config''' , use_auth_token=self._token)
SCREAMING_SNAKE_CASE_ : List[str] = GenerationConfig.from_pretrained(F'{USER}/test-generation-config')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_))
# Reset repo
delete_repo(token=self._token , repo_id='''test-generation-config''')
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id='''test-generation-config''' , push_to_hub=lowercase_ , use_auth_token=self._token)
SCREAMING_SNAKE_CASE_ : Any = GenerationConfig.from_pretrained(F'{USER}/test-generation-config')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_))
def _SCREAMING_SNAKE_CASE ( self : Dict):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''valid_org/test-generation-config-org''' , use_auth_token=self._token)
SCREAMING_SNAKE_CASE_ : int = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_))
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-generation-config-org''')
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id='''valid_org/test-generation-config-org''' , push_to_hub=lowercase_ , use_auth_token=self._token)
SCREAMING_SNAKE_CASE_ : Tuple = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_))
| 318
|
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCAmelCase__ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Any , lowercase_ : Dict[str, int] , lowercase_ : List[str] , lowercase_ : int = None , lowercase_ : int = None):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE_ : str = pad_token_id
SCREAMING_SNAKE_CASE_ : Optional[int] = max_length
SCREAMING_SNAKE_CASE_ : Dict = vocab
SCREAMING_SNAKE_CASE_ : Dict = merges
SCREAMING_SNAKE_CASE_ : Union[str, Any] = BytePairTokenizer(lowercase_ , lowercase_ , sequence_length=lowercase_)
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Dict , lowercase_ : GPTaTokenizer , *lowercase_ : Optional[Any] , **lowercase_ : Tuple):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = [''' '''.join(lowercase_) for m in tokenizer.bpe_ranks.keys()]
SCREAMING_SNAKE_CASE_ : str = tokenizer.get_vocab()
return cls(lowercase_ , lowercase_ , *lowercase_ , **lowercase_)
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : int , lowercase_ : Union[str, os.PathLike] , *lowercase_ : List[str] , **lowercase_ : Optional[int]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Union[str, Any] = GPTaTokenizer.from_pretrained(lowercase_ , *lowercase_ , **lowercase_)
return cls.from_tokenizer(lowercase_ , *lowercase_ , **lowercase_)
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Dict , lowercase_ : List[Any]):
'''simple docstring'''
return cls(**lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def _SCREAMING_SNAKE_CASE ( self : Any , lowercase_ : List[Any] , lowercase_ : int = None):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Dict = self.tf_tokenizer(lowercase_)
SCREAMING_SNAKE_CASE_ : Optional[int] = tf.ones_like(lowercase_)
if self.pad_token_id is not None:
# pad the tokens up to max length
SCREAMING_SNAKE_CASE_ : Union[str, Any] = max_length if max_length is not None else self.max_length
if max_length is not None:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[str] = pad_model_inputs(
lowercase_ , max_seq_length=lowercase_ , pad_value=self.pad_token_id)
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 318
| 1
|
"""simple docstring"""
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
UpperCAmelCase__ = logging.get_logger(__name__)
class a ( lowerCAmelCase_ ):
_snake_case : Union[str, Any] = ['pixel_values']
def __init__( self : Optional[Any] , __lowerCAmelCase : bool = True , __lowerCAmelCase : Optional[Dict[str, int]] = None , __lowerCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , __lowerCAmelCase : bool = True , __lowerCAmelCase : Dict[str, int] = None , __lowerCAmelCase : bool = True , __lowerCAmelCase : Union[int, float] = 1 / 255 , __lowerCAmelCase : bool = True , __lowerCAmelCase : Optional[Union[float, List[float]]] = None , __lowerCAmelCase : Optional[Union[float, List[float]]] = None , **__lowerCAmelCase : Optional[Any] , ):
super().__init__(**lowerCAmelCase__ )
_UpperCAmelCase = size if size is not None else {"""shortest_edge""": 256}
_UpperCAmelCase = get_size_dict(lowerCAmelCase__ , default_to_square=lowerCAmelCase__ )
_UpperCAmelCase = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
_UpperCAmelCase = get_size_dict(lowerCAmelCase__ , param_name="""crop_size""" )
_UpperCAmelCase = do_resize
_UpperCAmelCase = size
_UpperCAmelCase = resample
_UpperCAmelCase = do_center_crop
_UpperCAmelCase = crop_size
_UpperCAmelCase = do_rescale
_UpperCAmelCase = rescale_factor
_UpperCAmelCase = do_normalize
_UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase_ ( self : Tuple , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : Dict[str, int] , __lowerCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , __lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCAmelCase : Optional[Any] , ):
_UpperCAmelCase = get_size_dict(lowerCAmelCase__ , default_to_square=lowerCAmelCase__ )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
_UpperCAmelCase = get_resize_output_image_size(lowerCAmelCase__ , size=size["""shortest_edge"""] , default_to_square=lowerCAmelCase__ )
return resize(lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : Dict[str, int] , __lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCAmelCase : Optional[Any] , ):
_UpperCAmelCase = get_size_dict(lowerCAmelCase__ )
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(lowerCAmelCase__ , size=(size["""height"""], size["""width"""]) , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def lowerCAmelCase_ ( self : Dict , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : float , __lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCAmelCase : Tuple ):
return rescale(lowerCAmelCase__ , scale=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def lowerCAmelCase_ ( self : str , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : Union[float, List[float]] , __lowerCAmelCase : Union[float, List[float]] , __lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCAmelCase : Any , ):
return normalize(lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def lowerCAmelCase_ ( self : Optional[Any] , __lowerCAmelCase : ImageInput , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Dict[str, int] = None , __lowerCAmelCase : PILImageResampling = None , __lowerCAmelCase : bool = None , __lowerCAmelCase : Dict[str, int] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[float] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[Union[float, List[float]]] = None , __lowerCAmelCase : Optional[Union[float, List[float]]] = None , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Union[str, ChannelDimension] = ChannelDimension.FIRST , **__lowerCAmelCase : Optional[Any] , ):
_UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase = size if size is not None else self.size
_UpperCAmelCase = get_size_dict(lowerCAmelCase__ , default_to_square=lowerCAmelCase__ )
_UpperCAmelCase = resample if resample is not None else self.resample
_UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
_UpperCAmelCase = get_size_dict(lowerCAmelCase__ , param_name="""crop_size""" )
_UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase = image_std if image_std is not None else self.image_std
_UpperCAmelCase = make_list_of_images(lowerCAmelCase__ )
if not valid_images(lowerCAmelCase__ ):
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.
_UpperCAmelCase = [to_numpy_array(lowerCAmelCase__ ) for image in images]
if do_resize:
_UpperCAmelCase = [self.resize(image=lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ ) for image in images]
if do_center_crop:
_UpperCAmelCase = [self.center_crop(image=lowerCAmelCase__ , size=lowerCAmelCase__ ) for image in images]
if do_rescale:
_UpperCAmelCase = [self.rescale(image=lowerCAmelCase__ , scale=lowerCAmelCase__ ) for image in images]
if do_normalize:
_UpperCAmelCase = [self.normalize(image=lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ ) for image in images]
_UpperCAmelCase = [to_channel_dimension_format(lowerCAmelCase__ , lowerCAmelCase__ ) for image in images]
_UpperCAmelCase = {"""pixel_values""": images}
return BatchFeature(data=lowerCAmelCase__ , tensor_type=lowerCAmelCase__ )
def lowerCAmelCase_ ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[Tuple] = None ):
_UpperCAmelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(lowerCAmelCase__ ):
_UpperCAmelCase = target_sizes.numpy()
_UpperCAmelCase = []
for idx in range(len(lowerCAmelCase__ ) ):
_UpperCAmelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=lowerCAmelCase__ )
_UpperCAmelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(lowerCAmelCase__ )
else:
_UpperCAmelCase = logits.argmax(dim=1 )
_UpperCAmelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 289
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ : Dict = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
lowercase__ : List[Any] = 25_00_04
lowercase__ : str = 25_00_20
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
_snake_case : Optional[Any] = MBartTokenizer
_snake_case : Tuple = MBartTokenizerFast
_snake_case : List[str] = True
_snake_case : Optional[Any] = True
def snake_case__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCamelCase = MBartTokenizer(lowerCAmelCase__ , keep_accents=lowerCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = MBartTokenizer(lowerCAmelCase__ , keep_accents=lowerCAmelCase__ )
_UpperCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_UpperCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCAmelCase__ , [
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 = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
self.assertListEqual(
lowerCAmelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(lowerCAmelCase__ )
self.assertListEqual(
lowerCAmelCase__ , [
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 snake_case__ ( self : Any ) -> Dict:
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
_UpperCamelCase = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCamelCase = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCamelCase = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCamelCase = tempfile.mkdtemp()
_UpperCamelCase = tokenizer_r.save_pretrained(lowerCAmelCase__ )
_UpperCamelCase = tokenizer_p.save_pretrained(lowerCAmelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
_UpperCamelCase = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Checks everything loads correctly in the same way
_UpperCamelCase = tokenizer_r.from_pretrained(lowerCAmelCase__ )
_UpperCamelCase = tokenizer_p.from_pretrained(lowerCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCAmelCase__ )
# Save tokenizer rust, legacy_format=True
_UpperCamelCase = tempfile.mkdtemp()
_UpperCamelCase = tokenizer_r.save_pretrained(lowerCAmelCase__ , legacy_format=lowerCAmelCase__ )
_UpperCamelCase = tokenizer_p.save_pretrained(lowerCAmelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Checks everything loads correctly in the same way
_UpperCamelCase = tokenizer_r.from_pretrained(lowerCAmelCase__ )
_UpperCamelCase = tokenizer_p.from_pretrained(lowerCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
shutil.rmtree(lowerCAmelCase__ )
# Save tokenizer rust, legacy_format=False
_UpperCamelCase = tempfile.mkdtemp()
_UpperCamelCase = tokenizer_r.save_pretrained(lowerCAmelCase__ , legacy_format=lowerCAmelCase__ )
_UpperCamelCase = tokenizer_p.save_pretrained(lowerCAmelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_UpperCamelCase = tokenizer_r.from_pretrained(lowerCAmelCase__ )
_UpperCamelCase = tokenizer_p.from_pretrained(lowerCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
shutil.rmtree(lowerCAmelCase__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
_snake_case : Dict = 'facebook/mbart-large-en-ro'
_snake_case : Dict = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
_snake_case : List[Any] = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
_snake_case : Union[str, Any] = [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]
@classmethod
def snake_case__ ( cls : List[str] ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en_XX''' , tgt_lang='''ro_RO''' )
_UpperCamelCase = 1
return cls
def snake_case__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] , 250001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] , 250004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] , 250020 )
def snake_case__ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowerCAmelCase__ )
def snake_case__ ( self : str ) -> List[Any]:
'''simple docstring'''
self.assertIn(lowerCAmelCase__ , self.tokenizer.all_special_ids )
_UpperCamelCase = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
_UpperCamelCase = self.tokenizer.decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
_UpperCamelCase = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertNotIn(self.tokenizer.eos_token , lowerCAmelCase__ )
def snake_case__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = ['''this is gunna be a long sentence ''' * 20]
assert isinstance(src_text[0] , lowerCAmelCase__ )
_UpperCamelCase = 10
_UpperCamelCase = self.tokenizer(lowerCAmelCase__ , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , lowerCAmelCase__ )
self.assertEqual(len(lowerCAmelCase__ ) , lowerCAmelCase__ )
def snake_case__ ( self : List[Any] ) -> int:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [250026, 250001] )
def snake_case__ ( self : int ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = tempfile.mkdtemp()
_UpperCamelCase = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowerCAmelCase__ )
_UpperCamelCase = MBartTokenizer.from_pretrained(lowerCAmelCase__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowerCAmelCase__ )
@require_torch
def snake_case__ ( self : Any ) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=lowerCAmelCase__ , return_tensors='''pt''' )
_UpperCamelCase = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def snake_case__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
_UpperCamelCase = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
_UpperCamelCase = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
_UpperCamelCase = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowerCAmelCase__ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def snake_case__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.tokenizer(self.src_text , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=3 , return_tensors='''pt''' )
_UpperCamelCase = self.tokenizer(
text_target=self.tgt_text , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=10 , return_tensors='''pt''' )
_UpperCamelCase = targets['''input_ids''']
_UpperCamelCase = shift_tokens_right(lowerCAmelCase__ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def snake_case__ ( self : Tuple ) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''ar_AR''' )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) , {
# A, test, EOS, en_XX
'''input_ids''': [[62, 3034, 2, 250004]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 250001,
} , )
| 324
| 0
|
"""simple docstring"""
class __lowercase :
"""simple docstring"""
def __init__( self , lowerCAmelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = n
SCREAMING_SNAKE_CASE_ : Tuple = [None] * self.n
SCREAMING_SNAKE_CASE_ : Any = 0 # index of the first element
SCREAMING_SNAKE_CASE_ : Any = 0
SCREAMING_SNAKE_CASE_ : Optional[int] = 0
def __len__( self ):
"""simple docstring"""
return self.size
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self.size == 0
def UpperCamelCase__ ( self ):
"""simple docstring"""
return False if self.is_empty() else self.array[self.front]
def UpperCamelCase__ ( self , lowerCAmelCase__ ):
"""simple docstring"""
if self.size >= self.n:
raise Exception('QUEUE IS FULL' )
SCREAMING_SNAKE_CASE_ : str = data
SCREAMING_SNAKE_CASE_ : Optional[Any] = (self.rear + 1) % self.n
self.size += 1
return self
def UpperCamelCase__ ( self ):
"""simple docstring"""
if self.size == 0:
raise Exception('UNDERFLOW' )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.array[self.front]
SCREAMING_SNAKE_CASE_ : List[Any] = None
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (self.front + 1) % self.n
self.size -= 1
return temp
| 362
|
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __lowercase :
"""simple docstring"""
_UpperCAmelCase = 42
_UpperCAmelCase = None
_UpperCAmelCase = None
def a__ ( ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = Node(1 )
SCREAMING_SNAKE_CASE_ : Any = Node(2 )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = Node(3 )
SCREAMING_SNAKE_CASE_ : int = Node(4 )
SCREAMING_SNAKE_CASE_ : List[str] = Node(5 )
return tree
def a__ ( A__ ):
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def a__ ( A__ ):
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def a__ ( A__ ):
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def a__ ( A__ ):
return (max(height(root.left ), height(root.right ) ) + 1) if root else 0
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : list[Any] = []
if root is None:
return output
SCREAMING_SNAKE_CASE_ : int = deque([root] )
while process_queue:
SCREAMING_SNAKE_CASE_ : List[str] = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def a__ ( A__, A__ ):
SCREAMING_SNAKE_CASE_ : list[Any] = []
def populate_output(A__, A__ ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left, level - 1 )
populate_output(root.right, level - 1 )
populate_output(A__, A__ )
return output
def a__ ( A__, A__ ):
SCREAMING_SNAKE_CASE_ : list[Any] = []
def populate_output(A__, A__ ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right, level - 1 )
populate_output(root.left, level - 1 )
populate_output(A__, A__ )
return output
def a__ ( A__ ):
if root is None:
return []
SCREAMING_SNAKE_CASE_ : list[Sequence[Node | None]] = []
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
SCREAMING_SNAKE_CASE_ : Optional[Any] = height(A__ )
for h in range(1, height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(A__, A__ ) )
SCREAMING_SNAKE_CASE_ : List[Any] = 1
else:
output.append(get_nodes_from_right_to_left(A__, A__ ) )
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
return output
def a__ ( ): # Main function for testing.
SCREAMING_SNAKE_CASE_ : Optional[int] = make_tree()
print(F'''In-order Traversal: {inorder(A__ )}''' )
print(F'''Pre-order Traversal: {preorder(A__ )}''' )
print(F'''Post-order Traversal: {postorder(A__ )}''', '\n' )
print(F'''Height of Tree: {height(A__ )}''', '\n' )
print('Complete Level Order Traversal: ' )
print(level_order(A__ ), '\n' )
print('Level-wise order Traversal: ' )
for level in range(1, height(A__ ) + 1 ):
print(F'''Level {level}:''', get_nodes_from_left_to_right(A__, level=A__ ) )
print('\nZigZag order Traversal: ' )
print(zigzag(A__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 162
| 0
|
import math
import unittest
def _a ( SCREAMING_SNAKE_CASE_ : int ):
assert isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) 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(UpperCAmelCase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class a__ ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(1_1 ) )
self.assertTrue(is_prime(1_3 ) )
self.assertTrue(is_prime(1_7 ) )
self.assertTrue(is_prime(1_9 ) )
self.assertTrue(is_prime(2_3 ) )
self.assertTrue(is_prime(2_9 ) )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
with self.assertRaises(lowercase_ ):
is_prime(-1_9 )
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()
| 92
|
'''simple docstring'''
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin
enable_full_determinism()
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = PriorTransformer
UpperCamelCase__ = '''hidden_states'''
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Optional[Any] = 4
lowercase_ : int = 8
lowercase_ : Union[str, Any] = 7
lowercase_ : List[str] = floats_tensor((batch_size, embedding_dim) ).to(lowercase_ )
lowercase_ : Tuple = floats_tensor((batch_size, embedding_dim) ).to(lowercase_ )
lowercase_ : List[str] = floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(lowercase_ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : Optional[Any]=0 ):
torch.manual_seed(lowercase_ )
lowercase_ : int = 4
lowercase_ : Any = 8
lowercase_ : Tuple = 7
lowercase_ : Union[str, Any] = torch.randn((batch_size, embedding_dim) ).to(lowercase_ )
lowercase_ : Tuple = torch.randn((batch_size, embedding_dim) ).to(lowercase_ )
lowercase_ : Union[str, Any] = torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowercase_ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
return (4, 8)
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return (4, 8)
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Optional[Any] = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 4,
"""num_layers""": 2,
"""embedding_dim""": 8,
"""num_embeddings""": 7,
"""additional_embeddings""": 4,
}
lowercase_ : List[Any] = self.dummy_input
return init_dict, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ , lowercase_ : Tuple = PriorTransformer.from_pretrained(
"""hf-internal-testing/prior-dummy""" , output_loading_info=lowercase_ )
self.assertIsNotNone(lowercase_ )
self.assertEqual(len(loading_info["""missing_keys"""] ) , 0 )
model.to(lowercase_ )
lowercase_ : Optional[int] = model(**self.dummy_input )[0]
assert hidden_states is not None, "Make sure output is not None"
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ , lowercase_ : str = self.prepare_init_args_and_inputs_for_common()
lowercase_ : List[Any] = self.model_class(**lowercase_ )
lowercase_ : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ : Any = [*signature.parameters.keys()]
lowercase_ : Optional[Any] = ["""hidden_states""", """timestep"""]
self.assertListEqual(arg_names[:2] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[Any] = PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy""" )
lowercase_ : Any = model.to(lowercase_ )
if hasattr(lowercase_ , """set_default_attn_processor""" ):
model.set_default_attn_processor()
lowercase_ : Any = self.get_dummy_seed_input()
with torch.no_grad():
lowercase_ : List[str] = model(**lowercase_ )[0]
lowercase_ : Tuple = output[0, :5].flatten().cpu()
print(lowercase_ )
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
lowercase_ : Dict = torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39] )
self.assertTrue(torch_all_close(lowercase_ , lowercase_ , rtol=1E-2 ) )
@slow
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : Union[str, Any]=1 , lowercase_ : Optional[Any]=768 , lowercase_ : Tuple=77 , lowercase_ : Optional[int]=0 ):
torch.manual_seed(lowercase_ )
lowercase_ : Optional[Any] = batch_size
lowercase_ : int = embedding_dim
lowercase_ : int = num_embeddings
lowercase_ : Dict = torch.randn((batch_size, embedding_dim) ).to(lowercase_ )
lowercase_ : Any = torch.randn((batch_size, embedding_dim) ).to(lowercase_ )
lowercase_ : List[Any] = torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowercase_ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def SCREAMING_SNAKE_CASE_ ( self : Any ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@parameterized.expand(
[
# fmt: off
[13, [-0.58_61, 0.12_83, -0.09_31, 0.08_82, 0.44_76, 0.13_29, -0.04_98, 0.06_40]],
[37, [-0.49_13, 0.01_10, -0.04_83, 0.05_41, 0.49_54, -0.01_70, 0.03_54, 0.16_51]],
# fmt: on
] )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : Any , lowercase_ : List[str] ):
lowercase_ : List[Any] = PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior""" , subfolder="""prior""" )
model.to(lowercase_ )
lowercase_ : Optional[Any] = self.get_dummy_seed_input(seed=lowercase_ )
with torch.no_grad():
lowercase_ : Tuple = model(**lowercase_ )[0]
assert list(sample.shape ) == [1, 768]
lowercase_ : Union[str, Any] = sample[0, :8].flatten().cpu()
print(lowercase_ )
lowercase_ : Optional[Any] = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=1E-3 )
| 239
| 0
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__: str = logging.get_logger(__name__)
__magic_name__: Optional[int] = {
"facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class snake_case__ ( _lowerCAmelCase ):
lowercase__ : str = '''levit'''
def __init__( self , lowerCAmelCase__=2_24 , lowerCAmelCase__=3 , lowerCAmelCase__=3 , lowerCAmelCase__=2 , lowerCAmelCase__=1 , lowerCAmelCase__=16 , lowerCAmelCase__=[1_28, 2_56, 3_84] , lowerCAmelCase__=[4, 8, 12] , lowerCAmelCase__=[4, 4, 4] , lowerCAmelCase__=[16, 16, 16] , lowerCAmelCase__=0 , lowerCAmelCase__=[2, 2, 2] , lowerCAmelCase__=[2, 2, 2] , lowerCAmelCase__=0.0_2 , **lowerCAmelCase__ , ) -> Tuple:
super().__init__(**lowerCAmelCase__ )
__magic_name__ : str = image_size
__magic_name__ : int = num_channels
__magic_name__ : Dict = kernel_size
__magic_name__ : int = stride
__magic_name__ : str = padding
__magic_name__ : List[str] = hidden_sizes
__magic_name__ : List[Any] = num_attention_heads
__magic_name__ : Union[str, Any] = depths
__magic_name__ : List[Any] = key_dim
__magic_name__ : Union[str, Any] = drop_path_rate
__magic_name__ : Union[str, Any] = patch_size
__magic_name__ : Tuple = attention_ratio
__magic_name__ : Union[str, Any] = mlp_ratio
__magic_name__ : List[str] = initializer_range
__magic_name__ : Dict = [
["""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],
]
class snake_case__ ( _lowerCAmelCase ):
lowercase__ : Optional[int] = version.parse('''1.11''' )
@property
def __magic_name__ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __magic_name__ ( self ) -> float:
return 1e-4
| 358
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class snake_case__ ( unittest.TestCase ):
def __magic_name__ ( self ) -> int:
__magic_name__ : List[str] = tempfile.mkdtemp()
# fmt: off
__magic_name__ : Union[str, Any] = ["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
__magic_name__ : Any = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
__magic_name__ : int = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
__magic_name__ : Any = {"""unk_token""": """<unk>"""}
__magic_name__ : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
__magic_name__ : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowerCAmelCase__ ) )
__magic_name__ : int = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
"""image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
__magic_name__ : List[str] = os.path.join(self.tmpdirname , lowerCAmelCase__ )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(lowerCAmelCase__ , lowerCAmelCase__ )
def __magic_name__ ( self , **lowerCAmelCase__ ) -> List[str]:
return CLIPTokenizer.from_pretrained(self.tmpdirname , pad_token="""!""" , **lowerCAmelCase__ )
def __magic_name__ ( self , **lowerCAmelCase__ ) -> Any:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , pad_token="""!""" , **lowerCAmelCase__ )
def __magic_name__ ( self , **lowerCAmelCase__ ) -> Optional[Any]:
return OwlViTImageProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def __magic_name__ ( self ) -> int:
shutil.rmtree(self.tmpdirname )
def __magic_name__ ( self ) -> int:
__magic_name__ : str = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
__magic_name__ : Any = [Image.fromarray(np.moveaxis(lowerCAmelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __magic_name__ ( self ) -> Union[str, Any]:
__magic_name__ : Any = self.get_tokenizer()
__magic_name__ : str = self.get_rust_tokenizer()
__magic_name__ : Tuple = self.get_image_processor()
__magic_name__ : List[str] = OwlViTProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
processor_slow.save_pretrained(self.tmpdirname )
__magic_name__ : Optional[int] = OwlViTProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCAmelCase__ )
__magic_name__ : int = OwlViTProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
processor_fast.save_pretrained(self.tmpdirname )
__magic_name__ : List[Any] = OwlViTProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , lowerCAmelCase__ )
self.assertIsInstance(processor_fast.tokenizer , lowerCAmelCase__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , lowerCAmelCase__ )
self.assertIsInstance(processor_fast.image_processor , lowerCAmelCase__ )
def __magic_name__ ( self ) -> Optional[int]:
__magic_name__ : Optional[Any] = OwlViTProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__magic_name__ : str = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
__magic_name__ : Any = self.get_image_processor(do_normalize=lowerCAmelCase__ )
__magic_name__ : Tuple = OwlViTProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=lowerCAmelCase__ )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCAmelCase__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCAmelCase__ )
def __magic_name__ ( self ) -> Dict:
__magic_name__ : int = self.get_image_processor()
__magic_name__ : int = self.get_tokenizer()
__magic_name__ : Union[str, Any] = OwlViTProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
__magic_name__ : Dict = self.prepare_image_inputs()
__magic_name__ : Any = image_processor(lowerCAmelCase__ , return_tensors="""np""" )
__magic_name__ : str = processor(images=lowerCAmelCase__ , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __magic_name__ ( self ) -> Tuple:
__magic_name__ : Union[str, Any] = self.get_image_processor()
__magic_name__ : int = self.get_tokenizer()
__magic_name__ : int = OwlViTProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
__magic_name__ : Optional[int] = """lower newer"""
__magic_name__ : Tuple = processor(text=lowerCAmelCase__ , return_tensors="""np""" )
__magic_name__ : Optional[int] = tokenizer(lowerCAmelCase__ , return_tensors="""np""" )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key][0].tolist() , encoded_processor[key][0].tolist() )
def __magic_name__ ( self ) -> Tuple:
__magic_name__ : Tuple = self.get_image_processor()
__magic_name__ : Union[str, Any] = self.get_tokenizer()
__magic_name__ : List[str] = OwlViTProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
__magic_name__ : Any = """lower newer"""
__magic_name__ : Union[str, Any] = self.prepare_image_inputs()
__magic_name__ : int = processor(text=lowerCAmelCase__ , images=lowerCAmelCase__ )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase__ ):
processor()
def __magic_name__ ( self ) -> Optional[Any]:
__magic_name__ : Dict = """google/owlvit-base-patch32"""
__magic_name__ : int = OwlViTProcessor.from_pretrained(lowerCAmelCase__ )
__magic_name__ : List[Any] = ["""cat""", """nasa badge"""]
__magic_name__ : Any = processor(text=lowerCAmelCase__ )
__magic_name__ : Dict = 16
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask"""] )
self.assertEqual(inputs["""input_ids"""].shape , (2, seq_length) )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase__ ):
processor()
def __magic_name__ ( self ) -> List[Any]:
__magic_name__ : List[str] = """google/owlvit-base-patch32"""
__magic_name__ : Optional[Any] = OwlViTProcessor.from_pretrained(lowerCAmelCase__ )
__magic_name__ : Tuple = [["""cat""", """nasa badge"""], ["""person"""]]
__magic_name__ : Tuple = processor(text=lowerCAmelCase__ )
__magic_name__ : str = 16
__magic_name__ : str = len(lowerCAmelCase__ )
__magic_name__ : int = max([len(lowerCAmelCase__ ) for texts in input_texts] )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask"""] )
self.assertEqual(inputs["""input_ids"""].shape , (batch_size * num_max_text_queries, seq_length) )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase__ ):
processor()
def __magic_name__ ( self ) -> Any:
__magic_name__ : Optional[int] = """google/owlvit-base-patch32"""
__magic_name__ : Any = OwlViTProcessor.from_pretrained(lowerCAmelCase__ )
__magic_name__ : str = ["""cat""", """nasa badge"""]
__magic_name__ : List[str] = processor(text=lowerCAmelCase__ )
__magic_name__ : List[Any] = 16
__magic_name__ : Any = inputs["""input_ids"""]
__magic_name__ : Optional[Any] = [
[4_94_06, 23_68, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[4_94_06, 68_41, 1_13_01, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask"""] )
self.assertEqual(inputs["""input_ids"""].shape , (2, seq_length) )
self.assertListEqual(list(input_ids[0] ) , predicted_ids[0] )
self.assertListEqual(list(input_ids[1] ) , predicted_ids[1] )
def __magic_name__ ( self ) -> Tuple:
__magic_name__ : List[str] = self.get_image_processor()
__magic_name__ : Dict = self.get_tokenizer()
__magic_name__ : Tuple = OwlViTProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
__magic_name__ : Tuple = self.prepare_image_inputs()
__magic_name__ : List[Any] = self.prepare_image_inputs()
__magic_name__ : List[str] = processor(images=lowerCAmelCase__ , query_images=lowerCAmelCase__ )
self.assertListEqual(list(inputs.keys() ) , ["""query_pixel_values""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase__ ):
processor()
def __magic_name__ ( self ) -> Any:
__magic_name__ : Optional[Any] = self.get_image_processor()
__magic_name__ : List[Any] = self.get_tokenizer()
__magic_name__ : Tuple = OwlViTProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
__magic_name__ : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__magic_name__ : Optional[Any] = processor.batch_decode(lowerCAmelCase__ )
__magic_name__ : Optional[int] = tokenizer.batch_decode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
| 138
| 0
|
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowercase_ ( _lowerCamelCase : int):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set())
@pytest.fixture
def lowercase_ ( _lowerCamelCase : Union[str, Any]):
class snake_case_ :
def __init__( self : int , lowercase_ : Dict ) -> Tuple:
lowercase__ : List[Any] = metric_id
class snake_case_ :
__A : Any = [MetricMock(__A ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
def __UpperCamelCase ( self : Tuple ) -> Tuple:
return self._metrics
monkeypatch.setattr("datasets.inspect.huggingface_hub" , HfhMock())
@pytest.mark.parametrize(
"func, args" , [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))])
def lowercase_ ( _lowerCamelCase : List[Any] , _lowerCamelCase : int , _lowerCamelCase : str , _lowerCamelCase : Tuple , _lowerCamelCase : Dict):
if "tmp_path" in args:
lowercase__ : Any = tuple(arg if arg != "tmp_path" else tmp_path for arg in args)
with pytest.warns(_lowerCamelCase , match="https://huggingface.co/docs/evaluate"):
func(*_lowerCamelCase)
| 87
|
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 YolosImageProcessor
class _A( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _A , _A=7 , _A=3 , _A=30 , _A=400 , _A=True , _A=None , _A=True , _A=[0.5, 0.5, 0.5] , _A=[0.5, 0.5, 0.5] , _A=True , _A=1 / 255 , _A=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__A : List[Any] = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
__A : Union[str, Any] = parent
__A : Optional[int] = batch_size
__A : int = num_channels
__A : int = min_resolution
__A : Any = max_resolution
__A : List[Any] = do_resize
__A : List[Any] = size
__A : Union[str, Any] = do_normalize
__A : Optional[int] = image_mean
__A : Optional[int] = image_std
__A : int = do_rescale
__A : str = rescale_factor
__A : Tuple = do_pad
def UpperCAmelCase_ ( self ):
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 UpperCAmelCase_ ( self , _A , _A=False ):
if not batched:
__A : List[str] = image_inputs[0]
if isinstance(_A , Image.Image ):
__A , __A : int = image.size
else:
__A , __A : Any = image.shape[1], image.shape[2]
if w < h:
__A : List[Any] = int(self.size['shortest_edge'] * h / w )
__A : List[Any] = self.size['shortest_edge']
elif w > h:
__A : Union[str, Any] = self.size['shortest_edge']
__A : str = int(self.size['shortest_edge'] * w / h )
else:
__A : Dict = self.size['shortest_edge']
__A : str = self.size['shortest_edge']
else:
__A : int = []
for image in image_inputs:
__A , __A : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__A : List[str] = max(_A , key=lambda _A : item[0] )[0]
__A : str = max(_A , key=lambda _A : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _A( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : List[str] = YolosImageProcessor if is_vision_available() else None
def UpperCAmelCase_ ( self ):
__A : Dict = YolosImageProcessingTester(self )
@property
def UpperCAmelCase_ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase_ ( self ):
__A : 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 UpperCAmelCase_ ( self ):
__A : Tuple = 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 )
__A : 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 UpperCAmelCase_ ( self ):
pass
def UpperCAmelCase_ ( self ):
# Initialize image_processing
__A : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__A : List[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
__A : Any = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__A , __A : Optional[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
__A , __A : Optional[Any] = self.image_processor_tester.get_expected_values(_A , batched=_A )
__A : str = 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 UpperCAmelCase_ ( self ):
# Initialize image_processing
__A : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__A : List[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
__A : str = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__A , __A : List[Any] = 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
__A : Tuple = image_processing(_A , return_tensors='pt' ).pixel_values
__A , __A : Optional[int] = 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 UpperCAmelCase_ ( self ):
# Initialize image_processing
__A : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__A : Dict = 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
__A : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__A , __A : Union[str, Any] = 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
__A : Optional[int] = image_processing(_A , return_tensors='pt' ).pixel_values
__A , __A : Optional[int] = 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 UpperCAmelCase_ ( self ):
# Initialize image_processings
__A : Tuple = self.image_processing_class(**self.image_processor_dict )
__A : Any = self.image_processing_class(do_resize=_A , do_normalize=_A , do_rescale=_A )
# create random PyTorch tensors
__A : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , torchify=_A )
for image in image_inputs:
self.assertIsInstance(_A , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
__A : Optional[int] = image_processing_a.pad(_A , return_tensors='pt' )
__A : Optional[int] = image_processing_a(_A , return_tensors='pt' )
self.assertTrue(
torch.allclose(encoded_images_with_method['pixel_values'] , encoded_images['pixel_values'] , atol=1e-4 ) )
@slow
def UpperCAmelCase_ ( self ):
# prepare image and target
__A : List[Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
__A : Optional[Any] = json.loads(f.read() )
__A : Optional[Any] = {'image_id': 39769, 'annotations': target}
# encode them
__A : str = YolosImageProcessor.from_pretrained('hustvl/yolos-small' )
__A : List[Any] = image_processing(images=_A , annotations=_A , return_tensors='pt' )
# verify pixel values
__A : List[Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , _A )
__A : Union[str, Any] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _A , atol=1e-4 ) )
# verify area
__A : List[Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _A ) )
# verify boxes
__A : Any = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _A )
__A : Optional[Any] = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _A , atol=1e-3 ) )
# verify image_id
__A : Optional[int] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _A ) )
# verify is_crowd
__A : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _A ) )
# verify class_labels
__A : Any = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _A ) )
# verify orig_size
__A : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _A ) )
# verify size
__A : str = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _A ) )
@slow
def UpperCAmelCase_ ( self ):
# prepare image, target and masks_path
__A : Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
__A : Tuple = json.loads(f.read() )
__A : Any = {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}
__A : List[Any] = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
__A : Any = YolosImageProcessor(format='coco_panoptic' )
__A : List[Any] = image_processing(images=_A , annotations=_A , masks_path=_A , return_tensors='pt' )
# verify pixel values
__A : Any = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , _A )
__A : Union[str, Any] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _A , atol=1e-4 ) )
# verify area
__A : int = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _A ) )
# verify boxes
__A : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _A )
__A : Optional[Any] = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _A , atol=1e-3 ) )
# verify image_id
__A : Union[str, Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _A ) )
# verify is_crowd
__A : Tuple = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _A ) )
# verify class_labels
__A : List[str] = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _A ) )
# verify masks
__A : Tuple = 822873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , _A )
# verify orig_size
__A : str = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _A ) )
# verify size
__A : int = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _A ) )
| 280
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : int = {
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''': ['''LukeTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
'''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LukeForEntityClassification''',
'''LukeForEntityPairClassification''',
'''LukeForEntitySpanClassification''',
'''LukeForMultipleChoice''',
'''LukeForQuestionAnswering''',
'''LukeForSequenceClassification''',
'''LukeForTokenClassification''',
'''LukeForMaskedLM''',
'''LukeModel''',
'''LukePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 62
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[int] = {'''vocab_file''': '''sentencepiece.bpe.model'''}
UpperCAmelCase_ : str = {
'''vocab_file''': {
'''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez-orangesum-title''': (
'''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
),
},
}
UpperCAmelCase_ : List[Any] = {
'''moussaKam/mbarthez''': 10_24,
'''moussaKam/barthez''': 10_24,
'''moussaKam/barthez-orangesum-title''': 10_24,
}
UpperCAmelCase_ : List[str] = '''▁'''
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : Optional[int] = VOCAB_FILES_NAMES
snake_case__ : int = PRETRAINED_VOCAB_FILES_MAP
snake_case__ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ : Dict = ["""input_ids""", """attention_mask"""]
def __init__( self : int , __lowerCamelCase : Dict , __lowerCamelCase : List[str]="<s>" , __lowerCamelCase : str="</s>" , __lowerCamelCase : List[str]="</s>" , __lowerCamelCase : Optional[int]="<s>" , __lowerCamelCase : Optional[int]="<unk>" , __lowerCamelCase : Optional[Any]="<pad>" , __lowerCamelCase : str="<mask>" , __lowerCamelCase : Optional[Dict[str, Any]] = None , **__lowerCamelCase : Any , ):
# Mask token behave like a normal word, i.e. include the space before it
UpperCamelCase :int = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else mask_token
UpperCamelCase :int = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCamelCase , )
UpperCamelCase :Union[str, Any] = vocab_file
UpperCamelCase :List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__lowerCamelCase ) )
UpperCamelCase :Optional[int] = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
UpperCamelCase :Tuple = len(self.sp_model ) - 1
UpperCamelCase :List[str] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def _A ( self : Optional[Any] , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCamelCase :Any = [self.cls_token_id]
UpperCamelCase :Dict = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _A ( self : Dict , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None , __lowerCamelCase : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCamelCase , token_ids_a=__lowerCamelCase , already_has_special_tokens=__lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCamelCase )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1, 1] + ([0] * len(__lowerCamelCase )) + [1]
def _A ( self : int , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
UpperCamelCase :Any = [self.sep_token_id]
UpperCamelCase :int = [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[Any] ):
return len(self.sp_model )
def _A ( self : Any ):
UpperCamelCase :Optional[int] = {self.convert_ids_to_tokens(__lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _A ( self : int , __lowerCamelCase : str ):
return self.sp_model.encode(__lowerCamelCase , out_type=__lowerCamelCase )
def _A ( self : Dict , __lowerCamelCase : Optional[int] ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCamelCase :List[Any] = self.sp_model.PieceToId(__lowerCamelCase )
return spm_id if spm_id else self.unk_token_id
def _A ( self : Union[str, Any] , __lowerCamelCase : Union[str, Any] ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(__lowerCamelCase )
def _A ( self : Optional[int] , __lowerCamelCase : Union[str, Any] ):
UpperCamelCase :List[Any] = []
UpperCamelCase :str = """"""
UpperCamelCase :Optional[int] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__lowerCamelCase ) + token
UpperCamelCase :List[str] = True
UpperCamelCase :Dict = []
else:
current_sub_tokens.append(__lowerCamelCase )
UpperCamelCase :Optional[Any] = False
out_string += self.sp_model.decode(__lowerCamelCase )
return out_string.strip()
def __getstate__( self : str ):
UpperCamelCase :Tuple = self.__dict__.copy()
UpperCamelCase :str = None
return state
def __setstate__( self : Tuple , __lowerCamelCase : Optional[int] ):
UpperCamelCase :Any = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCamelCase :Any = {}
UpperCamelCase :str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _A ( self : str , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
if not os.path.isdir(__lowerCamelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCamelCase :Union[str, Any] = os.path.join(
__lowerCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCamelCase , """wb""" ) as fi:
UpperCamelCase :List[str] = self.sp_model.serialized_model_proto()
fi.write(__lowerCamelCase )
return (out_vocab_file,)
| 62
| 1
|
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class __lowercase ( unittest.TestCase ):
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Dict = tempfile.mkdtemp()
lowerCamelCase_ : int = BlipImageProcessor()
lowerCamelCase_ : List[Any] = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''' )
lowerCamelCase_ : List[Any] = BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
lowerCamelCase_ : Union[str, Any] = InstructBlipProcessor(A , A , A )
processor.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ (self , **A ):
return AutoProcessor.from_pretrained(self.tmpdirname , **A ).tokenizer
def UpperCAmelCase__ (self , **A ):
return AutoProcessor.from_pretrained(self.tmpdirname , **A ).image_processor
def UpperCAmelCase__ (self , **A ):
return AutoProcessor.from_pretrained(self.tmpdirname , **A ).qformer_tokenizer
def UpperCAmelCase__ (self ):
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : List[Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
lowerCamelCase_ : Union[str, Any] = [Image.fromarray(np.moveaxis(A , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Any = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ : str = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
lowerCamelCase_ : int = self.get_image_processor(do_normalize=A , padding_value=1.0 )
lowerCamelCase_ : Dict = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A )
self.assertIsInstance(processor.qformer_tokenizer , A )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Optional[int] = self.get_image_processor()
lowerCamelCase_ : str = self.get_tokenizer()
lowerCamelCase_ : str = self.get_qformer_tokenizer()
lowerCamelCase_ : Optional[Any] = InstructBlipProcessor(
tokenizer=A , image_processor=A , qformer_tokenizer=A )
lowerCamelCase_ : Union[str, Any] = self.prepare_image_inputs()
lowerCamelCase_ : Optional[int] = image_processor(A , return_tensors='''np''' )
lowerCamelCase_ : Tuple = processor(images=A , 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 ):
lowerCamelCase_ : Union[str, Any] = self.get_image_processor()
lowerCamelCase_ : Union[str, Any] = self.get_tokenizer()
lowerCamelCase_ : str = self.get_qformer_tokenizer()
lowerCamelCase_ : Any = InstructBlipProcessor(
tokenizer=A , image_processor=A , qformer_tokenizer=A )
lowerCamelCase_ : Union[str, Any] = '''lower newer'''
lowerCamelCase_ : Union[str, Any] = processor(text=A )
lowerCamelCase_ : List[Any] = tokenizer(A , return_token_type_ids=A )
lowerCamelCase_ : str = qformer_tokenizer(A , return_token_type_ids=A )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor['''qformer_''' + key] )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Union[str, Any] = self.get_image_processor()
lowerCamelCase_ : Dict = self.get_tokenizer()
lowerCamelCase_ : Tuple = self.get_qformer_tokenizer()
lowerCamelCase_ : Optional[Any] = InstructBlipProcessor(
tokenizer=A , image_processor=A , qformer_tokenizer=A )
lowerCamelCase_ : Any = '''lower newer'''
lowerCamelCase_ : int = self.prepare_image_inputs()
lowerCamelCase_ : Optional[Any] = processor(text=A , images=A )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
# test if it raises when no input is passed
with pytest.raises(A ):
processor()
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Dict = self.get_image_processor()
lowerCamelCase_ : Dict = self.get_tokenizer()
lowerCamelCase_ : List[str] = self.get_qformer_tokenizer()
lowerCamelCase_ : List[str] = InstructBlipProcessor(
tokenizer=A , image_processor=A , qformer_tokenizer=A )
lowerCamelCase_ : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCamelCase_ : Optional[int] = processor.batch_decode(A )
lowerCamelCase_ : int = tokenizer.batch_decode(A )
self.assertListEqual(A , A )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : List[Any] = self.get_image_processor()
lowerCamelCase_ : List[Any] = self.get_tokenizer()
lowerCamelCase_ : List[str] = self.get_qformer_tokenizer()
lowerCamelCase_ : Union[str, Any] = InstructBlipProcessor(
tokenizer=A , image_processor=A , qformer_tokenizer=A )
lowerCamelCase_ : Optional[int] = '''lower newer'''
lowerCamelCase_ : Tuple = self.prepare_image_inputs()
lowerCamelCase_ : Optional[int] = processor(text=A , images=A )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
| 318
|
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
__lowercase : Any = logging.get_logger(__name__)
__lowercase : Any = OrderedDict(
[
('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
('''beit''', '''BeitFeatureExtractor'''),
('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
('''clap''', '''ClapFeatureExtractor'''),
('''clip''', '''CLIPFeatureExtractor'''),
('''clipseg''', '''ViTFeatureExtractor'''),
('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
('''convnext''', '''ConvNextFeatureExtractor'''),
('''cvt''', '''ConvNextFeatureExtractor'''),
('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
('''data2vec-vision''', '''BeitFeatureExtractor'''),
('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
('''deit''', '''DeiTFeatureExtractor'''),
('''detr''', '''DetrFeatureExtractor'''),
('''dinat''', '''ViTFeatureExtractor'''),
('''donut-swin''', '''DonutFeatureExtractor'''),
('''dpt''', '''DPTFeatureExtractor'''),
('''encodec''', '''EncodecFeatureExtractor'''),
('''flava''', '''FlavaFeatureExtractor'''),
('''glpn''', '''GLPNFeatureExtractor'''),
('''groupvit''', '''CLIPFeatureExtractor'''),
('''hubert''', '''Wav2Vec2FeatureExtractor'''),
('''imagegpt''', '''ImageGPTFeatureExtractor'''),
('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
('''levit''', '''LevitFeatureExtractor'''),
('''maskformer''', '''MaskFormerFeatureExtractor'''),
('''mctct''', '''MCTCTFeatureExtractor'''),
('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
('''mobilevit''', '''MobileViTFeatureExtractor'''),
('''nat''', '''ViTFeatureExtractor'''),
('''owlvit''', '''OwlViTFeatureExtractor'''),
('''perceiver''', '''PerceiverFeatureExtractor'''),
('''poolformer''', '''PoolFormerFeatureExtractor'''),
('''regnet''', '''ConvNextFeatureExtractor'''),
('''resnet''', '''ConvNextFeatureExtractor'''),
('''segformer''', '''SegformerFeatureExtractor'''),
('''sew''', '''Wav2Vec2FeatureExtractor'''),
('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
('''speecht5''', '''SpeechT5FeatureExtractor'''),
('''swiftformer''', '''ViTFeatureExtractor'''),
('''swin''', '''ViTFeatureExtractor'''),
('''swinv2''', '''ViTFeatureExtractor'''),
('''table-transformer''', '''DetrFeatureExtractor'''),
('''timesformer''', '''VideoMAEFeatureExtractor'''),
('''tvlt''', '''TvltFeatureExtractor'''),
('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
('''van''', '''ConvNextFeatureExtractor'''),
('''videomae''', '''VideoMAEFeatureExtractor'''),
('''vilt''', '''ViltFeatureExtractor'''),
('''vit''', '''ViTFeatureExtractor'''),
('''vit_mae''', '''ViTFeatureExtractor'''),
('''vit_msn''', '''ViTFeatureExtractor'''),
('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
('''whisper''', '''WhisperFeatureExtractor'''),
('''xclip''', '''CLIPFeatureExtractor'''),
('''yolos''', '''YolosFeatureExtractor'''),
]
)
__lowercase : List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def lowercase_ ( _lowercase ) -> List[Any]:
'''simple docstring'''
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
lowerCamelCase_ : Dict = model_type_to_module_name(_lowercase )
lowerCamelCase_ : Any = importlib.import_module(F""".{module_name}""" , '''transformers.models''' )
try:
return getattr(_lowercase , _lowercase )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(_lowercase , '''__name__''' , _lowercase ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
lowerCamelCase_ : Optional[Any] = importlib.import_module('''transformers''' )
if hasattr(_lowercase , _lowercase ):
return getattr(_lowercase , _lowercase )
return None
def lowercase_ ( _lowercase , _lowercase = None , _lowercase = False , _lowercase = False , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = False , **_lowercase , ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ : Optional[int] = get_file_from_repo(
_lowercase , _lowercase , cache_dir=_lowercase , force_download=_lowercase , resume_download=_lowercase , proxies=_lowercase , use_auth_token=_lowercase , revision=_lowercase , local_files_only=_lowercase , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(_lowercase , encoding='''utf-8''' ) as reader:
return json.load(_lowercase )
class __lowercase :
def __init__(self ):
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(A )
def UpperCAmelCase__ (cls , A , **A ):
lowerCamelCase_ : Optional[Any] = kwargs.pop('''config''' , A )
lowerCamelCase_ : Union[str, Any] = kwargs.pop('''trust_remote_code''' , A )
lowerCamelCase_ : List[Any] = True
lowerCamelCase_, lowerCamelCase_ : List[Any] = FeatureExtractionMixin.get_feature_extractor_dict(A , **A )
lowerCamelCase_ : Tuple = config_dict.get('''feature_extractor_type''' , A )
lowerCamelCase_ : List[Any] = None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''' , {} ):
lowerCamelCase_ : Optional[Any] = config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(A , A ):
lowerCamelCase_ : List[str] = AutoConfig.from_pretrained(A , **A )
# It could be in `config.feature_extractor_type``
lowerCamelCase_ : Union[str, Any] = getattr(A , '''feature_extractor_type''' , A )
if hasattr(A , '''auto_map''' ) and "AutoFeatureExtractor" in config.auto_map:
lowerCamelCase_ : Optional[int] = config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
lowerCamelCase_ : Any = feature_extractor_class_from_name(A )
lowerCamelCase_ : Optional[int] = feature_extractor_auto_map is not None
lowerCamelCase_ : Optional[Any] = feature_extractor_class is not None or type(A ) in FEATURE_EXTRACTOR_MAPPING
lowerCamelCase_ : int = resolve_trust_remote_code(
A , A , A , A )
if has_remote_code and trust_remote_code:
lowerCamelCase_ : Any = get_class_from_dynamic_module(
A , A , **A )
lowerCamelCase_ : List[Any] = kwargs.pop('''code_revision''' , A )
if os.path.isdir(A ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(A , **A )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(A , **A )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(A ) in FEATURE_EXTRACTOR_MAPPING:
lowerCamelCase_ : Optional[int] = FEATURE_EXTRACTOR_MAPPING[type(A )]
return feature_extractor_class.from_dict(A , **A )
raise ValueError(
F"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
F"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
F"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}""" )
@staticmethod
def UpperCAmelCase__ (A , A ):
FEATURE_EXTRACTOR_MAPPING.register(A , A )
| 318
| 1
|
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def a__ ( lowercase : List[str], lowercase : Union[str, Any], lowercase : List[Any] ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = OmegaConf.load(lowercase )
_UpperCamelCase = torch.load(lowercase, map_location='''cpu''' )['''model''']
_UpperCamelCase = list(state_dict.keys() )
# extract state_dict for VQVAE
_UpperCamelCase = {}
_UpperCamelCase = '''first_stage_model.'''
for key in keys:
if key.startswith(lowercase ):
_UpperCamelCase = state_dict[key]
# extract state_dict for UNetLDM
_UpperCamelCase = {}
_UpperCamelCase = '''model.diffusion_model.'''
for key in keys:
if key.startswith(lowercase ):
_UpperCamelCase = state_dict[key]
_UpperCamelCase = config.model.params.first_stage_config.params
_UpperCamelCase = config.model.params.unet_config.params
_UpperCamelCase = VQModel(**lowercase ).eval()
vqvae.load_state_dict(lowercase )
_UpperCamelCase = UNetLDMModel(**lowercase ).eval()
unet.load_state_dict(lowercase )
_UpperCamelCase = DDIMScheduler(
timesteps=config.model.params.timesteps, beta_schedule='''scaled_linear''', beta_start=config.model.params.linear_start, beta_end=config.model.params.linear_end, clip_sample=lowercase, )
_UpperCamelCase = LDMPipeline(lowercase, lowercase, lowercase )
pipeline.save_pretrained(lowercase )
if __name__ == "__main__":
lowercase__ : List[str] = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path', type=str, required=True)
parser.add_argument('--config_path', type=str, required=True)
parser.add_argument('--output_path', type=str, required=True)
lowercase__ : Dict = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 362
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[Any] = logging.get_logger(__name__)
lowercase__ : Tuple = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : int = 'trocr'
_snake_case : List[str] = ['past_key_values']
_snake_case : Tuple = {
'num_attention_heads': 'decoder_attention_heads',
'hidden_size': 'd_model',
'num_hidden_layers': 'decoder_layers',
}
def __init__( self : Tuple , lowerCAmelCase__ : Union[str, Any]=50265 , lowerCAmelCase__ : List[Any]=1024 , lowerCAmelCase__ : List[str]=12 , lowerCAmelCase__ : int=16 , lowerCAmelCase__ : List[Any]=4096 , lowerCAmelCase__ : Optional[int]="gelu" , lowerCAmelCase__ : int=512 , lowerCAmelCase__ : Optional[Any]=0.1 , lowerCAmelCase__ : Optional[int]=0.0 , lowerCAmelCase__ : Dict=0.0 , lowerCAmelCase__ : Tuple=2 , lowerCAmelCase__ : Dict=0.02 , lowerCAmelCase__ : Tuple=0.0 , lowerCAmelCase__ : int=True , lowerCAmelCase__ : Optional[Any]=False , lowerCAmelCase__ : Any=True , lowerCAmelCase__ : Dict=True , lowerCAmelCase__ : Optional[int]=1 , lowerCAmelCase__ : List[Any]=0 , lowerCAmelCase__ : Any=2 , **lowerCAmelCase__ : Optional[int] , ) -> Any:
'''simple docstring'''
_UpperCamelCase = vocab_size
_UpperCamelCase = d_model
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = activation_function
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = init_std
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = use_cache
_UpperCamelCase = scale_embedding
_UpperCamelCase = use_learned_position_embeddings
_UpperCamelCase = layernorm_embedding
super().__init__(
pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , decoder_start_token_id=lowerCAmelCase__ , **lowerCAmelCase__ , )
| 287
| 0
|
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :Optional[int] , lowerCAmelCase__ :int , lowerCAmelCase__ :int , lowerCAmelCase__ :int , lowerCAmelCase__ :List[str]=0.0 , lowerCAmelCase__ :Optional[int] = None , lowerCAmelCase__ :str = "geglu" , lowerCAmelCase__ :Optional[int] = None , lowerCAmelCase__ :bool = False , lowerCAmelCase__ :bool = False , lowerCAmelCase__ :bool = False , lowerCAmelCase__ :bool = False , lowerCAmelCase__ :bool = True , lowerCAmelCase__ :str = "layer_norm" , lowerCAmelCase__ :bool = False , ) -> Tuple:
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[Any] = only_cross_attention
__SCREAMING_SNAKE_CASE : int = (num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
__SCREAMING_SNAKE_CASE : List[str] = (num_embeds_ada_norm is not None) and norm_type == '''ada_norm'''
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.''' )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
__SCREAMING_SNAKE_CASE : Dict = AdaLayerNorm(lowerCAmelCase__ , lowerCAmelCase__ )
elif self.use_ada_layer_norm_zero:
__SCREAMING_SNAKE_CASE : Union[str, Any] = AdaLayerNormZero(lowerCAmelCase__ , lowerCAmelCase__ )
else:
__SCREAMING_SNAKE_CASE : Dict = nn.LayerNorm(lowerCAmelCase__ , elementwise_affine=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = Attention(
query_dim=lowerCAmelCase__ , heads=lowerCAmelCase__ , dim_head=lowerCAmelCase__ , dropout=lowerCAmelCase__ , bias=lowerCAmelCase__ , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=lowerCAmelCase__ , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
__SCREAMING_SNAKE_CASE : Optional[int] = (
AdaLayerNorm(lowerCAmelCase__ , lowerCAmelCase__ )
if self.use_ada_layer_norm
else nn.LayerNorm(lowerCAmelCase__ , elementwise_affine=lowerCAmelCase__ )
)
__SCREAMING_SNAKE_CASE : Dict = Attention(
query_dim=lowerCAmelCase__ , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=lowerCAmelCase__ , dim_head=lowerCAmelCase__ , dropout=lowerCAmelCase__ , bias=lowerCAmelCase__ , upcast_attention=lowerCAmelCase__ , ) # is self-attn if encoder_hidden_states is none
else:
__SCREAMING_SNAKE_CASE : Dict = None
__SCREAMING_SNAKE_CASE : Dict = None
# 3. Feed-forward
__SCREAMING_SNAKE_CASE : Tuple = nn.LayerNorm(lowerCAmelCase__ , elementwise_affine=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = FeedForward(lowerCAmelCase__ , dropout=lowerCAmelCase__ , activation_fn=lowerCAmelCase__ , final_dropout=lowerCAmelCase__ )
# let chunk size default to None
__SCREAMING_SNAKE_CASE : int = None
__SCREAMING_SNAKE_CASE : int = 0
def __magic_name__( self :List[str] , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :int ) -> Union[str, Any]:
# Sets chunk feed-forward
__SCREAMING_SNAKE_CASE : Tuple = chunk_size
__SCREAMING_SNAKE_CASE : str = dim
def __magic_name__( self :Tuple , lowerCAmelCase__ :torch.FloatTensor , lowerCAmelCase__ :Optional[torch.FloatTensor] = None , lowerCAmelCase__ :Optional[torch.FloatTensor] = None , lowerCAmelCase__ :Optional[torch.FloatTensor] = None , lowerCAmelCase__ :Optional[torch.LongTensor] = None , lowerCAmelCase__ :Dict[str, Any] = None , lowerCAmelCase__ :Optional[torch.LongTensor] = None , ) -> List[Any]:
# Notice that normalization is always applied before the real computation in the following blocks.
# 1. Self-Attention
if self.use_ada_layer_norm:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.norma(lowerCAmelCase__ , lowerCAmelCase__ )
elif self.use_ada_layer_norm_zero:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.norma(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , hidden_dtype=hidden_states.dtype )
else:
__SCREAMING_SNAKE_CASE : List[Any] = self.norma(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : int = cross_attention_kwargs if cross_attention_kwargs is not None else {}
__SCREAMING_SNAKE_CASE : Any = self.attna(
lowerCAmelCase__ , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=lowerCAmelCase__ , **lowerCAmelCase__ , )
if self.use_ada_layer_norm_zero:
__SCREAMING_SNAKE_CASE : List[str] = gate_msa.unsqueeze(1 ) * attn_output
__SCREAMING_SNAKE_CASE : Union[str, Any] = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
__SCREAMING_SNAKE_CASE : Optional[Any] = (
self.norma(lowerCAmelCase__ , lowerCAmelCase__ ) if self.use_ada_layer_norm else self.norma(lowerCAmelCase__ )
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.attna(
lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , **lowerCAmelCase__ , )
__SCREAMING_SNAKE_CASE : str = attn_output + hidden_states
# 3. Feed-forward
__SCREAMING_SNAKE_CASE : int = self.norma(lowerCAmelCase__ )
if self.use_ada_layer_norm_zero:
__SCREAMING_SNAKE_CASE : Optional[Any] = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.''' )
__SCREAMING_SNAKE_CASE : List[str] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cat(
[self.ff(lowerCAmelCase__ ) for hid_slice in norm_hidden_states.chunk(lowerCAmelCase__ , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
__SCREAMING_SNAKE_CASE : int = self.ff(lowerCAmelCase__ )
if self.use_ada_layer_norm_zero:
__SCREAMING_SNAKE_CASE : Union[str, Any] = gate_mlp.unsqueeze(1 ) * ff_output
__SCREAMING_SNAKE_CASE : Tuple = ff_output + hidden_states
return hidden_states
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :int , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[int] = None , lowerCAmelCase__ :int = 4 , lowerCAmelCase__ :float = 0.0 , lowerCAmelCase__ :str = "geglu" , lowerCAmelCase__ :bool = False , ) -> Optional[Any]:
super().__init__()
__SCREAMING_SNAKE_CASE : List[Any] = int(dim * mult )
__SCREAMING_SNAKE_CASE : Optional[Any] = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
__SCREAMING_SNAKE_CASE : Tuple = GELU(lowerCAmelCase__ , lowerCAmelCase__ )
if activation_fn == "gelu-approximate":
__SCREAMING_SNAKE_CASE : int = GELU(lowerCAmelCase__ , lowerCAmelCase__ , approximate='''tanh''' )
elif activation_fn == "geglu":
__SCREAMING_SNAKE_CASE : List[str] = GEGLU(lowerCAmelCase__ , lowerCAmelCase__ )
elif activation_fn == "geglu-approximate":
__SCREAMING_SNAKE_CASE : int = ApproximateGELU(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = nn.ModuleList([] )
# project in
self.net.append(lowerCAmelCase__ )
# project dropout
self.net.append(nn.Dropout(lowerCAmelCase__ ) )
# project out
self.net.append(nn.Linear(lowerCAmelCase__ , lowerCAmelCase__ ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(lowerCAmelCase__ ) )
def __magic_name__( self :Dict , lowerCAmelCase__ :int ) -> Any:
for module in self.net:
__SCREAMING_SNAKE_CASE : str = module(lowerCAmelCase__ )
return hidden_states
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :List[Any] , lowerCAmelCase__ :int , lowerCAmelCase__ :int , lowerCAmelCase__ :str = "none" ) -> Optional[int]:
super().__init__()
__SCREAMING_SNAKE_CASE : Tuple = nn.Linear(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = approximate
def __magic_name__( self :int , lowerCAmelCase__ :Optional[int] ) -> Tuple:
if gate.device.type != "mps":
return F.gelu(lowerCAmelCase__ , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def __magic_name__( self :int , lowerCAmelCase__ :Union[str, Any] ) -> int:
__SCREAMING_SNAKE_CASE : Tuple = self.proj(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = self.gelu(lowerCAmelCase__ )
return hidden_states
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :Dict , lowerCAmelCase__ :int , lowerCAmelCase__ :int ) -> Optional[Any]:
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = nn.Linear(lowerCAmelCase__ , dim_out * 2 )
def __magic_name__( self :List[Any] , lowerCAmelCase__ :List[Any] ) -> Optional[Any]:
if gate.device.type != "mps":
return F.gelu(lowerCAmelCase__ )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def __magic_name__( self :Tuple , lowerCAmelCase__ :List[Any] ) -> Dict:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = self.proj(lowerCAmelCase__ ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(lowerCAmelCase__ )
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :List[str] , lowerCAmelCase__ :int , lowerCAmelCase__ :int ) -> Tuple:
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = nn.Linear(lowerCAmelCase__ , lowerCAmelCase__ )
def __magic_name__( self :Tuple , lowerCAmelCase__ :List[Any] ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE : Dict = self.proj(lowerCAmelCase__ )
return x * torch.sigmoid(1.702 * x )
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :Any , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Dict ) -> Any:
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = nn.Embedding(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = nn.SiLU()
__SCREAMING_SNAKE_CASE : Any = nn.Linear(lowerCAmelCase__ , embedding_dim * 2 )
__SCREAMING_SNAKE_CASE : Optional[Any] = nn.LayerNorm(lowerCAmelCase__ , elementwise_affine=lowerCAmelCase__ )
def __magic_name__( self :Any , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Dict ) -> Any:
__SCREAMING_SNAKE_CASE : Any = self.linear(self.silu(self.emb(lowerCAmelCase__ ) ) )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = torch.chunk(lowerCAmelCase__ , 2 )
__SCREAMING_SNAKE_CASE : str = self.norm(lowerCAmelCase__ ) * (1 + scale) + shift
return x
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :Any , lowerCAmelCase__ :Any , lowerCAmelCase__ :str ) -> Dict:
super().__init__()
__SCREAMING_SNAKE_CASE : List[Any] = CombinedTimestepLabelEmbeddings(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = nn.SiLU()
__SCREAMING_SNAKE_CASE : int = nn.Linear(lowerCAmelCase__ , 6 * embedding_dim , bias=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : int = nn.LayerNorm(lowerCAmelCase__ , elementwise_affine=lowerCAmelCase__ , eps=1E-6 )
def __magic_name__( self :Dict , lowerCAmelCase__ :Any , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[Any]=None ) -> List[Any]:
__SCREAMING_SNAKE_CASE : Optional[int] = self.linear(self.silu(self.emb(lowerCAmelCase__ , lowerCAmelCase__ , hidden_dtype=lowerCAmelCase__ ) ) )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = emb.chunk(6 , dim=1 )
__SCREAMING_SNAKE_CASE : Optional[int] = self.norm(lowerCAmelCase__ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :int , lowerCAmelCase__ :int , lowerCAmelCase__ :int , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[str] = None , lowerCAmelCase__ :float = 1E-5 ) -> Tuple:
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = num_groups
__SCREAMING_SNAKE_CASE : Optional[Any] = eps
if act_fn is None:
__SCREAMING_SNAKE_CASE : Optional[int] = None
else:
__SCREAMING_SNAKE_CASE : str = get_activation(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Linear(lowerCAmelCase__ , out_dim * 2 )
def __magic_name__( self :int , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[Any] ) -> Optional[int]:
if self.act:
__SCREAMING_SNAKE_CASE : Dict = self.act(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = self.linear(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = emb[:, :, None, None]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = emb.chunk(2 , dim=1 )
__SCREAMING_SNAKE_CASE : Tuple = F.group_norm(lowerCAmelCase__ , self.num_groups , eps=self.eps )
__SCREAMING_SNAKE_CASE : List[Any] = x * (1 + scale) + shift
return x
| 9
|
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A__ :
lowercase = 42
lowercase = None
lowercase = None
def UpperCAmelCase__ ( ) -> Node | None:
A_ = Node(1 )
A_ = Node(2 )
A_ = Node(3 )
A_ = Node(4 )
A_ = Node(5 )
return tree
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> list[int]:
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> list[int]:
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> list[int]:
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
return (max(height(root.left ), height(root.right ) ) + 1) if root else 0
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Sequence[Node | None]:
A_ = []
if root is None:
return output
A_ = deque([root] )
while process_queue:
A_ = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Sequence[Node | None]:
A_ = []
def populate_output(UpperCAmelCase__, UpperCAmelCase__ ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left, level - 1 )
populate_output(root.right, level - 1 )
populate_output(UpperCAmelCase__, UpperCAmelCase__ )
return output
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Sequence[Node | None]:
A_ = []
def populate_output(UpperCAmelCase__, UpperCAmelCase__ ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right, level - 1 )
populate_output(root.left, level - 1 )
populate_output(UpperCAmelCase__, UpperCAmelCase__ )
return output
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Sequence[Node | None] | list[Any]:
if root is None:
return []
A_ = []
A_ = 0
A_ = height(UpperCAmelCase__ )
for h in range(1, height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(UpperCAmelCase__, UpperCAmelCase__ ) )
A_ = 1
else:
output.append(get_nodes_from_right_to_left(UpperCAmelCase__, UpperCAmelCase__ ) )
A_ = 0
return output
def UpperCAmelCase__ ( ) -> None: # Main function for testing.
A_ = make_tree()
print(F'''In-order Traversal: {inorder(UpperCAmelCase__ )}''' )
print(F'''Pre-order Traversal: {preorder(UpperCAmelCase__ )}''' )
print(F'''Post-order Traversal: {postorder(UpperCAmelCase__ )}''', """\n""" )
print(F'''Height of Tree: {height(UpperCAmelCase__ )}''', """\n""" )
print("""Complete Level Order Traversal: """ )
print(level_order(UpperCAmelCase__ ), """\n""" )
print("""Level-wise order Traversal: """ )
for level in range(1, height(UpperCAmelCase__ ) + 1 ):
print(F'''Level {level}:''', get_nodes_from_left_to_right(UpperCAmelCase__, level=UpperCAmelCase__ ) )
print("""\nZigZag order Traversal: """ )
print(zigzag(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 162
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
a_ = logging.get_logger(__name__)
a_ = {
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """marian"""
snake_case_ = ["""past_key_values"""]
snake_case_ = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : Optional[Any] , __lowercase : List[str]=5_81_01 , __lowercase : Optional[int]=None , __lowercase : Tuple=10_24 , __lowercase : Union[str, Any]=12 , __lowercase : List[Any]=40_96 , __lowercase : Optional[int]=16 , __lowercase : List[str]=12 , __lowercase : str=40_96 , __lowercase : Optional[Any]=16 , __lowercase : List[str]=0.0 , __lowercase : List[str]=0.0 , __lowercase : Optional[Any]=True , __lowercase : List[str]=True , __lowercase : Optional[Any]="gelu" , __lowercase : int=10_24 , __lowercase : Optional[Any]=0.1 , __lowercase : int=0.0 , __lowercase : List[str]=0.0 , __lowercase : int=0.02 , __lowercase : List[str]=5_81_00 , __lowercase : int=False , __lowercase : str=5_81_00 , __lowercase : List[str]=0 , __lowercase : Union[str, Any]=0 , __lowercase : str=True , **__lowercase : Union[str, Any] , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =decoder_vocab_size or vocab_size
SCREAMING_SNAKE_CASE__ : str =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Union[str, Any] =d_model
SCREAMING_SNAKE_CASE__ : List[str] =encoder_ffn_dim
SCREAMING_SNAKE_CASE__ : Optional[Any] =encoder_layers
SCREAMING_SNAKE_CASE__ : Optional[Any] =encoder_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] =decoder_ffn_dim
SCREAMING_SNAKE_CASE__ : Union[str, Any] =decoder_layers
SCREAMING_SNAKE_CASE__ : int =decoder_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[Any] =dropout
SCREAMING_SNAKE_CASE__ : List[str] =attention_dropout
SCREAMING_SNAKE_CASE__ : int =activation_dropout
SCREAMING_SNAKE_CASE__ : List[Any] =activation_function
SCREAMING_SNAKE_CASE__ : int =init_std
SCREAMING_SNAKE_CASE__ : Optional[Any] =encoder_layerdrop
SCREAMING_SNAKE_CASE__ : Tuple =decoder_layerdrop
SCREAMING_SNAKE_CASE__ : Tuple =use_cache
SCREAMING_SNAKE_CASE__ : str =encoder_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] =scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE__ : Dict =share_encoder_decoder_embeddings
super().__init__(
pad_token_id=__lowercase , eos_token_id=__lowercase , is_encoder_decoder=__lowercase , decoder_start_token_id=__lowercase , forced_eos_token_id=__lowercase , **__lowercase , )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def __magic_name__ ( self : int ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE__ : List[Any] =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE__ : Optional[int] ={0: '''batch'''}
SCREAMING_SNAKE_CASE__ : List[Any] ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
SCREAMING_SNAKE_CASE__ : Any ={0: '''batch''', 1: '''decoder_sequence'''}
SCREAMING_SNAKE_CASE__ : Tuple ={0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(__lowercase , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
SCREAMING_SNAKE_CASE__ : str =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.num_layers
for i in range(__lowercase ):
SCREAMING_SNAKE_CASE__ : Tuple ={0: '''batch''', 2: '''past_sequence + sequence'''}
SCREAMING_SNAKE_CASE__ : Dict ={0: '''batch''', 2: '''past_sequence + sequence'''}
else:
SCREAMING_SNAKE_CASE__ : str =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def __magic_name__ ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =super().outputs
else:
SCREAMING_SNAKE_CASE__ : List[str] =super(__lowercase , self ).outputs
if self.use_past:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict =self.num_layers
for i in range(__lowercase ):
SCREAMING_SNAKE_CASE__ : List[Any] ={0: '''batch''', 2: '''past_sequence + sequence'''}
SCREAMING_SNAKE_CASE__ : Any ={0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def __magic_name__ ( self : Dict , __lowercase : PreTrainedTokenizer , __lowercase : int = -1 , __lowercase : int = -1 , __lowercase : bool = False , __lowercase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self._generate_dummy_inputs_for_encoder_and_decoder(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
# Generate decoder inputs
SCREAMING_SNAKE_CASE__ : str =seq_length if not self.use_past else 1
SCREAMING_SNAKE_CASE__ : Optional[Any] =self._generate_dummy_inputs_for_encoder_and_decoder(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] ={F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
SCREAMING_SNAKE_CASE__ : str =dict(**__lowercase , **__lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] =common_inputs['''input_ids'''].shape
SCREAMING_SNAKE_CASE__ : Dict =common_inputs['''decoder_input_ids'''].shape[1]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int =self.num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[Any] =(
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE__ : int =decoder_seq_length + 3
SCREAMING_SNAKE_CASE__ : Optional[int] =(
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
SCREAMING_SNAKE_CASE__ : Any =torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(__lowercase , __lowercase )] , dim=1 )
SCREAMING_SNAKE_CASE__ : str =[]
# If the number of encoder and decoder layers are present in the model configuration, both are considered
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any =self.num_layers
SCREAMING_SNAKE_CASE__ : Any =min(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =max(__lowercase , __lowercase ) - min_num_layers
SCREAMING_SNAKE_CASE__ : Optional[int] ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(__lowercase ):
common_inputs["past_key_values"].append(
(
torch.zeros(__lowercase ),
torch.zeros(__lowercase ),
torch.zeros(__lowercase ),
torch.zeros(__lowercase ),
) )
# TODO: test this.
SCREAMING_SNAKE_CASE__ : str =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(__lowercase , __lowercase ):
common_inputs["past_key_values"].append((torch.zeros(__lowercase ), torch.zeros(__lowercase )) )
return common_inputs
def __magic_name__ ( self : Optional[int] , __lowercase : PreTrainedTokenizer , __lowercase : int = -1 , __lowercase : int = -1 , __lowercase : bool = False , __lowercase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE__ : Dict =self._generate_dummy_inputs_for_encoder_and_decoder(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] =common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE__ : Optional[int] =seqlen + 2
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.num_layers
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] =self.num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict =(
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE__ : str =common_inputs['''attention_mask'''].dtype
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.cat(
[common_inputs['''attention_mask'''], torch.ones(__lowercase , __lowercase , dtype=__lowercase )] , dim=1 )
SCREAMING_SNAKE_CASE__ : int =[
(torch.zeros(__lowercase ), torch.zeros(__lowercase )) for _ in range(__lowercase )
]
return common_inputs
def __magic_name__ ( self : Optional[Any] , __lowercase : PreTrainedTokenizer , __lowercase : int = -1 , __lowercase : int = -1 , __lowercase : bool = False , __lowercase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE__ : int =compute_effective_axis_dimension(
__lowercase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.num_special_tokens_to_add(__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =compute_effective_axis_dimension(
__lowercase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__lowercase )
# Generate dummy inputs according to compute batch and sequence
SCREAMING_SNAKE_CASE__ : int =[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
SCREAMING_SNAKE_CASE__ : Optional[int] =dict(tokenizer(__lowercase , return_tensors=__lowercase ) )
return common_inputs
def __magic_name__ ( self : List[Any] , __lowercase : PreTrainedTokenizer , __lowercase : int = -1 , __lowercase : int = -1 , __lowercase : bool = False , __lowercase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE__ : Any =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__lowercase , batch_size=__lowercase , seq_length=__lowercase , is_pair=__lowercase , framework=__lowercase )
else:
SCREAMING_SNAKE_CASE__ : List[Any] =self._generate_dummy_inputs_for_causal_lm(
__lowercase , batch_size=__lowercase , seq_length=__lowercase , is_pair=__lowercase , framework=__lowercase )
return common_inputs
def __magic_name__ ( self : Any , __lowercase : Any , __lowercase : Optional[Any] , __lowercase : str , __lowercase : List[Any] ) -> Any:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE__ : str =super()._flatten_past_key_values_(__lowercase , __lowercase , __lowercase , __lowercase )
else:
SCREAMING_SNAKE_CASE__ : str =super(__lowercase , self )._flatten_past_key_values_(
__lowercase , __lowercase , __lowercase , __lowercase )
@property
def __magic_name__ ( self : Tuple ) -> float:
return 1e-4
| 222
|
'''simple docstring'''
from __future__ import annotations
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : int, UpperCamelCase__ : List[str] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
SCREAMING_SNAKE_CASE__ : List[str] =(l + r) // 2
if v[m] >= key:
SCREAMING_SNAKE_CASE__ : Dict =m
else:
SCREAMING_SNAKE_CASE__ : Any =m # noqa: E741
return r
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
if len(UpperCamelCase__ ) == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] =[0] * len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Dict =1
SCREAMING_SNAKE_CASE__ : Union[str, Any] =v[0]
for i in range(1, len(UpperCamelCase__ ) ):
if v[i] < tail[0]:
SCREAMING_SNAKE_CASE__ : List[Any] =v[i]
elif v[i] > tail[length - 1]:
SCREAMING_SNAKE_CASE__ : int =v[i]
length += 1
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 222
| 1
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
a__ : Any =None
a__ : Union[str, Any] =logging.get_logger(__name__)
a__ : Optional[int] ={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
a__ : Optional[Any] ={
'''vocab_file''': {
'''facebook/nllb-200-distilled-600M''': (
'''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'''
),
},
'''tokenizer_file''': {
'''facebook/nllb-200-distilled-600M''': (
'''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'''
),
},
}
a__ : Dict ={
'''facebook/nllb-large-en-ro''': 1_024,
'''facebook/nllb-200-distilled-600M''': 1_024,
}
# fmt: off
a__ : str =['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] =VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ : List[Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ : Tuple =PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ : List[Any] =["input_ids", "attention_mask"]
SCREAMING_SNAKE_CASE_ : Optional[Any] =NllbTokenizer
SCREAMING_SNAKE_CASE_ : List[int] =[]
SCREAMING_SNAKE_CASE_ : List[int] =[]
def __init__( self : int , __A : int=None , __A : Dict=None , __A : Optional[int]="<s>" , __A : List[str]="</s>" , __A : str="</s>" , __A : str="<s>" , __A : Tuple="<unk>" , __A : Any="<pad>" , __A : Any="<mask>" , __A : str=None , __A : Union[str, Any]=None , __A : Optional[Any]=None , __A : Union[str, Any]=False , **__A : Dict , ):
# Mask token behave like a normal word, i.e. include the space before it
__UpperCamelCase = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else mask_token
__UpperCamelCase = legacy_behaviour
super().__init__(
vocab_file=UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , sep_token=UpperCAmelCase_ , cls_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , src_lang=UpperCAmelCase_ , tgt_lang=UpperCAmelCase_ , additional_special_tokens=UpperCAmelCase_ , legacy_behaviour=UpperCAmelCase_ , **UpperCAmelCase_ , )
__UpperCamelCase = vocab_file
__UpperCamelCase = False if not self.vocab_file else True
__UpperCamelCase = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'additional_special_tokens': _additional_special_tokens} )
__UpperCamelCase = {
lang_code: self.convert_tokens_to_ids(UpperCAmelCase_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
__UpperCamelCase = src_lang if src_lang is not None else 'eng_Latn'
__UpperCamelCase = self.convert_tokens_to_ids(self._src_lang )
__UpperCamelCase = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _lowerCamelCase ( self : Union[str, Any] ):
return self._src_lang
@src_lang.setter
def _lowerCamelCase ( self : List[Any] , __A : str ):
__UpperCamelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _lowerCamelCase ( self : Optional[int] , __A : List[int] , __A : Optional[List[int]] = None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _lowerCamelCase ( self : Optional[Any] , __A : List[int] , __A : Optional[List[int]] = None ):
__UpperCamelCase = [self.sep_token_id]
__UpperCamelCase = [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]
def _lowerCamelCase ( self : Tuple , __A : str , __A : str , __A : Optional[str] , __A : Optional[str] , **__A : Any ):
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
__UpperCamelCase = src_lang
__UpperCamelCase = self(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
__UpperCamelCase = self.convert_tokens_to_ids(UpperCAmelCase_ )
__UpperCamelCase = tgt_lang_id
return inputs
def _lowerCamelCase ( self : Union[str, Any] , __A : List[str] , __A : str = "eng_Latn" , __A : Optional[List[str]] = None , __A : str = "fra_Latn" , **__A : Tuple , ):
__UpperCamelCase = src_lang
__UpperCamelCase = tgt_lang
return super().prepare_seqaseq_batch(UpperCAmelCase_ , UpperCAmelCase_ , **UpperCAmelCase_ )
def _lowerCamelCase ( self : Optional[Any] ):
return self.set_src_lang_special_tokens(self.src_lang )
def _lowerCamelCase ( self : Optional[int] ):
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _lowerCamelCase ( self : Dict , __A : Dict ):
__UpperCamelCase = self.convert_tokens_to_ids(UpperCAmelCase_ )
if self.legacy_behaviour:
__UpperCamelCase = []
__UpperCamelCase = [self.eos_token_id, self.cur_lang_code]
else:
__UpperCamelCase = [self.cur_lang_code]
__UpperCamelCase = [self.eos_token_id]
__UpperCamelCase = self.convert_ids_to_tokens(self.prefix_tokens )
__UpperCamelCase = self.convert_ids_to_tokens(self.suffix_tokens )
__UpperCamelCase = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str , pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _lowerCamelCase ( self : List[str] , __A : str ):
__UpperCamelCase = self.convert_tokens_to_ids(UpperCAmelCase_ )
if self.legacy_behaviour:
__UpperCamelCase = []
__UpperCamelCase = [self.eos_token_id, self.cur_lang_code]
else:
__UpperCamelCase = [self.cur_lang_code]
__UpperCamelCase = [self.eos_token_id]
__UpperCamelCase = self.convert_ids_to_tokens(self.prefix_tokens )
__UpperCamelCase = self.convert_ids_to_tokens(self.suffix_tokens )
__UpperCamelCase = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str , pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _lowerCamelCase ( self : Tuple , __A : str , __A : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(UpperCAmelCase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
__UpperCamelCase = os.path.join(
UpperCAmelCase_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase_ ):
copyfile(self.vocab_file , UpperCAmelCase_ )
return (out_vocab_file,)
| 53
|
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
lowerCAmelCase : Dict = cva.getAffineTransform(_UpperCAmelCase, _UpperCAmelCase )
return cva.warpAffine(_UpperCAmelCase, _UpperCAmelCase, (rows, cols) )
if __name__ == "__main__":
# read original image
__A : List[str] = cva.imread(
str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
)
# turn image in gray scale value
__A : int = cva.cvtColor(image, cva.COLOR_BGR2GRAY)
# get image shape
__A , __A : Optional[Any] = gray_img.shape
# set different points to rotate image
__A : int = np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
__A : Any = np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
__A : Optional[int] = np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
__A : List[Any] = np.array([[10, 100], [80, 50], [180, 250]], np.floataa)
# add all rotated images in a list
__A : List[str] = [
gray_img,
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
]
# plot different image rotations
__A : Union[str, Any] = plt.figure(1)
__A : Optional[Any] = ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
for i, image in enumerate(images):
plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
plt.title(titles[i])
plt.axis('''off''')
plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
plt.show()
| 138
| 0
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class _lowercase ( lowerCAmelCase, unittest.TestCase ):
"""simple docstring"""
__A = ShapEImgaImgPipeline
__A = ["image"]
__A = ["image"]
__A = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
__A = False
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
return 32
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
return 32
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
return 8
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
torch.manual_seed(0 )
a = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
a = CLIPVisionModel(lowerCamelCase_ )
return model
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
a = CLIPImageProcessor(
crop_size=224 , do_center_crop=lowerCamelCase_ , do_normalize=lowerCamelCase_ , do_resize=lowerCamelCase_ , image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073] , image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711] , resample=3 , size=224 , )
return image_processor
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
torch.manual_seed(0 )
a = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"embedding_proj_norm_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
a = PriorTransformer(**lowerCamelCase_ )
return model
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
torch.manual_seed(0 )
a = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
a = ShapERenderer(**lowerCamelCase_ )
return model
def UpperCamelCase_ (self ):
"""simple docstring"""
a = self.dummy_prior
a = self.dummy_image_encoder
a = self.dummy_image_processor
a = self.dummy_renderer
a = HeunDiscreteScheduler(
beta_schedule="exp" , num_train_timesteps=1024 , prediction_type="sample" , use_karras_sigmas=lowerCamelCase_ , clip_sample=lowerCamelCase_ , clip_sample_range=1.0 , )
a = {
"prior": prior,
"image_encoder": image_encoder,
"image_processor": image_processor,
"renderer": renderer,
"scheduler": scheduler,
}
return components
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_=0 ):
"""simple docstring"""
a = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowerCamelCase_ ) ).to(lowerCamelCase_ )
if str(lowerCamelCase_ ).startswith("mps" ):
a = torch.manual_seed(lowerCamelCase_ )
else:
a = torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
a = {
"image": input_image,
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "np",
}
return inputs
def UpperCamelCase_ (self ):
"""simple docstring"""
a = "cpu"
a = self.get_dummy_components()
a = self.pipeline_class(**lowerCamelCase_ )
a = pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
a = pipe(**self.get_dummy_inputs(lowerCamelCase_ ) )
a = output.images[0]
a = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
a = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase_ (self ):
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCamelCase_ (self ):
"""simple docstring"""
a = torch_device == "cpu"
a = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=lowerCamelCase_ , relax_max_difference=lowerCamelCase_ , )
def UpperCamelCase_ (self ):
"""simple docstring"""
a = self.get_dummy_components()
a = self.pipeline_class(**lowerCamelCase_ )
a = pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
a = 1
a = 2
a = self.get_dummy_inputs(lowerCamelCase_ )
for key in inputs.keys():
if key in self.batch_params:
a = batch_size * [inputs[key]]
a = pipe(**lowerCamelCase_ , num_images_per_prompt=lowerCamelCase_ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase_ (self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ (self ):
"""simple docstring"""
a = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png" )
a = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_img2img_out.npy" )
a = ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img" )
a = pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
a = torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
a = pipe(
lowerCamelCase_ , generator=lowerCamelCase_ , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="np" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(lowerCamelCase_ , lowerCamelCase_ )
| 71
|
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def a( A : Optional[Any] ) -> Tuple:
"""simple docstring"""
a = 384
a = 7
if "tiny" in model_name:
a = 96
a = (2, 2, 6, 2)
a = (3, 6, 12, 24)
elif "small" in model_name:
a = 96
a = (2, 2, 18, 2)
a = (3, 6, 12, 24)
elif "base" in model_name:
a = 128
a = (2, 2, 18, 2)
a = (4, 8, 16, 32)
a = 12
a = 512
elif "large" in model_name:
a = 192
a = (2, 2, 18, 2)
a = (6, 12, 24, 48)
a = 12
a = 768
# set label information
a = 150
a = "huggingface/label-files"
a = "ade20k-id2label.json"
a = json.load(open(hf_hub_download(A , A , repo_type="dataset" ) , "r" ) )
a = {int(A ): v for k, v in idalabel.items()}
a = {v: k for k, v in idalabel.items()}
a = SwinConfig(
embed_dim=A , depths=A , num_heads=A , window_size=A , out_features=["stage1", "stage2", "stage3", "stage4"] , )
a = UperNetConfig(
backbone_config=A , auxiliary_in_channels=A , num_labels=A , idalabel=A , labelaid=A , )
return config
def a( A : Optional[Any] ) -> Tuple:
"""simple docstring"""
a = []
# fmt: off
# stem
rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''') )
if i < 3:
rename_keys.append((f'''backbone.stages.{i}.downsample.reduction.weight''', f'''backbone.encoder.layers.{i}.downsample.reduction.weight''') )
rename_keys.append((f'''backbone.stages.{i}.downsample.norm.weight''', f'''backbone.encoder.layers.{i}.downsample.norm.weight''') )
rename_keys.append((f'''backbone.stages.{i}.downsample.norm.bias''', f'''backbone.encoder.layers.{i}.downsample.norm.bias''') )
rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''') )
rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''') )
# decode head
rename_keys.extend(
[
("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
] )
# fmt: on
return rename_keys
def a( A : List[str] , A : List[str] , A : Dict ) -> Any:
"""simple docstring"""
a = dct.pop(A )
a = val
def a( A : str , A : List[str] ) -> List[Any]:
"""simple docstring"""
a = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
a = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
a = state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight''' )
a = state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
a = in_proj_weight[:dim, :]
a = in_proj_bias[: dim]
a = in_proj_weight[
dim : dim * 2, :
]
a = in_proj_bias[
dim : dim * 2
]
a = in_proj_weight[
-dim :, :
]
a = in_proj_bias[-dim :]
# fmt: on
def a( A : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
a , a = x.shape
a = x.reshape(A , 4 , in_channel // 4 )
a = x[:, [0, 2, 1, 3], :].transpose(1 , 2 ).reshape(A , A )
return x
def a( A : int ) -> Dict:
"""simple docstring"""
a , a = x.shape
a = x.reshape(A , in_channel // 4 , 4 )
a = x[:, :, [0, 2, 1, 3]].transpose(1 , 2 ).reshape(A , A )
return x
def a( A : List[Any] ) -> Dict:
"""simple docstring"""
a = x.shape[0]
a = x.reshape(4 , in_channel // 4 )
a = x[[0, 2, 1, 3], :].transpose(0 , 1 ).reshape(A )
return x
def a( A : Optional[Any] ) -> List[str]:
"""simple docstring"""
a = x.shape[0]
a = x.reshape(in_channel // 4 , 4 )
a = x[:, [0, 2, 1, 3]].transpose(0 , 1 ).reshape(A )
return x
def a( A : Any , A : int , A : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = {
"upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
"upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
"upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
"upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
}
a = model_name_to_url[model_name]
a = torch.hub.load_state_dict_from_url(A , map_location="cpu" , file_name=A )[
"state_dict"
]
for name, param in state_dict.items():
print(A , param.shape )
a = get_upernet_config(A )
a = UperNetForSemanticSegmentation(A )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
a = state_dict.pop(A )
if "bn" in key:
a = key.replace("bn" , "batch_norm" )
a = val
# rename keys
a = create_rename_keys(A )
for src, dest in rename_keys:
rename_key(A , A , A )
read_in_q_k_v(A , config.backbone_config )
# fix downsample parameters
for key, value in state_dict.items():
if "downsample" in key:
if "reduction" in key:
a = reverse_correct_unfold_reduction_order(A )
if "norm" in key:
a = reverse_correct_unfold_norm_order(A )
model.load_state_dict(A )
# verify on image
a = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
a = Image.open(requests.get(A , stream=A ).raw ).convert("RGB" )
a = SegformerImageProcessor()
a = processor(A , return_tensors="pt" ).pixel_values
with torch.no_grad():
a = model(A )
a = outputs.logits
print(logits.shape )
print("First values of logits:" , logits[0, 0, :3, :3] )
# assert values
if model_name == "upernet-swin-tiny":
a = torch.tensor(
[[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]] )
elif model_name == "upernet-swin-small":
a = torch.tensor(
[[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]] )
elif model_name == "upernet-swin-base":
a = torch.tensor(
[[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]] )
elif model_name == "upernet-swin-large":
a = torch.tensor(
[[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]] )
print("Logits:" , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , A , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(A )
print(f'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(A )
if push_to_hub:
print(f'''Pushing model and processor for {model_name} to hub''' )
model.push_to_hub(f'''openmmlab/{model_name}''' )
processor.push_to_hub(f'''openmmlab/{model_name}''' )
if __name__ == "__main__":
_lowercase: Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="upernet-swin-tiny",
type=str,
choices=[F"""upernet-swin-{size}""" for size in ["tiny", "small", "base", "large"]],
help="Name of the Swin + UperNet model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
_lowercase: int = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 71
| 1
|
from __future__ import annotations
import math
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ ) -> None:
__UpperCamelCase =size
# approximate the overall size of segment tree with given value
__UpperCamelCase =[0 for i in range(0 , 4 * size )]
# create array to store lazy update
__UpperCamelCase =[0 for i in range(0 , 4 * size )]
__UpperCamelCase =[0 for i in range(0 , 4 * size )] # flag for lazy update
def _a ( self , A_ ) -> int:
return idx * 2
def _a ( self , A_ ) -> int:
return idx * 2 + 1
def _a ( self , A_ , A_ , A_ , A_ ) -> None:
if left_element == right_element:
__UpperCamelCase =a[left_element - 1]
else:
__UpperCamelCase =(left_element + right_element) // 2
self.build(self.left(A_ ) , A_ , A_ , A_ )
self.build(self.right(A_ ) , mid + 1 , A_ , A_ )
__UpperCamelCase =max(
self.segment_tree[self.left(A_ )] , self.segment_tree[self.right(A_ )] )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ ) -> bool:
if self.flag[idx] is True:
__UpperCamelCase =self.lazy[idx]
__UpperCamelCase =False
if left_element != right_element:
__UpperCamelCase =self.lazy[idx]
__UpperCamelCase =self.lazy[idx]
__UpperCamelCase =True
__UpperCamelCase =True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
__UpperCamelCase =val
if left_element != right_element:
__UpperCamelCase =val
__UpperCamelCase =val
__UpperCamelCase =True
__UpperCamelCase =True
return True
__UpperCamelCase =(left_element + right_element) // 2
self.update(self.left(A_ ) , A_ , A_ , A_ , A_ , A_ )
self.update(self.right(A_ ) , mid + 1 , A_ , A_ , A_ , A_ )
__UpperCamelCase =max(
self.segment_tree[self.left(A_ )] , self.segment_tree[self.right(A_ )] )
return True
def _a ( self , A_ , A_ , A_ , A_ , A_ ) -> int | float:
if self.flag[idx] is True:
__UpperCamelCase =self.lazy[idx]
__UpperCamelCase =False
if left_element != right_element:
__UpperCamelCase =self.lazy[idx]
__UpperCamelCase =self.lazy[idx]
__UpperCamelCase =True
__UpperCamelCase =True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
__UpperCamelCase =(left_element + right_element) // 2
__UpperCamelCase =self.query(self.left(A_ ) , A_ , A_ , A_ , A_ )
__UpperCamelCase =self.query(self.right(A_ ) , mid + 1 , A_ , A_ , A_ )
return max(A_ , A_ )
def __str__( self ) -> str:
return str([self.query(1 , 1 , self.size , A_ , A_ ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
_A = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
_A = 15
_A = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt)
| 62
|
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str = "" ):
__UpperCamelCase =url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
__UpperCamelCase =BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__ ).text , 'html.parser' )
__UpperCamelCase =soup.find_all('td' , attrs='titleColumn' )
__UpperCamelCase =soup.find_all('td' , class_='ratingColumn imdbRating' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
}
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str = "IMDb_Top_250_Movies.csv" ):
__UpperCamelCase =get_imdb_top_aaa_movies()
with open(SCREAMING_SNAKE_CASE__ , 'w' , newline='' ) as out_file:
__UpperCamelCase =csv.writer(SCREAMING_SNAKE_CASE__ )
writer.writerow(['Movie title', 'IMDb rating'] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 62
| 1
|
def _a ( lowerCamelCase: str , lowerCamelCase: list[str] ) -> str:
'''simple docstring'''
__A = ''''''
for word_or_phrase in separated:
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise Exception('''join() accepts only strings to be joined''' )
joined += word_or_phrase + separator
return joined.strip(lowerCamelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 250
|
def _a ( lowerCamelCase: Optional[Any] , lowerCamelCase: str , lowerCamelCase: Tuple , lowerCamelCase: Union[str, Any] ) -> str:
'''simple docstring'''
__A = [False] * len(lowerCamelCase )
__A = []
queue.append(lowerCamelCase )
__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: Tuple , lowerCamelCase: Union[str, Any] , lowerCamelCase: Optional[Any] ) -> Optional[int]:
'''simple docstring'''
__A = [-1] * (len(lowerCamelCase ))
__A = 0
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]
return max_flow
snake_case__ : List[Any] = [
[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],
]
snake_case__ , snake_case__ : List[Any] = 0, 5
print(ford_fulkerson(graph, source, sink))
| 250
| 1
|
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def lowercase ( A_ = "AAPL" )-> Any:
'''simple docstring'''
a : List[str] = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
a : int = BeautifulSoup(requests.get(A_ ).text , "html.parser" )
a : int = """My(6px) Pos(r) smartphone_Mt(6px)"""
return soup.find("div" , class_=class_ ).find("span" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')
| 40
|
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _a ( lowerCamelCase ):
return "".join(sorted(lowerCamelCase ) )
def _a ( lowerCamelCase ):
return word_by_signature[signature(lowerCamelCase )]
_lowerCamelCase =Path(__file__).parent.joinpath("""words.txt""").read_text(encoding="""utf-8""")
_lowerCamelCase =sorted({word.strip().lower() for word in data.splitlines()})
_lowerCamelCase =collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
_lowerCamelCase ={word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open("""anagrams.txt""", """w""") as file:
file.write("""all_anagrams = \n """)
file.write(pprint.pformat(all_anagrams))
| 287
| 0
|
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __UpperCamelCase ( _lowerCAmelCase ) -> Any:
"""simple docstring"""
A : str = prime_factors(lowercase__ )
if is_square_free(lowercase__ ):
return -1 if len(lowercase__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 363
|
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __UpperCamelCase ( ) -> Dict:
"""simple docstring"""
A : str = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
A : int = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(_lowerCAmelCase )
# Let's go
A : str = parser.parse_args()
if not hasattr(_lowerCAmelCase , """func""" ):
parser.print_help()
exit(1 )
# Run
A : Any = args.func(_lowerCAmelCase )
service.run()
if __name__ == "__main__":
main()
| 115
| 0
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : Tuple = {
"bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
"bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
"bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
"bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
"bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
"bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
"bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
"bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
"bert-large-uncased-whole-word-masking": (
"https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
),
"bert-large-cased-whole-word-masking": (
"https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
),
"bert-large-uncased-whole-word-masking-finetuned-squad": (
"https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
),
"bert-large-cased-whole-word-masking-finetuned-squad": (
"https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
),
"bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
"bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
"bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
"cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
"cl-tohoku/bert-base-japanese-whole-word-masking": (
"https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
),
"cl-tohoku/bert-base-japanese-char": (
"https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
),
"cl-tohoku/bert-base-japanese-char-whole-word-masking": (
"https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
),
"TurkuNLP/bert-base-finnish-cased-v1": (
"https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
),
"TurkuNLP/bert-base-finnish-uncased-v1": (
"https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
),
"wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
# See all BERT models at https://huggingface.co/models?filter=bert
}
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : Dict = "bert"
def __init__( self , A_=30_522 , A_=768 , A_=12 , A_=12 , A_=3_072 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=2 , A_=0.02 , A_=1e-12 , A_=0 , A_="absolute" , A_=True , A_=None , **A_ , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=A_ , **A_ )
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 ( _SCREAMING_SNAKE_CASE ):
@property
def __UpperCamelCase ( 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),
('token_type_ids', dynamic_axis),
] )
| 222
|
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 lowercase ( unittest.TestCase ):
def __UpperCamelCase ( self , A_ ) -> List[str]:
"""simple docstring"""
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] , model_result['ss'] ):
UpperCamelCase = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(A_ )
def __UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = 'sshleifer/tiny-gpt2'
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , inference=A_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __UpperCamelCase ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = 'sgugger/tiny-distilbert-classification'
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , inference=A_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A_ , only_pretrain_model=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __UpperCamelCase ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = 'sshleifer/tiny-gpt2'
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , inference=A_ , torchscript=A_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ )
UpperCamelCase = 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 __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = 'sshleifer/tiny-gpt2'
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , inference=A_ , fpaa=A_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
UpperCamelCase = 'sshleifer/tiny-gpt2'
UpperCamelCase = AutoConfig.from_pretrained(A_ )
# set architectures equal to `None`
UpperCamelCase = None
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , inference=A_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ , configs=[config] )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = 'sshleifer/tiny-gpt2'
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , inference=A_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ )
UpperCamelCase = 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 __UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = 'sshleifer/tiny-gpt2'
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , inference=A_ , sequence_lengths=[8] , batch_sizes=[1] , fpaa=A_ , multi_process=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = 'sshleifer/tiny-gpt2'
UpperCamelCase = AutoConfig.from_pretrained(A_ )
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , inference=A_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ , configs=[config] )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = 'sshleifer/tinier_bart'
UpperCamelCase = AutoConfig.from_pretrained(A_ )
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , inference=A_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ , configs=[config] )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = 'sshleifer/tiny-gpt2'
UpperCamelCase = AutoConfig.from_pretrained(A_ )
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , inference=A_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ , configs=[config] )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __UpperCamelCase ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = 'sshleifer/tinier_bart'
UpperCamelCase = AutoConfig.from_pretrained(A_ )
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , inference=A_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ , configs=[config] )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
UpperCamelCase = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , inference=A_ , save_to_csv=A_ , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(A_ , 'inf_time.csv' ) , train_memory_csv_file=os.path.join(A_ , 'train_mem.csv' ) , inference_memory_csv_file=os.path.join(A_ , 'inf_mem.csv' ) , train_time_csv_file=os.path.join(A_ , 'train_time.csv' ) , env_info_csv_file=os.path.join(A_ , 'env.csv' ) , multi_process=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ )
benchmark.run()
self.assertTrue(Path(os.path.join(A_ , 'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(A_ , 'train_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(A_ , 'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(A_ , 'train_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(A_ , 'env.csv' ) ).exists() )
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(A_ ):
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:
UpperCamelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A_ , 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_ , multi_process=A_ , )
UpperCamelCase = PyTorchBenchmark(A_ )
UpperCamelCase = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(A_ , 'log.txt' ) ).exists() )
| 222
| 1
|
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
UpperCAmelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase__ = "\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)[\"depth\"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline(\"depth-estimation\")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n\n >>> img = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n >>> prompt = \"A robot, 4k photo\"\n >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save(\"robot_cat.png\")\n ```\n"
def _UpperCAmelCase ( __lowerCamelCase : List[Any] , __lowerCamelCase : str , __lowerCamelCase : Dict=8 ) -> Union[str, Any]:
_snake_case = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
_snake_case = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE ):
def __init__( self : int , _lowerCamelCase : UNetaDConditionModel , _lowerCamelCase : DDPMScheduler , _lowerCamelCase : VQModel , ):
super().__init__()
self.register_modules(
unet=_lowerCamelCase , scheduler=_lowerCamelCase , movq=_lowerCamelCase , )
_snake_case = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def lowercase ( self : int , _lowerCamelCase : Tuple , _lowerCamelCase : Any , _lowerCamelCase : Dict , _lowerCamelCase : int , _lowerCamelCase : Optional[Any] , _lowerCamelCase : int ):
if latents is None:
_snake_case = randn_tensor(_lowerCamelCase , generator=_lowerCamelCase , device=_lowerCamelCase , dtype=_lowerCamelCase )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
_snake_case = latents.to(_lowerCamelCase )
_snake_case = latents * scheduler.init_noise_sigma
return latents
def lowercase ( self : List[Any] , _lowerCamelCase : str=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
_snake_case = torch.device(f'''cuda:{gpu_id}''' )
_snake_case = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_lowerCamelCase , _lowerCamelCase )
def lowercase ( self : Dict , _lowerCamelCase : List[str]=0 ):
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
_snake_case = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=_lowerCamelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
_snake_case = None
for cpu_offloaded_model in [self.unet, self.movq]:
_snake_case = cpu_offload_with_hook(_lowerCamelCase , _lowerCamelCase , prev_module_hook=_lowerCamelCase )
# We'll offload the last model manually.
_snake_case = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowercase ( self : List[Any] ):
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(_lowerCamelCase , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(_lowerCamelCase )
def __call__( self : List[Any] , _lowerCamelCase : Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowerCamelCase : Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowerCamelCase : torch.FloatTensor , _lowerCamelCase : int = 512 , _lowerCamelCase : int = 512 , _lowerCamelCase : int = 100 , _lowerCamelCase : float = 4.0 , _lowerCamelCase : int = 1 , _lowerCamelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowerCamelCase : Optional[torch.FloatTensor] = None , _lowerCamelCase : Optional[str] = "pil" , _lowerCamelCase : bool = True , ):
_snake_case = self._execution_device
_snake_case = guidance_scale > 1.0
if isinstance(_lowerCamelCase , _lowerCamelCase ):
_snake_case = torch.cat(_lowerCamelCase , dim=0 )
if isinstance(_lowerCamelCase , _lowerCamelCase ):
_snake_case = torch.cat(_lowerCamelCase , dim=0 )
if isinstance(_lowerCamelCase , _lowerCamelCase ):
_snake_case = torch.cat(_lowerCamelCase , dim=0 )
_snake_case = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
_snake_case = image_embeds.repeat_interleave(_lowerCamelCase , dim=0 )
_snake_case = negative_image_embeds.repeat_interleave(_lowerCamelCase , dim=0 )
_snake_case = hint.repeat_interleave(_lowerCamelCase , dim=0 )
_snake_case = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=_lowerCamelCase )
_snake_case = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=_lowerCamelCase )
self.scheduler.set_timesteps(_lowerCamelCase , device=_lowerCamelCase )
_snake_case = self.scheduler.timesteps
_snake_case = self.movq.config.latent_channels
_snake_case = downscale_height_and_width(_lowerCamelCase , _lowerCamelCase , self.movq_scale_factor )
# create initial latent
_snake_case = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , self.scheduler , )
for i, t in enumerate(self.progress_bar(_lowerCamelCase ) ):
# expand the latents if we are doing classifier free guidance
_snake_case = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_snake_case = {"""image_embeds""": image_embeds, """hint""": hint}
_snake_case = self.unet(
sample=_lowerCamelCase , timestep=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , added_cond_kwargs=_lowerCamelCase , return_dict=_lowerCamelCase , )[0]
if do_classifier_free_guidance:
_snake_case = noise_pred.split(latents.shape[1] , dim=1 )
_snake_case = noise_pred.chunk(2 )
_snake_case = variance_pred.chunk(2 )
_snake_case = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
_snake_case = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
_snake_case = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
_snake_case = self.scheduler.step(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , generator=_lowerCamelCase , )[0]
# post-processing
_snake_case = self.movq.decode(_lowerCamelCase , force_not_quantize=_lowerCamelCase )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
_snake_case = image * 0.5 + 0.5
_snake_case = image.clamp(0 , 1 )
_snake_case = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_snake_case = self.numpy_to_pil(_lowerCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_lowerCamelCase )
| 350
|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCAmelCase__ = {
'vocab_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
),
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
),
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
),
'bert-base-multilingual-cased': (
'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
),
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-cased': (
'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
),
},
}
UpperCAmelCase__ = {
'bert-base-uncased': 512,
'bert-large-uncased': 512,
'bert-base-cased': 512,
'bert-large-cased': 512,
'bert-base-multilingual-uncased': 512,
'bert-base-multilingual-cased': 512,
'bert-base-chinese': 512,
'bert-base-german-cased': 512,
'bert-large-uncased-whole-word-masking': 512,
'bert-large-cased-whole-word-masking': 512,
'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
'bert-large-cased-whole-word-masking-finetuned-squad': 512,
'bert-base-cased-finetuned-mrpc': 512,
'bert-base-german-dbmdz-cased': 512,
'bert-base-german-dbmdz-uncased': 512,
'TurkuNLP/bert-base-finnish-cased-v1': 512,
'TurkuNLP/bert-base-finnish-uncased-v1': 512,
'wietsedv/bert-base-dutch-cased': 512,
}
UpperCAmelCase__ = {
'bert-base-uncased': {'do_lower_case': True},
'bert-large-uncased': {'do_lower_case': True},
'bert-base-cased': {'do_lower_case': False},
'bert-large-cased': {'do_lower_case': False},
'bert-base-multilingual-uncased': {'do_lower_case': True},
'bert-base-multilingual-cased': {'do_lower_case': False},
'bert-base-chinese': {'do_lower_case': False},
'bert-base-german-cased': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
'bert-large-cased-whole-word-masking': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
'bert-base-german-dbmdz-cased': {'do_lower_case': False},
'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}
class lowerCAmelCase__ ( A_ ):
__a = VOCAB_FILES_NAMES
__a = PRETRAINED_VOCAB_FILES_MAP
__a = PRETRAINED_INIT_CONFIGURATION
__a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a = BertTokenizer
def __init__( self : Optional[int] , _lowerCamelCase : int=None , _lowerCamelCase : Optional[int]=None , _lowerCamelCase : Tuple=True , _lowerCamelCase : Optional[int]="[UNK]" , _lowerCamelCase : Any="[SEP]" , _lowerCamelCase : Any="[PAD]" , _lowerCamelCase : List[Any]="[CLS]" , _lowerCamelCase : Dict="[MASK]" , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Any=None , **_lowerCamelCase : int , ):
super().__init__(
_lowerCamelCase , tokenizer_file=_lowerCamelCase , do_lower_case=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , pad_token=_lowerCamelCase , cls_token=_lowerCamelCase , mask_token=_lowerCamelCase , tokenize_chinese_chars=_lowerCamelCase , strip_accents=_lowerCamelCase , **_lowerCamelCase , )
_snake_case = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , _lowerCamelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , _lowerCamelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , _lowerCamelCase ) != tokenize_chinese_chars
):
_snake_case = getattr(_lowerCamelCase , normalizer_state.pop('''type''' ) )
_snake_case = do_lower_case
_snake_case = strip_accents
_snake_case = tokenize_chinese_chars
_snake_case = normalizer_class(**_lowerCamelCase )
_snake_case = do_lower_case
def lowercase ( self : Optional[int] , _lowerCamelCase : List[Any] , _lowerCamelCase : List[Any]=None ):
_snake_case = [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 lowercase ( self : Optional[Any] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None ):
_snake_case = [self.sep_token_id]
_snake_case = [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 lowercase ( self : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : Optional[str] = None ):
_snake_case = self._tokenizer.model.save(_lowerCamelCase , name=_lowerCamelCase )
return tuple(_lowerCamelCase )
| 40
| 0
|
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class __A ( a , a , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase__ : Any =(
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
UpperCamelCase__ : Dict =(
{
"""feature-extraction""": TFMobileBertModel,
"""fill-mask""": TFMobileBertForMaskedLM,
"""question-answering""": TFMobileBertForQuestionAnswering,
"""text-classification""": TFMobileBertForSequenceClassification,
"""token-classification""": TFMobileBertForTokenClassification,
"""zero-shot""": TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCamelCase__ : List[str] =False
UpperCamelCase__ : Any =False
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =super()._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
if return_labels:
if model_class in get_values(lowerCamelCase__ ):
__UpperCamelCase : str =tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class __A ( a ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=99 , lowerCamelCase__=32 , lowerCamelCase__=32 , lowerCamelCase__=2 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=512 , lowerCamelCase__=16 , lowerCamelCase__=2 , lowerCamelCase__=0.02 , lowerCamelCase__=3 , lowerCamelCase__=4 , lowerCamelCase__=None , ):
"""simple docstring"""
__UpperCamelCase : List[Any] =parent
__UpperCamelCase : List[Any] =batch_size
__UpperCamelCase : Dict =seq_length
__UpperCamelCase : Dict =is_training
__UpperCamelCase : Tuple =use_input_mask
__UpperCamelCase : List[Any] =use_token_type_ids
__UpperCamelCase : int =use_labels
__UpperCamelCase : List[Any] =vocab_size
__UpperCamelCase : Optional[Any] =hidden_size
__UpperCamelCase : str =num_hidden_layers
__UpperCamelCase : Optional[Any] =num_attention_heads
__UpperCamelCase : str =intermediate_size
__UpperCamelCase : Tuple =hidden_act
__UpperCamelCase : int =hidden_dropout_prob
__UpperCamelCase : Any =attention_probs_dropout_prob
__UpperCamelCase : List[str] =max_position_embeddings
__UpperCamelCase : int =type_vocab_size
__UpperCamelCase : Union[str, Any] =type_sequence_label_size
__UpperCamelCase : Optional[int] =initializer_range
__UpperCamelCase : Optional[Any] =num_labels
__UpperCamelCase : Any =num_choices
__UpperCamelCase : Tuple =scope
__UpperCamelCase : Optional[int] =embedding_size
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase : int =None
if self.use_input_mask:
__UpperCamelCase : str =random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase : List[Any] =None
if self.use_token_type_ids:
__UpperCamelCase : Optional[int] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCamelCase : int =None
__UpperCamelCase : Any =None
__UpperCamelCase : Tuple =None
if self.use_labels:
__UpperCamelCase : str =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase : Any =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase : int =ids_tensor([self.batch_size] , self.num_choices )
__UpperCamelCase : Dict =MobileBertConfig(
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 , initializer_range=self.initializer_range , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] =TFMobileBertModel(config=lowerCamelCase__ )
__UpperCamelCase : Any ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
__UpperCamelCase : int =model(lowerCamelCase__ )
__UpperCamelCase : Union[str, Any] =[input_ids, input_mask]
__UpperCamelCase : Any =model(lowerCamelCase__ )
__UpperCamelCase : Optional[int] =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 __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] =TFMobileBertForMaskedLM(config=lowerCamelCase__ )
__UpperCamelCase : str ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
__UpperCamelCase : List[Any] =model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : List[str] =TFMobileBertForNextSentencePrediction(config=lowerCamelCase__ )
__UpperCamelCase : List[Any] ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
__UpperCamelCase : List[str] =model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : Dict =TFMobileBertForPreTraining(config=lowerCamelCase__ )
__UpperCamelCase : List[Any] ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
__UpperCamelCase : Union[str, Any] =model(lowerCamelCase__ )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : str =self.num_labels
__UpperCamelCase : Optional[int] =TFMobileBertForSequenceClassification(config=lowerCamelCase__ )
__UpperCamelCase : int ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
__UpperCamelCase : Optional[int] =model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : List[str] =self.num_choices
__UpperCamelCase : Tuple =TFMobileBertForMultipleChoice(config=lowerCamelCase__ )
__UpperCamelCase : Union[str, Any] =tf.tile(tf.expand_dims(lowerCamelCase__ , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase : Any =tf.tile(tf.expand_dims(lowerCamelCase__ , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase : Dict =tf.tile(tf.expand_dims(lowerCamelCase__ , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase : Any ={
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
__UpperCamelCase : str =model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : List[str] =self.num_labels
__UpperCamelCase : str =TFMobileBertForTokenClassification(config=lowerCamelCase__ )
__UpperCamelCase : int ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
__UpperCamelCase : Optional[Any] =model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : str =TFMobileBertForQuestionAnswering(config=lowerCamelCase__ )
__UpperCamelCase : Optional[int] ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
__UpperCamelCase : Optional[Any] =model(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 __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : Optional[Any] =config_and_inputs
__UpperCamelCase : Any ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : int =TFMobileBertModelTest.TFMobileBertModelTester(self )
__UpperCamelCase : Tuple =ConfigTester(self , config_class=lowerCamelCase__ , hidden_size=37 )
def __lowercase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCamelCase__ )
@slow
def __lowercase ( self ):
"""simple docstring"""
for model_name in ["google/mobilebert-uncased"]:
__UpperCamelCase : Tuple =TFMobileBertModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
@require_tf
class __A ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Dict =TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased' )
__UpperCamelCase : int =tf.constant([[0, 1, 2, 3, 4, 5]] )
__UpperCamelCase : Union[str, Any] =model(lowerCamelCase__ )[0]
__UpperCamelCase : List[Any] =[1, 6, 30522]
self.assertEqual(output.shape , lowerCamelCase__ )
__UpperCamelCase : List[Any] =tf.constant(
[
[
[-4.5_919_547, -9.248_295, -9.645_256],
[-6.7_306_175, -6.440_284, -6.6_052_837],
[-7.2_743_506, -6.7_847_915, -6.024_673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , lowerCamelCase__ , atol=1E-4 )
| 71
|
import re
def A ( a_ ) -> bool:
__UpperCamelCase : Any =re.compile(
r'^(?:0|94|\+94|0{2}94)' r'7(0|1|2|4|5|6|7|8)' r'(-| |)' r'\d{7}$' )
return bool(re.search(a_ ,a_ ) )
if __name__ == "__main__":
A_ :List[str] = '''0094702343221'''
print(is_sri_lankan_phone_number(phone))
| 71
| 1
|
"""simple docstring"""
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Any = DownBlockaD # noqa F405
a__ : Tuple = "down"
def a ( self : Optional[Any] ):
__UpperCAmelCase = [-0.0_232, -0.9_869, 0.8_054, -0.0_637, -0.1_688, -1.4_264, 0.4_470, -1.3_394, 0.0_904]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Dict = ResnetDownsampleBlockaD # noqa F405
a__ : int = "down"
def a ( self : Any ):
__UpperCAmelCase = [0.0_710, 0.2_410, -0.7_320, -1.0_757, -1.1_343, 0.3_540, -0.0_133, -0.2_576, 0.0_948]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Dict = AttnDownBlockaD # noqa F405
a__ : int = "down"
def a ( self : List[str] ):
__UpperCAmelCase = [0.0_636, 0.8_964, -0.6_234, -1.0_131, 0.0_844, 0.4_935, 0.3_437, 0.0_911, -0.2_957]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Dict = CrossAttnDownBlockaD # noqa F405
a__ : List[Any] = "down"
def a ( self : Dict ):
__UpperCAmelCase = super().prepare_init_args_and_inputs_for_common()
__UpperCAmelCase = 32
return init_dict, inputs_dict
def a ( self : str ):
__UpperCAmelCase = [0.2_238, -0.7_396, -0.2_255, -0.3_829, 0.1_925, 1.1_665, 0.0_603, -0.7_295, 0.1_983]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Tuple = SimpleCrossAttnDownBlockaD # noqa F405
a__ : Optional[Any] = "down"
@property
def a ( self : Optional[int] ):
return super().get_dummy_input(include_encoder_hidden_states=_lowerCamelCase )
def a ( self : Optional[int] ):
__UpperCAmelCase = super().prepare_init_args_and_inputs_for_common()
__UpperCAmelCase = 32
return init_dict, inputs_dict
@unittest.skipIf(torch_device == '''mps''' , '''MPS result is not consistent''' )
def a ( self : Dict ):
__UpperCAmelCase = [0.7_921, -0.0_992, -0.1_962, -0.7_695, -0.4_242, 0.7_804, 0.4_737, 0.2_765, 0.3_338]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Any = SkipDownBlockaD # noqa F405
a__ : List[str] = "down"
@property
def a ( self : Dict ):
return super().get_dummy_input(include_skip_sample=_lowerCamelCase )
def a ( self : str ):
__UpperCAmelCase = [-0.0_845, -0.2_087, -0.2_465, 0.0_971, 0.1_900, -0.0_484, 0.2_664, 0.4_179, 0.5_069]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Union[str, Any] = AttnSkipDownBlockaD # noqa F405
a__ : Union[str, Any] = "down"
@property
def a ( self : int ):
return super().get_dummy_input(include_skip_sample=_lowerCamelCase )
def a ( self : Any ):
__UpperCAmelCase = [0.5_539, 0.1_609, 0.4_924, 0.0_537, -0.1_995, 0.4_050, 0.0_979, -0.2_721, -0.0_642]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Any = DownEncoderBlockaD # noqa F405
a__ : int = "down"
@property
def a ( self : Optional[int] ):
return super().get_dummy_input(include_temb=_lowerCamelCase )
def a ( self : Optional[Any] ):
__UpperCAmelCase = {
'''in_channels''': 32,
'''out_channels''': 32,
}
__UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def a ( self : Optional[int] ):
__UpperCAmelCase = [1.1_102, 0.5_302, 0.4_872, -0.0_023, -0.8_042, 0.0_483, -0.3_489, -0.5_632, 0.7_626]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : int = AttnDownEncoderBlockaD # noqa F405
a__ : str = "down"
@property
def a ( self : List[str] ):
return super().get_dummy_input(include_temb=_lowerCamelCase )
def a ( self : Union[str, Any] ):
__UpperCAmelCase = {
'''in_channels''': 32,
'''out_channels''': 32,
}
__UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def a ( self : List[str] ):
__UpperCAmelCase = [0.8_966, -0.1_486, 0.8_568, 0.8_141, -0.9_046, -0.1_342, -0.0_972, -0.7_417, 0.1_538]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : str = UNetMidBlockaD # noqa F405
a__ : List[str] = "mid"
def a ( self : Any ):
__UpperCAmelCase = {
'''in_channels''': 32,
'''temb_channels''': 1_28,
}
__UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def a ( self : List[str] ):
__UpperCAmelCase = [-0.1_062, 1.7_248, 0.3_494, 1.4_569, -0.0_910, -1.2_421, -0.9_984, 0.6_736, 1.0_028]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Tuple = UNetMidBlockaDCrossAttn # noqa F405
a__ : Dict = "mid"
def a ( self : Optional[int] ):
__UpperCAmelCase = super().prepare_init_args_and_inputs_for_common()
__UpperCAmelCase = 32
return init_dict, inputs_dict
def a ( self : List[str] ):
__UpperCAmelCase = [0.0_187, 2.4_220, 0.4_484, 1.1_203, -0.6_121, -1.5_122, -0.8_270, 0.7_851, 1.8_335]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : int = UNetMidBlockaDSimpleCrossAttn # noqa F405
a__ : Union[str, Any] = "mid"
@property
def a ( self : Any ):
return super().get_dummy_input(include_encoder_hidden_states=_lowerCamelCase )
def a ( self : List[Any] ):
__UpperCAmelCase = super().prepare_init_args_and_inputs_for_common()
__UpperCAmelCase = 32
return init_dict, inputs_dict
def a ( self : int ):
__UpperCAmelCase = [0.7_143, 1.9_974, 0.5_448, 1.3_977, 0.1_282, -1.1_237, -1.4_238, 0.5_530, 0.8_880]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Optional[Any] = UpBlockaD # noqa F405
a__ : List[str] = "up"
@property
def a ( self : str ):
return super().get_dummy_input(include_res_hidden_states_tuple=_lowerCamelCase )
def a ( self : int ):
__UpperCAmelCase = [-0.2_041, -0.4_165, -0.3_022, 0.0_041, -0.6_628, -0.7_053, 0.1_928, -0.0_325, 0.0_523]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Union[str, Any] = ResnetUpsampleBlockaD # noqa F405
a__ : Tuple = "up"
@property
def a ( self : Optional[Any] ):
return super().get_dummy_input(include_res_hidden_states_tuple=_lowerCamelCase )
def a ( self : Optional[Any] ):
__UpperCAmelCase = [0.2_287, 0.3_549, -0.1_346, 0.4_797, -0.1_715, -0.9_649, 0.7_305, -0.5_864, -0.6_244]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Union[str, Any] = CrossAttnUpBlockaD # noqa F405
a__ : List[str] = "up"
@property
def a ( self : Dict ):
return super().get_dummy_input(include_res_hidden_states_tuple=_lowerCamelCase )
def a ( self : List[Any] ):
__UpperCAmelCase = super().prepare_init_args_and_inputs_for_common()
__UpperCAmelCase = 32
return init_dict, inputs_dict
def a ( self : int ):
__UpperCAmelCase = [-0.1_403, -0.3_515, -0.0_420, -0.1_425, 0.3_167, 0.5_094, -0.2_181, 0.5_931, 0.5_582]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Tuple = SimpleCrossAttnUpBlockaD # noqa F405
a__ : Dict = "up"
@property
def a ( self : List[str] ):
return super().get_dummy_input(include_res_hidden_states_tuple=_lowerCamelCase , include_encoder_hidden_states=_lowerCamelCase )
def a ( self : Dict ):
__UpperCAmelCase = super().prepare_init_args_and_inputs_for_common()
__UpperCAmelCase = 32
return init_dict, inputs_dict
def a ( self : List[str] ):
__UpperCAmelCase = [0.2_645, 0.1_480, 0.0_909, 0.8_044, -0.9_758, -0.9_083, 0.0_994, -1.1_453, -0.7_402]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Tuple = AttnUpBlockaD # noqa F405
a__ : List[str] = "up"
@property
def a ( self : List[Any] ):
return super().get_dummy_input(include_res_hidden_states_tuple=_lowerCamelCase )
@unittest.skipIf(torch_device == '''mps''' , '''MPS result is not consistent''' )
def a ( self : int ):
__UpperCAmelCase = [0.0_979, 0.1_326, 0.0_021, 0.0_659, 0.2_249, 0.0_059, 0.1_132, 0.5_952, 0.1_033]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : int = SkipUpBlockaD # noqa F405
a__ : List[str] = "up"
@property
def a ( self : Any ):
return super().get_dummy_input(include_res_hidden_states_tuple=_lowerCamelCase )
def a ( self : List[str] ):
__UpperCAmelCase = [-0.0_893, -0.1_234, -0.1_506, -0.0_332, 0.0_123, -0.0_211, 0.0_566, 0.0_143, 0.0_362]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Dict = AttnSkipUpBlockaD # noqa F405
a__ : List[str] = "up"
@property
def a ( self : Tuple ):
return super().get_dummy_input(include_res_hidden_states_tuple=_lowerCamelCase )
def a ( self : List[Any] ):
__UpperCAmelCase = [0.0_361, 0.0_617, 0.2_787, -0.0_350, 0.0_342, 0.3_421, -0.0_843, 0.0_913, 0.3_015]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Any = UpDecoderBlockaD # noqa F405
a__ : Any = "up"
@property
def a ( self : str ):
return super().get_dummy_input(include_temb=_lowerCamelCase )
def a ( self : Any ):
__UpperCAmelCase = {'''in_channels''': 32, '''out_channels''': 32}
__UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def a ( self : Optional[Any] ):
__UpperCAmelCase = [0.4_404, 0.1_998, -0.9_886, -0.3_320, -0.3_128, -0.7_034, -0.6_955, -0.2_338, -0.3_137]
super().test_output(_lowerCamelCase )
class _UpperCAmelCase ( a__ , unittest.TestCase ):
a__ : Dict = AttnUpDecoderBlockaD # noqa F405
a__ : List[Any] = "up"
@property
def a ( self : Any ):
return super().get_dummy_input(include_temb=_lowerCamelCase )
def a ( self : Optional[Any] ):
__UpperCAmelCase = {'''in_channels''': 32, '''out_channels''': 32}
__UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def a ( self : List[str] ):
__UpperCAmelCase = [0.6_738, 0.4_491, 0.1_055, 1.0_710, 0.7_316, 0.3_339, 0.3_352, 0.1_023, 0.3_568]
super().test_output(_lowerCamelCase )
| 354
|
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
require_torch,
require_torch_gpu,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class _UpperCAmelCase ( unittest.TestCase ):
a__ : Optional[int] = MODEL_FOR_MASKED_LM_MAPPING
a__ : Tuple = TF_MODEL_FOR_MASKED_LM_MAPPING
def a ( self : List[str] ):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
if is_torch_available():
import torch
torch.cuda.empty_cache()
@require_tf
def a ( self : Tuple ):
__UpperCAmelCase = pipeline(task='''fill-mask''' , model='''sshleifer/tiny-distilroberta-base''' , top_k=2 , framework='''tf''' )
__UpperCAmelCase = unmasker('''My name is <mask>''' )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
{'''sequence''': '''My name is grouped''', '''score''': 2.1E-05, '''token''': 3_80_15, '''token_str''': ''' grouped'''},
{'''sequence''': '''My name is accuser''', '''score''': 2.1E-05, '''token''': 2_55_06, '''token_str''': ''' accuser'''},
] , )
__UpperCAmelCase = unmasker('''The largest city in France is <mask>''' )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
{
'''sequence''': '''The largest city in France is grouped''',
'''score''': 2.1E-05,
'''token''': 3_80_15,
'''token_str''': ''' grouped''',
},
{
'''sequence''': '''The largest city in France is accuser''',
'''score''': 2.1E-05,
'''token''': 2_55_06,
'''token_str''': ''' accuser''',
},
] , )
__UpperCAmelCase = unmasker('''My name is <mask>''' , targets=[''' Patrick''', ''' Clara''', ''' Teven'''] , top_k=3 )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
{'''sequence''': '''My name is Clara''', '''score''': 2E-05, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
{'''sequence''': '''My name is Patrick''', '''score''': 2E-05, '''token''': 34_99, '''token_str''': ''' Patrick'''},
{'''sequence''': '''My name is Te''', '''score''': 1.9E-05, '''token''': 29_41, '''token_str''': ''' Te'''},
] , )
@require_torch
def a ( self : Optional[int] ):
__UpperCAmelCase = pipeline(task='''fill-mask''' , model='''sshleifer/tiny-distilroberta-base''' , top_k=2 , framework='''pt''' )
__UpperCAmelCase = unmasker('''My name is <mask>''' )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
{'''sequence''': '''My name is Maul''', '''score''': 2.2E-05, '''token''': 3_56_76, '''token_str''': ''' Maul'''},
{'''sequence''': '''My name isELS''', '''score''': 2.2E-05, '''token''': 1_64_16, '''token_str''': '''ELS'''},
] , )
__UpperCAmelCase = unmasker('''The largest city in France is <mask>''' )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
{
'''sequence''': '''The largest city in France is Maul''',
'''score''': 2.2E-05,
'''token''': 3_56_76,
'''token_str''': ''' Maul''',
},
{'''sequence''': '''The largest city in France isELS''', '''score''': 2.2E-05, '''token''': 1_64_16, '''token_str''': '''ELS'''},
] , )
__UpperCAmelCase = unmasker('''My name is <mask>''' , targets=[''' Patrick''', ''' Clara''', ''' Teven'''] , top_k=3 )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
{'''sequence''': '''My name is Patrick''', '''score''': 2.1E-05, '''token''': 34_99, '''token_str''': ''' Patrick'''},
{'''sequence''': '''My name is Te''', '''score''': 2E-05, '''token''': 29_41, '''token_str''': ''' Te'''},
{'''sequence''': '''My name is Clara''', '''score''': 2E-05, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
] , )
__UpperCAmelCase = unmasker('''My name is <mask> <mask>''' , top_k=2 )
self.assertEqual(
nested_simplify(_lowercase , decimals=6 ) , [
[
{
'''score''': 2.2E-05,
'''token''': 3_56_76,
'''token_str''': ''' Maul''',
'''sequence''': '''<s>My name is Maul<mask></s>''',
},
{'''score''': 2.2E-05, '''token''': 1_64_16, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
],
[
{
'''score''': 2.2E-05,
'''token''': 3_56_76,
'''token_str''': ''' Maul''',
'''sequence''': '''<s>My name is<mask> Maul</s>''',
},
{'''score''': 2.2E-05, '''token''': 1_64_16, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
],
] , )
@require_torch_gpu
def a ( self : Any ):
__UpperCAmelCase = pipeline('''fill-mask''' , model='''hf-internal-testing/tiny-random-distilbert''' , device=0 , framework='''pt''' )
# convert model to fp16
pipe.model.half()
__UpperCAmelCase = pipe('''Paris is the [MASK] of France.''' )
# We actually don't care about the result, we just want to make sure
# it works, meaning the float16 tensor got casted back to float32
# for postprocessing.
self.assertIsInstance(_lowercase , _lowercase )
@slow
@require_torch
def a ( self : int ):
__UpperCAmelCase = pipeline(task='''fill-mask''' , model='''distilroberta-base''' , top_k=2 , framework='''pt''' )
self.run_large_test(_lowercase )
@slow
@require_tf
def a ( self : Optional[Any] ):
__UpperCAmelCase = pipeline(task='''fill-mask''' , model='''distilroberta-base''' , top_k=2 , framework='''tf''' )
self.run_large_test(_lowercase )
def a ( self : Dict , _lowercase : str ):
__UpperCAmelCase = unmasker('''My name is <mask>''' )
self.assertEqual(
nested_simplify(_lowercase ) , [
{'''sequence''': '''My name is John''', '''score''': 0.008, '''token''': 6_10, '''token_str''': ''' John'''},
{'''sequence''': '''My name is Chris''', '''score''': 0.007, '''token''': 15_73, '''token_str''': ''' Chris'''},
] , )
__UpperCAmelCase = unmasker('''The largest city in France is <mask>''' )
self.assertEqual(
nested_simplify(_lowercase ) , [
{
'''sequence''': '''The largest city in France is Paris''',
'''score''': 0.251,
'''token''': 22_01,
'''token_str''': ''' Paris''',
},
{
'''sequence''': '''The largest city in France is Lyon''',
'''score''': 0.214,
'''token''': 1_27_90,
'''token_str''': ''' Lyon''',
},
] , )
__UpperCAmelCase = unmasker('''My name is <mask>''' , targets=[''' Patrick''', ''' Clara''', ''' Teven'''] , top_k=3 )
self.assertEqual(
nested_simplify(_lowercase ) , [
{'''sequence''': '''My name is Patrick''', '''score''': 0.005, '''token''': 34_99, '''token_str''': ''' Patrick'''},
{'''sequence''': '''My name is Clara''', '''score''': 0.000, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
{'''sequence''': '''My name is Te''', '''score''': 0.000, '''token''': 29_41, '''token_str''': ''' Te'''},
] , )
@require_torch
def a ( self : List[Any] ):
__UpperCAmelCase = pipeline(task='''fill-mask''' , model='''sshleifer/tiny-distilroberta-base''' , framework='''pt''' )
__UpperCAmelCase = None
__UpperCAmelCase = None
self.run_pipeline_test(_lowercase , [] )
@require_tf
def a ( self : str ):
__UpperCAmelCase = pipeline(task='''fill-mask''' , model='''sshleifer/tiny-distilroberta-base''' , framework='''tf''' )
__UpperCAmelCase = None
__UpperCAmelCase = None
self.run_pipeline_test(_lowercase , [] )
def a ( self : Optional[int] , _lowercase : Optional[Any] , _lowercase : Optional[int] , _lowercase : Tuple ):
if tokenizer is None or tokenizer.mask_token_id is None:
self.skipTest('''The provided tokenizer has no mask token, (probably reformer or wav2vec2)''' )
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase )
__UpperCAmelCase = [
F'''This is another {tokenizer.mask_token} test''',
]
return fill_masker, examples
def a ( self : int , _lowercase : Tuple , _lowercase : Tuple ):
__UpperCAmelCase = fill_masker.tokenizer
__UpperCAmelCase = fill_masker.model
__UpperCAmelCase = fill_masker(
F'''This is a {tokenizer.mask_token}''' , )
self.assertEqual(
_lowercase , [
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
] , )
__UpperCAmelCase = fill_masker([F'''This is a {tokenizer.mask_token}'''] )
self.assertEqual(
_lowercase , [
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
] , )
__UpperCAmelCase = fill_masker([F'''This is a {tokenizer.mask_token}''', F'''Another {tokenizer.mask_token} great test.'''] )
self.assertEqual(
_lowercase , [
[
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
],
[
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
],
] , )
with self.assertRaises(_lowercase ):
fill_masker([None] )
# No mask_token is not supported
with self.assertRaises(_lowercase ):
fill_masker('''This is''' )
self.run_test_top_k(_lowercase , _lowercase )
self.run_test_targets(_lowercase , _lowercase )
self.run_test_top_k_targets(_lowercase , _lowercase )
self.fill_mask_with_duplicate_targets_and_top_k(_lowercase , _lowercase )
self.fill_mask_with_multiple_masks(_lowercase , _lowercase )
def a ( self : Optional[Any] , _lowercase : Optional[Any] , _lowercase : List[Any] ):
__UpperCAmelCase = tokenizer.get_vocab()
__UpperCAmelCase = sorted(vocab.keys() )[:2]
# Pipeline argument
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase , targets=_lowercase )
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' )
self.assertEqual(
_lowercase , [
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
] , )
__UpperCAmelCase = {vocab[el] for el in targets}
self.assertEqual({el['''token'''] for el in outputs} , _lowercase )
__UpperCAmelCase = [tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el['''token_str'''] for el in outputs} , set(_lowercase ) )
# Call argument
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase )
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , targets=_lowercase )
self.assertEqual(
_lowercase , [
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
] , )
__UpperCAmelCase = {vocab[el] for el in targets}
self.assertEqual({el['''token'''] for el in outputs} , _lowercase )
__UpperCAmelCase = [tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el['''token_str'''] for el in outputs} , set(_lowercase ) )
# Score equivalence
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , targets=_lowercase )
__UpperCAmelCase = [top_mask['''token_str'''] for top_mask in outputs]
__UpperCAmelCase = [top_mask['''score'''] for top_mask in outputs]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(_lowercase ) == set(_lowercase ):
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , targets=_lowercase )
__UpperCAmelCase = [top_mask['''score'''] for top_mask in unmasked_targets]
self.assertEqual(nested_simplify(_lowercase ) , nested_simplify(_lowercase ) )
# Raises with invalid
with self.assertRaises(_lowercase ):
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , targets=[] )
# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
if "" not in tokenizer.get_vocab():
with self.assertRaises(_lowercase ):
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , targets=[''''''] )
with self.assertRaises(_lowercase ):
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , targets='''''' )
def a ( self : List[Any] , _lowercase : Tuple , _lowercase : Optional[Any] ):
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase , top_k=2 )
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' )
self.assertEqual(
_lowercase , [
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
] , )
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase )
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , top_k=2 )
self.assertEqual(
_lowercase , [
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
] , )
self.assertEqual(nested_simplify(_lowercase ) , nested_simplify(_lowercase ) )
def a ( self : Optional[int] , _lowercase : int , _lowercase : Tuple ):
__UpperCAmelCase = tokenizer.get_vocab()
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase )
# top_k=2, ntargets=3
__UpperCAmelCase = sorted(vocab.keys() )[:3]
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , top_k=2 , targets=_lowercase )
# If we use the most probably targets, and filter differently, we should still
# have the same results
__UpperCAmelCase = [el['''token_str'''] for el in sorted(_lowercase , key=lambda _lowercase : x["score"] , reverse=_lowercase )]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(_lowercase ).issubset(_lowercase ):
__UpperCAmelCase = fill_masker(F'''This is a {tokenizer.mask_token}''' , top_k=3 , targets=_lowercase )
# They should yield exactly the same result
self.assertEqual(nested_simplify(_lowercase ) , nested_simplify(_lowercase ) )
def a ( self : Union[str, Any] , _lowercase : Tuple , _lowercase : Union[str, Any] ):
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase )
__UpperCAmelCase = tokenizer.get_vocab()
# String duplicates + id duplicates
__UpperCAmelCase = sorted(vocab.keys() )[:3]
__UpperCAmelCase = [targets[0], targets[1], targets[0], targets[2], targets[1]]
__UpperCAmelCase = fill_masker(F'''My name is {tokenizer.mask_token}''' , targets=_lowercase , top_k=10 )
# The target list contains duplicates, so we can't output more
# than them
self.assertEqual(len(_lowercase ) , 3 )
def a ( self : Dict , _lowercase : Dict , _lowercase : Any ):
__UpperCAmelCase = FillMaskPipeline(model=_lowercase , tokenizer=_lowercase )
__UpperCAmelCase = fill_masker(
F'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}''' , top_k=2 )
self.assertEqual(
_lowercase , [
[
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
],
[
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
],
[
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
{'''sequence''': ANY(_lowercase ), '''score''': ANY(_lowercase ), '''token''': ANY(_lowercase ), '''token_str''': ANY(_lowercase )},
],
] , )
| 86
| 0
|
'''simple docstring'''
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def _A ( snake_case , snake_case , snake_case ) -> Optional[int]:
_lowercase : Tuple = os.path.abspath(snake_case )
logger.info(F'''Converting TensorFlow checkpoint from {tf_path}''' )
# Load weights from TF model
_lowercase : Optional[Any] = tf.train.list_variables(snake_case )
_lowercase : Optional[int] = []
_lowercase : List[Any] = []
_lowercase : Union[str, Any] = []
for full_name, shape in init_vars:
# logger.info(f"Loading TF weight {name} with shape {shape}")
_lowercase : Tuple = full_name.split("/" )
if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
logger.info(F'''Skipping non-model layer {full_name}''' )
continue
if "optimizer" in full_name:
logger.info(F'''Skipping optimization layer {full_name}''' )
continue
if name[0] == "model":
# ignore initial 'model'
_lowercase : str = name[1:]
# figure out how many levels deep the name is
_lowercase : str = 0
for _name in name:
if _name.startswith("layer_with_weights" ):
depth += 1
else:
break
layer_depth.append(snake_case )
# read data
_lowercase : str = tf.train.load_variable(snake_case , snake_case )
names.append("/".join(snake_case ) )
arrays.append(snake_case )
logger.info(F'''Read a total of {len(snake_case ):,} layers''' )
# Sanity check
if len(set(snake_case ) ) != 1:
raise ValueError(F'''Found layer names with different depths (layer depth {list(set(snake_case ) )})''' )
_lowercase : Optional[Any] = list(set(snake_case ) )[0]
if layer_depth != 1:
raise ValueError(
"The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
" heads." )
# convert layers
logger.info("Converting weights..." )
for full_name, array in zip(snake_case , snake_case ):
_lowercase : Optional[int] = full_name.split("/" )
_lowercase : Union[str, Any] = model
_lowercase : int = []
for i, m_name in enumerate(snake_case ):
if m_name == ".ATTRIBUTES":
# variable names end with .ATTRIBUTES/VARIABLE_VALUE
break
if m_name.startswith("layer_with_weights" ):
_lowercase : Any = int(m_name.split("-" )[-1] )
if layer_num <= 2:
# embedding layers
# layer_num 0: word_embeddings
# layer_num 1: position_embeddings
# layer_num 2: token_type_embeddings
continue
elif layer_num == 3:
# embedding LayerNorm
trace.extend(["embeddings", "LayerNorm"] )
_lowercase : str = getattr(snake_case , "embeddings" )
_lowercase : int = getattr(snake_case , "LayerNorm" )
elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
# encoder layers
trace.extend(["encoder", "layer", str(layer_num - 4 )] )
_lowercase : int = getattr(snake_case , "encoder" )
_lowercase : Union[str, Any] = getattr(snake_case , "layer" )
_lowercase : Optional[int] = pointer[layer_num - 4]
elif layer_num == config.num_hidden_layers + 4:
# pooler layer
trace.extend(["pooler", "dense"] )
_lowercase : List[str] = getattr(snake_case , "pooler" )
_lowercase : str = getattr(snake_case , "dense" )
elif m_name == "embeddings":
trace.append("embeddings" )
_lowercase : Optional[Any] = getattr(snake_case , "embeddings" )
if layer_num == 0:
trace.append("word_embeddings" )
_lowercase : Tuple = getattr(snake_case , "word_embeddings" )
elif layer_num == 1:
trace.append("position_embeddings" )
_lowercase : List[Any] = getattr(snake_case , "position_embeddings" )
elif layer_num == 2:
trace.append("token_type_embeddings" )
_lowercase : int = getattr(snake_case , "token_type_embeddings" )
else:
raise ValueError(F'''Unknown embedding layer with name {full_name}''' )
trace.append("weight" )
_lowercase : str = getattr(snake_case , "weight" )
elif m_name == "_attention_layer":
# self-attention layer
trace.extend(["attention", "self"] )
_lowercase : Dict = getattr(snake_case , "attention" )
_lowercase : Optional[int] = getattr(snake_case , "self" )
elif m_name == "_attention_layer_norm":
# output attention norm
trace.extend(["attention", "output", "LayerNorm"] )
_lowercase : List[Any] = getattr(snake_case , "attention" )
_lowercase : Optional[int] = getattr(snake_case , "output" )
_lowercase : Dict = getattr(snake_case , "LayerNorm" )
elif m_name == "_attention_output_dense":
# output attention dense
trace.extend(["attention", "output", "dense"] )
_lowercase : Dict = getattr(snake_case , "attention" )
_lowercase : int = getattr(snake_case , "output" )
_lowercase : Union[str, Any] = getattr(snake_case , "dense" )
elif m_name == "_output_dense":
# output dense
trace.extend(["output", "dense"] )
_lowercase : Dict = getattr(snake_case , "output" )
_lowercase : Optional[Any] = getattr(snake_case , "dense" )
elif m_name == "_output_layer_norm":
# output dense
trace.extend(["output", "LayerNorm"] )
_lowercase : Optional[Any] = getattr(snake_case , "output" )
_lowercase : List[str] = getattr(snake_case , "LayerNorm" )
elif m_name == "_key_dense":
# attention key
trace.append("key" )
_lowercase : List[Any] = getattr(snake_case , "key" )
elif m_name == "_query_dense":
# attention query
trace.append("query" )
_lowercase : Optional[int] = getattr(snake_case , "query" )
elif m_name == "_value_dense":
# attention value
trace.append("value" )
_lowercase : str = getattr(snake_case , "value" )
elif m_name == "_intermediate_dense":
# attention intermediate dense
trace.extend(["intermediate", "dense"] )
_lowercase : List[Any] = getattr(snake_case , "intermediate" )
_lowercase : Any = getattr(snake_case , "dense" )
elif m_name == "_output_layer_norm":
# output layer norm
trace.append("output" )
_lowercase : Union[str, Any] = getattr(snake_case , "output" )
# weights & biases
elif m_name in ["bias", "beta"]:
trace.append("bias" )
_lowercase : Optional[Any] = getattr(snake_case , "bias" )
elif m_name in ["kernel", "gamma"]:
trace.append("weight" )
_lowercase : List[Any] = getattr(snake_case , "weight" )
else:
logger.warning(F'''Ignored {m_name}''' )
# for certain layers reshape is necessary
_lowercase : Tuple = ".".join(snake_case )
if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)" , snake_case ) or re.match(
r"(\S+)\.attention\.output\.dense\.weight" , snake_case ):
_lowercase : int = array.reshape(pointer.data.shape )
if "kernel" in full_name:
_lowercase : str = array.transpose()
if pointer.shape == array.shape:
_lowercase : List[Any] = torch.from_numpy(snake_case )
else:
raise ValueError(
F'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
F''' {array.shape}''' )
logger.info(F'''Successfully set variable {full_name} to PyTorch layer {trace}''' )
return model
def _A ( snake_case , snake_case , snake_case ) -> str:
# Instantiate model
logger.info(F'''Loading model based on config from {config_path}...''' )
_lowercase : Union[str, Any] = BertConfig.from_json_file(snake_case )
_lowercase : Optional[int] = BertModel(snake_case )
# Load weights from checkpoint
logger.info(F'''Loading weights from checkpoint {tf_checkpoint_path}...''' )
load_tfa_weights_in_bert(snake_case , snake_case , snake_case )
# Save pytorch-model
logger.info(F'''Saving PyTorch model to {pytorch_dump_path}...''' )
torch.save(model.state_dict() , snake_case )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
'--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
)
parser.add_argument(
'--bert_config_file',
type=str,
required=True,
help='The config json file corresponding to the BERT model. This specifies the model architecture.',
)
parser.add_argument(
'--pytorch_dump_path',
type=str,
required=True,
help='Path to the output PyTorch model (must include filename).',
)
_snake_case = parser.parse_args()
convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 250
|
'''simple docstring'''
class a__ :
def __init__( self , _UpperCamelCase ):
"""simple docstring"""
_lowercase : Tuple = n
_lowercase : Any = [None] * self.n
_lowercase : Tuple = 0 # index of the first element
_lowercase : Union[str, Any] = 0
_lowercase : str = 0
def __len__( self ):
"""simple docstring"""
return self.size
def _lowerCamelCase ( self ):
"""simple docstring"""
return self.size == 0
def _lowerCamelCase ( self ):
"""simple docstring"""
return False if self.is_empty() else self.array[self.front]
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
if self.size >= self.n:
raise Exception("QUEUE IS FULL" )
_lowercase : Optional[int] = data
_lowercase : Dict = (self.rear + 1) % self.n
self.size += 1
return self
def _lowerCamelCase ( self ):
"""simple docstring"""
if self.size == 0:
raise Exception("UNDERFLOW" )
_lowercase : Optional[Any] = self.array[self.front]
_lowercase : List[Any] = None
_lowercase : int = (self.front + 1) % self.n
self.size -= 1
return temp
| 250
| 1
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
lowercase__ : str = None
lowercase__ : List[Any] = logging.get_logger(__name__)
lowercase__ : int = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
lowercase__ : Union[str, Any] = {
'vocab_file': {
'facebook/mbart-large-en-ro': (
'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
),
'facebook/mbart-large-cc25': (
'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
),
},
'tokenizer_file': {
'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
},
}
lowercase__ : Union[str, Any] = {
'facebook/mbart-large-en-ro': 10_24,
'facebook/mbart-large-cc25': 10_24,
}
# fmt: off
lowercase__ : Dict = ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : Any = VOCAB_FILES_NAMES
_snake_case : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_snake_case : Any = ['input_ids', 'attention_mask']
_snake_case : str = MBartTokenizer
_snake_case : List[int] = []
_snake_case : List[int] = []
def __init__( self : Union[str, Any] , lowerCAmelCase__ : Optional[int]=None , lowerCAmelCase__ : Dict=None , lowerCAmelCase__ : int="<s>" , lowerCAmelCase__ : str="</s>" , lowerCAmelCase__ : str="</s>" , lowerCAmelCase__ : Any="<s>" , lowerCAmelCase__ : List[str]="<unk>" , lowerCAmelCase__ : str="<pad>" , lowerCAmelCase__ : Union[str, Any]="<mask>" , lowerCAmelCase__ : List[str]=None , lowerCAmelCase__ : List[str]=None , lowerCAmelCase__ : Any=None , **lowerCAmelCase__ : Any , ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else mask_token
super().__init__(
vocab_file=lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , src_lang=lowerCAmelCase__ , tgt_lang=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , **lowerCAmelCase__ , )
_UpperCamelCase = vocab_file
_UpperCamelCase = False if not self.vocab_file else True
_UpperCamelCase = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
_UpperCamelCase = {
lang_code: self.convert_tokens_to_ids(lowerCAmelCase__ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
_UpperCamelCase = src_lang if src_lang is not None else '''en_XX'''
_UpperCamelCase = self.convert_tokens_to_ids(self._src_lang )
_UpperCamelCase = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def snake_case__ ( self : int ) -> str:
'''simple docstring'''
return self._src_lang
@src_lang.setter
def snake_case__ ( self : Optional[int] , lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
_UpperCamelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def snake_case__ ( self : Union[str, Any] , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def snake_case__ ( self : Tuple , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
_UpperCamelCase = [self.sep_token_id]
_UpperCamelCase = [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]
def snake_case__ ( self : Tuple , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] , lowerCAmelCase__ : Optional[str] , **lowerCAmelCase__ : List[str] ) -> Optional[int]:
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
_UpperCamelCase = src_lang
_UpperCamelCase = self(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCamelCase = self.convert_tokens_to_ids(lowerCAmelCase__ )
_UpperCamelCase = tgt_lang_id
return inputs
def snake_case__ ( self : str , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str = "en_XX" , lowerCAmelCase__ : Optional[List[str]] = None , lowerCAmelCase__ : str = "ro_RO" , **lowerCAmelCase__ : Tuple , ) -> BatchEncoding:
'''simple docstring'''
_UpperCamelCase = src_lang
_UpperCamelCase = tgt_lang
return super().prepare_seqaseq_batch(lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ )
def snake_case__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang )
def snake_case__ ( self : int ) -> str:
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def snake_case__ ( self : Dict , lowerCAmelCase__ : Union[str, Any] ) -> None:
'''simple docstring'''
_UpperCamelCase = self.convert_tokens_to_ids(lowerCAmelCase__ )
_UpperCamelCase = []
_UpperCamelCase = [self.eos_token_id, self.cur_lang_code]
_UpperCamelCase = self.convert_ids_to_tokens(self.prefix_tokens )
_UpperCamelCase = self.convert_ids_to_tokens(self.suffix_tokens )
_UpperCamelCase = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def snake_case__ ( self : Optional[Any] , lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
_UpperCamelCase = self.convert_tokens_to_ids(lowerCAmelCase__ )
_UpperCamelCase = []
_UpperCamelCase = [self.eos_token_id, self.cur_lang_code]
_UpperCamelCase = self.convert_ids_to_tokens(self.prefix_tokens )
_UpperCamelCase = self.convert_ids_to_tokens(self.suffix_tokens )
_UpperCamelCase = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def snake_case__ ( self : str , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""" )
return
_UpperCamelCase = os.path.join(
lowerCAmelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ):
copyfile(self.vocab_file , lowerCAmelCase__ )
return (out_vocab_file,)
| 287
|
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowercase__ : List[Any] = parse(importlib.metadata.version('torch'))
def a__ ( lowercase : Union[str, Version], lowercase : str, lowercase : str ) -> List[str]:
"""simple docstring"""
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(F"""`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}""" )
_UpperCamelCase = STR_OPERATION_TO_FUNC[operation]
if isinstance(lowercase, lowercase ):
_UpperCamelCase = parse(importlib.metadata.version(lowercase ) )
return operation(lowercase, parse(lowercase ) )
def a__ ( lowercase : str, lowercase : str ) -> List[Any]:
"""simple docstring"""
return compare_versions(lowercase, lowercase, lowercase )
| 287
| 1
|
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
a__ : Any = 4
a__ : Dict = 3
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
pass
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
for shard in shards:
for i in range(lowerCAmelCase_ ):
yield {"i": i, "shard": shard}
def UpperCAmelCase__ ():
'''simple docstring'''
__SCREAMING_SNAKE_CASE = int(os.environ["RANK"] )
__SCREAMING_SNAKE_CASE = int(os.environ["WORLD_SIZE"] )
__SCREAMING_SNAKE_CASE = ArgumentParser()
parser.add_argument("--streaming" , type=lowerCAmelCase_ )
parser.add_argument("--local_rank" , type=lowerCAmelCase_ )
parser.add_argument("--num_workers" , type=lowerCAmelCase_ , default=0 )
__SCREAMING_SNAKE_CASE = parser.parse_args()
__SCREAMING_SNAKE_CASE = args.streaming
__SCREAMING_SNAKE_CASE = args.num_workers
__SCREAMING_SNAKE_CASE = {"shards": [f"""shard_{shard_idx}""" for shard_idx in range(lowerCAmelCase_ )]}
__SCREAMING_SNAKE_CASE = IterableDataset.from_generator(lowerCAmelCase_ , gen_kwargs=lowerCAmelCase_ )
if not streaming:
__SCREAMING_SNAKE_CASE = Dataset.from_list(list(lowerCAmelCase_ ) )
__SCREAMING_SNAKE_CASE = split_dataset_by_node(lowerCAmelCase_ , rank=lowerCAmelCase_ , world_size=lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = torch.utils.data.DataLoader(lowerCAmelCase_ , num_workers=lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = NUM_SHARDS * NUM_ITEMS_PER_SHARD
__SCREAMING_SNAKE_CASE = full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
__SCREAMING_SNAKE_CASE = sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}""" )
if __name__ == "__main__":
main()
| 54
|
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowerCamelCase__ ( ctypes.Structure ):
"""simple docstring"""
__a = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def lowerCamelCase ( ) -> Optional[int]:
'''simple docstring'''
if os.name == "nt":
__UpperCAmelCase : Dict = CursorInfo()
__UpperCAmelCase : Any = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase , ctypes.byref(_UpperCamelCase ) )
__UpperCAmelCase : Tuple = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase , ctypes.byref(_UpperCamelCase ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> Optional[int]:
'''simple docstring'''
if os.name == "nt":
__UpperCAmelCase : str = CursorInfo()
__UpperCAmelCase : int = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase , ctypes.byref(_UpperCamelCase ) )
__UpperCAmelCase : Union[str, Any] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase , ctypes.byref(_UpperCamelCase ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> str:
'''simple docstring'''
try:
hide_cursor()
yield
finally:
show_cursor()
| 115
| 0
|
def lowerCAmelCase_ ( __A, __A, __A, __A, __A, __A ) -> Tuple:
'''simple docstring'''
if index == r:
for j in range(__A ):
print(data[j], end=" " )
print(" " )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
UpperCAmelCase__ = arr[i]
combination_util(__A, __A, __A, index + 1, __A, i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(__A, __A, __A, __A, __A, i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def lowerCAmelCase_ ( __A, __A, __A ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(__A, __A, __A, 0, __A, 0 )
if __name__ == "__main__":
# Driver code to check the function above
UpperCamelCase__ = [1_0, 2_0, 3_0, 4_0, 5_0]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 143
|
from __future__ import annotations
def lowerCAmelCase_ ( __A ) -> bool:
'''simple docstring'''
UpperCAmelCase__ = str(__A )
return n == n[::-1]
def lowerCAmelCase_ ( __A = 1_000_000 ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ = 0
for i in range(1, __A ):
if is_palindrome(__A ) and is_palindrome(bin(__A ).split("b" )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 143
| 1
|
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = [int(A_ ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(A_ ) == 4 and all(0 <= int(A_ ) <= 254 for octet in octets )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = input().strip()
__lowerCAmelCase : int = 'valid' if is_ip_va_address_valid(ip) else 'invalid'
print(F'''{ip} is a {valid_or_invalid} IP v4 address.''')
| 88
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
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 ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _A ( _a ,unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase : Dict = KandinskyVaaControlnetPipeline
UpperCAmelCase : List[str] = ["""image_embeds""", """negative_image_embeds""", """hint"""]
UpperCAmelCase : Optional[Any] = ["""image_embeds""", """negative_image_embeds""", """hint"""]
UpperCAmelCase : Dict = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
UpperCAmelCase : Optional[int] = False
@property
def __snake_case ( self : Optional[Any]):
return 32
@property
def __snake_case ( self : Dict):
return 32
@property
def __snake_case ( self : Dict):
return self.time_input_dim
@property
def __snake_case ( self : Any):
return self.time_input_dim * 4
@property
def __snake_case ( self : str):
return 100
@property
def __snake_case ( self : str):
torch.manual_seed(0)
a : str = {
"in_channels": 8,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "image_hint",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
a : Dict = UNetaDConditionModel(**__UpperCAmelCase)
return model
@property
def __snake_case ( self : str):
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def __snake_case ( self : Union[str, Any]):
torch.manual_seed(0)
a : Dict = VQModel(**self.dummy_movq_kwargs)
return model
def __snake_case ( self : Optional[Any]):
a : Optional[Any] = self.dummy_unet
a : int = self.dummy_movq
a : str = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule="linear" , beta_start=0.00_085 , beta_end=0.012 , clip_sample=__UpperCAmelCase , set_alpha_to_one=__UpperCAmelCase , steps_offset=1 , prediction_type="epsilon" , thresholding=__UpperCAmelCase , )
a : Optional[Any] = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def __snake_case ( self : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : int=0):
a : Optional[int] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
a : List[str] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1)).to(
__UpperCAmelCase)
# create hint
a : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
if str(__UpperCAmelCase).startswith("mps"):
a : Union[str, Any] = torch.manual_seed(__UpperCAmelCase)
else:
a : List[Any] = torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
a : str = {
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"hint": hint,
"generator": generator,
"height": 64,
"width": 64,
"guidance_scale": 4.0,
"num_inference_steps": 2,
"output_type": "np",
}
return inputs
def __snake_case ( self : Dict):
a : str = "cpu"
a : Tuple = self.get_dummy_components()
a : Dict = self.pipeline_class(**__UpperCAmelCase)
a : Optional[int] = pipe.to(__UpperCAmelCase)
pipe.set_progress_bar_config(disable=__UpperCAmelCase)
a : Optional[Any] = pipe(**self.get_dummy_inputs(__UpperCAmelCase))
a : Any = output.images
a : Any = pipe(
**self.get_dummy_inputs(__UpperCAmelCase) , return_dict=__UpperCAmelCase , )[0]
a : Union[str, Any] = image[0, -3:, -3:, -1]
a : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a : Tuple = np.array(
[0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595])
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : Optional[int]):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __snake_case ( self : List[str]):
a : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy")
a : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/hint_image_cat.png")
a : List[Any] = torch.from_numpy(np.array(__UpperCAmelCase)).float() / 255.0
a : str = hint.permute(2 , 0 , 1).unsqueeze(0)
a : Optional[int] = KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior" , torch_dtype=torch.floataa)
pipe_prior.to(__UpperCAmelCase)
a : List[str] = KandinskyVaaControlnetPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-controlnet-depth" , torch_dtype=torch.floataa)
a : int = pipeline.to(__UpperCAmelCase)
pipeline.set_progress_bar_config(disable=__UpperCAmelCase)
a : Tuple = "A robot, 4k photo"
a : Any = torch.Generator(device="cuda").manual_seed(0)
a , a : int = pipe_prior(
__UpperCAmelCase , generator=__UpperCAmelCase , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
a : str = torch.Generator(device="cuda").manual_seed(0)
a : Union[str, Any] = pipeline(
image_embeds=__UpperCAmelCase , negative_image_embeds=__UpperCAmelCase , hint=__UpperCAmelCase , generator=__UpperCAmelCase , num_inference_steps=100 , output_type="np" , )
a : str = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(__UpperCAmelCase , __UpperCAmelCase)
| 40
| 0
|
"""simple docstring"""
def UpperCamelCase__( UpperCamelCase__ : str )->Optional[Any]:
A__ = []
A__ = []
A__ = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''': 1,
} # Priority of each operator
A__ = len(UpperCamelCase__ ) if (len(UpperCamelCase__ ) > 7) else 7
# Print table header for output
print(
'''Symbol'''.center(8 ) , '''Stack'''.center(UpperCamelCase__ ) , '''Postfix'''.center(UpperCamelCase__ ) , sep=''' | ''' , )
print('''-''' * (print_width * 3 + 7) )
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(UpperCamelCase__ ) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(UpperCamelCase__ ) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop() ) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(UpperCamelCase__ ) == 0:
stack.append(UpperCamelCase__ ) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(UpperCamelCase__ ) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop() ) # pop stack & add to Postfix
stack.append(UpperCamelCase__ ) # push x to stack
print(
x.center(8 ) , (''''''.join(UpperCamelCase__ )).ljust(UpperCamelCase__ ) , (''''''.join(UpperCamelCase__ )).ljust(UpperCamelCase__ ) , sep=''' | ''' , ) # Output in tabular format
while len(UpperCamelCase__ ) > 0: # while stack is not empty
post_fix.append(stack.pop() ) # pop stack & add to Postfix
print(
''' '''.center(8 ) , (''''''.join(UpperCamelCase__ )).ljust(UpperCamelCase__ ) , (''''''.join(UpperCamelCase__ )).ljust(UpperCamelCase__ ) , sep=''' | ''' , ) # Output in tabular format
return "".join(UpperCamelCase__ ) # return Postfix as str
def UpperCamelCase__( UpperCamelCase__ : Dict )->Dict:
A__ = list(infix[::-1] ) # reverse the infix equation
for i in range(len(UpperCamelCase__ ) ):
if infix[i] == "(":
A__ = ''')''' # change "(" to ")"
elif infix[i] == ")":
A__ = '''(''' # change ")" to "("
return (infix_2_postfix(''''''.join(UpperCamelCase__ ) ))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
a__: Optional[Any] = input('\nEnter an Infix Equation = ') # Input an Infix equation
a__: int = ''.join(Infix.split()) # Remove spaces from the input
print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')
| 352
|
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
a__: str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a__: list[int] = [ord(letter) for letter in string.ascii_lowercase]
a__: set[int] = {ord(char) for char in VALID_CHARS}
a__: list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def UpperCamelCase__( UpperCamelCase__ : list[int] , UpperCamelCase__ : tuple[int, ...] )->str | None:
A__ = ""
A__ = 42
A__ = 42
A__ = 42
for keychar, cipherchar in zip(cycle(UpperCamelCase__ ) , UpperCamelCase__ ):
A__ = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(UpperCamelCase__ )
return decoded
def UpperCamelCase__( UpperCamelCase__ : list[int] )->list[str]:
A__ = []
for key in product(UpperCamelCase__ , repeat=3 ):
A__ = try_key(UpperCamelCase__ , UpperCamelCase__ )
if encoded is not None:
possibles.append(UpperCamelCase__ )
return possibles
def UpperCamelCase__( UpperCamelCase__ : list[str] , UpperCamelCase__ : str )->list[str]:
return [possible for possible in possibles if common_word in possible.lower()]
def UpperCamelCase__( UpperCamelCase__ : str = "p059_cipher.txt" )->int:
A__ = 42
A__ = 42
A__ = 42
A__ = 42
A__ = Path(UpperCamelCase__ ).parent.joinpath(UpperCamelCase__ ).read_text(encoding='''utf-8''' )
A__ = [int(UpperCamelCase__ ) for number in data.strip().split(''',''' )]
A__ = filter_valid_chars(UpperCamelCase__ )
for common_word in COMMON_WORDS:
A__ = filter_common_word(UpperCamelCase__ , UpperCamelCase__ )
if len(UpperCamelCase__ ) == 1:
break
A__ = possibles[0]
return sum(ord(UpperCamelCase__ ) for char in decoded_text )
if __name__ == "__main__":
print(F"{solution() = }")
| 39
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase : Dict = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : List[str] = ['DeiTFeatureExtractor']
lowerCamelCase : Optional[int] = ['DeiTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : List[str] = [
'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DeiTForImageClassification',
'DeiTForImageClassificationWithTeacher',
'DeiTForMaskedImageModeling',
'DeiTModel',
'DeiTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Optional[Any] = [
'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDeiTForImageClassification',
'TFDeiTForImageClassificationWithTeacher',
'TFDeiTForMaskedImageModeling',
'TFDeiTModel',
'TFDeiTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 2
|
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class A__ ( _lowerCamelCase):
A_ : Any = ['image_processor', 'tokenizer']
A_ : Optional[Any] = 'AutoImageProcessor'
A_ : str = 'AutoTokenizer'
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Union[str, Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , _SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Any = kwargs.pop('feature_extractor' )
__lowerCAmelCase : str = 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__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = self.image_processor
__lowerCAmelCase : Tuple = False
def __call__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = kwargs.pop('images' , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = kwargs.pop('text' , _SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
__lowerCAmelCase : Dict = args[0]
__lowerCAmelCase : Union[str, Any] = args[1:]
if images is None and text is None:
raise ValueError('You need to specify either an `images` or `text` input to process.' )
if images is not None:
__lowerCAmelCase : Union[str, Any] = self.image_processor(_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if text is not None:
__lowerCAmelCase : Dict = self.tokenizer(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if text is None:
return inputs
elif images is None:
return encodings
else:
__lowerCAmelCase : Union[str, Any] = encodings['input_ids']
return inputs
def __lowerCamelCase ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@contextmanager
def __lowerCamelCase ( self ):
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your images inputs, or in a separate call.' )
__lowerCAmelCase : Any = True
__lowerCAmelCase : Dict = self.tokenizer
yield
__lowerCAmelCase : Optional[int] = self.image_processor
__lowerCAmelCase : Optional[Any] = False
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=None ):
if added_vocab is None:
__lowerCAmelCase : str = self.tokenizer.get_added_vocab()
__lowerCAmelCase : List[Any] = {}
while tokens:
__lowerCAmelCase : int = re.search(R'<s_(.*?)>' , _SCREAMING_SNAKE_CASE , re.IGNORECASE )
if start_token is None:
break
__lowerCAmelCase : Union[str, Any] = start_token.group(1 )
__lowerCAmelCase : Tuple = re.search(Rf"</s_{key}>" , _SCREAMING_SNAKE_CASE , re.IGNORECASE )
__lowerCAmelCase : str = start_token.group()
if end_token is None:
__lowerCAmelCase : Optional[int] = tokens.replace(_SCREAMING_SNAKE_CASE , '' )
else:
__lowerCAmelCase : Optional[Any] = end_token.group()
__lowerCAmelCase : Tuple = re.escape(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Tuple = re.escape(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = re.search(f"{start_token_escaped}(.*?){end_token_escaped}" , _SCREAMING_SNAKE_CASE , re.IGNORECASE )
if content is not None:
__lowerCAmelCase : List[str] = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
__lowerCAmelCase : int = self.tokenajson(_SCREAMING_SNAKE_CASE , is_inner_value=_SCREAMING_SNAKE_CASE , added_vocab=_SCREAMING_SNAKE_CASE )
if value:
if len(_SCREAMING_SNAKE_CASE ) == 1:
__lowerCAmelCase : Tuple = value[0]
__lowerCAmelCase : Tuple = value
else: # leaf nodes
__lowerCAmelCase : Any = []
for leaf in content.split(R'<sep/>' ):
__lowerCAmelCase : List[Any] = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
__lowerCAmelCase : Dict = leaf[1:-2] # for categorical special tokens
output[key].append(_SCREAMING_SNAKE_CASE )
if len(output[key] ) == 1:
__lowerCAmelCase : str = output[key][0]
__lowerCAmelCase : Dict = tokens[tokens.find(_SCREAMING_SNAKE_CASE ) + len(_SCREAMING_SNAKE_CASE ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=_SCREAMING_SNAKE_CASE , added_vocab=_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def __lowerCamelCase ( self ):
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , _SCREAMING_SNAKE_CASE , )
return self.image_processor_class
@property
def __lowerCamelCase ( self ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , _SCREAMING_SNAKE_CASE , )
return self.image_processor
| 86
| 0
|
"""simple docstring"""
import numpy as np
class lowercase_ :
def __init__( self , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None ):
self.set_matricies(red=__snake_case , green=__snake_case , blue=__snake_case , red_edge=__snake_case , nir=__snake_case )
def UpperCamelCase ( self , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None ):
if red is not None:
_snake_case : Optional[int] = red
if green is not None:
_snake_case : Optional[Any] = green
if blue is not None:
_snake_case : Tuple = blue
if red_edge is not None:
_snake_case : str = red_edge
if nir is not None:
_snake_case : List[Any] = nir
return True
def UpperCamelCase ( self , lowercase_="" , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None ):
self.set_matricies(red=__snake_case , green=__snake_case , blue=__snake_case , red_edge=__snake_case , nir=__snake_case )
_snake_case : int = {
'ARVI2': self.arvaa,
'CCCI': self.ccci,
'CVI': self.cvi,
'GLI': self.gli,
'NDVI': self.ndvi,
'BNDVI': self.bndvi,
'redEdgeNDVI': self.red_edge_ndvi,
'GNDVI': self.gndvi,
'GBNDVI': self.gbndvi,
'GRNDVI': self.grndvi,
'RBNDVI': self.rbndvi,
'PNDVI': self.pndvi,
'ATSAVI': self.atsavi,
'BWDRVI': self.bwdrvi,
'CIgreen': self.ci_green,
'CIrededge': self.ci_rededge,
'CI': self.ci,
'CTVI': self.ctvi,
'GDVI': self.gdvi,
'EVI': self.evi,
'GEMI': self.gemi,
'GOSAVI': self.gosavi,
'GSAVI': self.gsavi,
'Hue': self.hue,
'IVI': self.ivi,
'IPVI': self.ipvi,
'I': self.i,
'RVI': self.rvi,
'MRVI': self.mrvi,
'MSAVI': self.m_savi,
'NormG': self.norm_g,
'NormNIR': self.norm_nir,
'NormR': self.norm_r,
'NGRDI': self.ngrdi,
'RI': self.ri,
'S': self.s,
'IF': self._if,
'DVI': self.dvi,
'TVI': self.tvi,
'NDRE': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print("Index not in the list!" )
return False
def UpperCamelCase ( self ):
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def UpperCamelCase ( self ):
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def UpperCamelCase ( self ):
return self.nir * (self.red / (self.green**2))
def UpperCamelCase ( self ):
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def UpperCamelCase ( self ):
return (self.nir - self.red) / (self.nir + self.red)
def UpperCamelCase ( self ):
return (self.nir - self.blue) / (self.nir + self.blue)
def UpperCamelCase ( self ):
return (self.redEdge - self.red) / (self.redEdge + self.red)
def UpperCamelCase ( self ):
return (self.nir - self.green) / (self.nir + self.green)
def UpperCamelCase ( self ):
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def UpperCamelCase ( self ):
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def UpperCamelCase ( self ):
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def UpperCamelCase ( self ):
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def UpperCamelCase ( self , lowercase_=0.08 , lowercase_=1.22 , lowercase_=0.03 ):
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def UpperCamelCase ( self ):
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def UpperCamelCase ( self ):
return (self.nir / self.green) - 1
def UpperCamelCase ( self ):
return (self.nir / self.redEdge) - 1
def UpperCamelCase ( self ):
return (self.red - self.blue) / self.red
def UpperCamelCase ( self ):
_snake_case : List[Any] = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def UpperCamelCase ( self ):
return self.nir - self.green
def UpperCamelCase ( self ):
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def UpperCamelCase ( self ):
_snake_case : Union[str, Any] = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def UpperCamelCase ( self , lowercase_=0.16 ):
return (self.nir - self.green) / (self.nir + self.green + y)
def UpperCamelCase ( self , lowercase_=0.5 ):
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def UpperCamelCase ( self ):
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def UpperCamelCase ( self , lowercase_=None , lowercase_=None ):
return (self.nir - b) / (a * self.red)
def UpperCamelCase ( self ):
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def UpperCamelCase ( self ):
return (self.red + self.green + self.blue) / 30.5
def UpperCamelCase ( self ):
return self.nir / self.red
def UpperCamelCase ( self ):
return (self.rvi() - 1) / (self.rvi() + 1)
def UpperCamelCase ( self ):
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def UpperCamelCase ( self ):
return self.green / (self.nir + self.red + self.green)
def UpperCamelCase ( self ):
return self.nir / (self.nir + self.red + self.green)
def UpperCamelCase ( self ):
return self.red / (self.nir + self.red + self.green)
def UpperCamelCase ( self ):
return (self.green - self.red) / (self.green + self.red)
def UpperCamelCase ( self ):
return (self.red - self.green) / (self.red + self.green)
def UpperCamelCase ( self ):
_snake_case : Optional[Any] = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
_snake_case : Union[str, Any] = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def UpperCamelCase ( self ):
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def UpperCamelCase ( self ):
return self.nir / self.red
def UpperCamelCase ( self ):
return (self.ndvi() + 0.5) ** (1 / 2)
def UpperCamelCase ( self ):
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 358
|
def snake_case (__lowercase ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError("Input must be a positive integer" )
_snake_case : Any = [True] * (num + 1)
_snake_case : str = 2
while p * p <= num:
if primes[p]:
for i in range(p * p , num + 1 , __lowercase ):
_snake_case : Optional[int] = False
p += 1
return [prime for prime in range(2 , num + 1 ) if primes[prime]]
if __name__ == "__main__":
import doctest
doctest.testmod()
__SCREAMING_SNAKE_CASE : Any = int(input('Enter a positive integer: ').strip())
print(prime_sieve_eratosthenes(user_num))
| 284
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase ={
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""LongformerConfig""",
"""LongformerOnnxConfig""",
],
"""tokenization_longformer""": ["""LongformerTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =["""LongformerTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"""LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LongformerForMaskedLM""",
"""LongformerForMultipleChoice""",
"""LongformerForQuestionAnswering""",
"""LongformerForSequenceClassification""",
"""LongformerForTokenClassification""",
"""LongformerModel""",
"""LongformerPreTrainedModel""",
"""LongformerSelfAttention""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"""TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFLongformerForMaskedLM""",
"""TFLongformerForMultipleChoice""",
"""TFLongformerForQuestionAnswering""",
"""TFLongformerForSequenceClassification""",
"""TFLongformerForTokenClassification""",
"""TFLongformerModel""",
"""TFLongformerPreTrainedModel""",
"""TFLongformerSelfAttention""",
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 287
|
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
_lowerCamelCase =get_logger(__name__)
class A__ :
def __init__( self , __magic_name__ = None ):
lowerCamelCase : Dict = (
os.path.join(__magic_name__ , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
lowerCamelCase : List[str] = Extractor
def UpperCamelCase__ ( self , __magic_name__ ):
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
lowerCamelCase : int = os.path.abspath(__magic_name__ )
return os.path.join(self.extract_dir , hash_url_to_filename(__magic_name__ ) )
def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ ):
return force_extract or (
not os.path.isfile(__magic_name__ ) and not (os.path.isdir(__magic_name__ ) and os.listdir(__magic_name__ ))
)
def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ = False ):
lowerCamelCase : Union[str, Any] = self.extractor.infer_extractor_format(__magic_name__ )
if not extractor_format:
return input_path
lowerCamelCase : int = self._get_output_path(__magic_name__ )
if self._do_extract(__magic_name__ , __magic_name__ ):
self.extractor.extract(__magic_name__ , __magic_name__ , __magic_name__ )
return output_path
class A__ ( __SCREAMING_SNAKE_CASE):
@classmethod
@abstractmethod
def UpperCamelCase__ ( cls , __magic_name__ , **__magic_name__ ):
...
@staticmethod
@abstractmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
...
class A__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[bytes] = []
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
with open(__magic_name__ , """rb""" ) as f:
return f.read(__magic_name__ )
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ = b"" ):
if not magic_number:
lowerCamelCase : Optional[Any] = max(len(__magic_name__ ) for cls_magic_number in cls.magic_numbers )
try:
lowerCamelCase : Tuple = cls.read_magic_number(__magic_name__ , __magic_name__ )
except OSError:
return False
return any(magic_number.startswith(__magic_name__ ) for cls_magic_number in cls.magic_numbers )
class A__ ( __SCREAMING_SNAKE_CASE):
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , **__magic_name__ ):
return tarfile.is_tarfile(__magic_name__ )
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
def resolved(__magic_name__ ) -> str:
return os.path.realpath(os.path.abspath(__magic_name__ ) )
def badpath(__magic_name__ , __magic_name__ ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(__magic_name__ , __magic_name__ ) ).startswith(__magic_name__ )
def badlink(__magic_name__ , __magic_name__ ) -> bool:
# Links are interpreted relative to the directory containing the link
lowerCamelCase : List[str] = resolved(os.path.join(__magic_name__ , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=__magic_name__ )
lowerCamelCase : Optional[Any] = resolved(__magic_name__ )
for finfo in members:
if badpath(finfo.name , __magic_name__ ):
logger.error(F'''Extraction of {finfo.name} is blocked (illegal path)''' )
elif finfo.issym() and badlink(__magic_name__ , __magic_name__ ):
logger.error(F'''Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}''' )
elif finfo.islnk() and badlink(__magic_name__ , __magic_name__ ):
logger.error(F'''Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}''' )
else:
yield finfo
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
lowerCamelCase : Dict = tarfile.open(__magic_name__ )
tar_file.extractall(__magic_name__ , members=TarExtractor.safemembers(__magic_name__ , __magic_name__ ) )
tar_file.close()
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : str = [B"""\x1F\x8B"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
with gzip.open(__magic_name__ , """rb""" ) as gzip_file:
with open(__magic_name__ , """wb""" ) as extracted_file:
shutil.copyfileobj(__magic_name__ , __magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Optional[int] = [
B"""PK\x03\x04""",
B"""PK\x05\x06""", # empty archive
B"""PK\x07\x08""", # spanned archive
]
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ = b"" ):
if super().is_extractable(__magic_name__ , magic_number=__magic_name__ ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(__magic_name__ , """rb""" ) as fp:
lowerCamelCase : List[str] = _EndRecData(__magic_name__ )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
lowerCamelCase : List[Any] = fp.read(__magic_name__ ) # CD is where we expect it to be
if len(__magic_name__ ) == sizeCentralDir:
lowerCamelCase : str = struct.unpack(__magic_name__ , __magic_name__ ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
with zipfile.ZipFile(__magic_name__ , """r""" ) as zip_file:
zip_file.extractall(__magic_name__ )
zip_file.close()
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[str] = [B"""\xFD\x37\x7A\x58\x5A\x00"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
with lzma.open(__magic_name__ ) as compressed_file:
with open(__magic_name__ , """wb""" ) as extracted_file:
shutil.copyfileobj(__magic_name__ , __magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Any = [B"""Rar!\x1a\x07\x00""", B"""Rar!\x1a\x07\x01\x00"""] # RAR_ID # RAR5_ID
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
if not config.RARFILE_AVAILABLE:
raise ImportError("""Please pip install rarfile""" )
import rarfile
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
lowerCamelCase : Union[str, Any] = rarfile.RarFile(__magic_name__ )
rf.extractall(__magic_name__ )
rf.close()
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Tuple = [B"""\x28\xb5\x2F\xFD"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("""Please pip install zstandard""" )
import zstandard as zstd
lowerCamelCase : int = zstd.ZstdDecompressor()
with open(__magic_name__ , """rb""" ) as ifh, open(__magic_name__ , """wb""" ) as ofh:
dctx.copy_stream(__magic_name__ , __magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Any = [B"""\x42\x5A\x68"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
with bza.open(__magic_name__ , """rb""" ) as compressed_file:
with open(__magic_name__ , """wb""" ) as extracted_file:
shutil.copyfileobj(__magic_name__ , __magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[Any] = [B"""\x37\x7A\xBC\xAF\x27\x1C"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
if not config.PY7ZR_AVAILABLE:
raise ImportError("""Please pip install py7zr""" )
import pyazr
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
with pyazr.SevenZipFile(__magic_name__ , """r""" ) as archive:
archive.extractall(__magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[Any] = [B"""\x04\x22\x4D\x18"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
if not config.LZ4_AVAILABLE:
raise ImportError("""Please pip install lz4""" )
import lza.frame
with lza.frame.open(__magic_name__ , """rb""" ) as compressed_file:
with open(__magic_name__ , """wb""" ) as extracted_file:
shutil.copyfileobj(__magic_name__ , __magic_name__ )
class A__ :
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
_UpperCAmelCase : Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def UpperCamelCase__ ( cls ):
return max(
len(__magic_name__ )
for extractor in cls.extractors.values()
if issubclass(__magic_name__ , __magic_name__ )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
try:
return MagicNumberBaseExtractor.read_magic_number(__magic_name__ , magic_number_length=__magic_name__ )
except OSError:
return b""
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ = False ):
warnings.warn(
"""Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'infer_extractor_format' instead.""" , category=__magic_name__ , )
lowerCamelCase : int = cls.infer_extractor_format(__magic_name__ )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ ): # <Added version="2.4.0"/>
lowerCamelCase : Dict = cls._get_magic_number_max_length()
lowerCamelCase : Optional[Any] = cls._read_magic_number(__magic_name__ , __magic_name__ )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(__magic_name__ , magic_number=__magic_name__ ):
return extractor_format
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ , __magic_name__ = None , __magic_name__ = "deprecated" , ):
os.makedirs(os.path.dirname(__magic_name__ ) , exist_ok=__magic_name__ )
# Prevent parallel extractions
lowerCamelCase : Tuple = str(Path(__magic_name__ ).with_suffix(""".lock""" ) )
with FileLock(__magic_name__ ):
shutil.rmtree(__magic_name__ , ignore_errors=__magic_name__ )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(__magic_name__ , __magic_name__ ): # passed as positional arg
warnings.warn(
"""Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'extractor_format' instead.""" , category=__magic_name__ , )
lowerCamelCase : int = extractor if extractor != """deprecated""" else extractor_format
else:
lowerCamelCase : Optional[int] = cls.extractors[extractor_format]
return extractor.extract(__magic_name__ , __magic_name__ )
else:
warnings.warn(
"""Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an """
"""exception in 3.0.0.""" , category=__magic_name__ , )
for extractor in cls.extractors.values():
if extractor.is_extractable(__magic_name__ ):
return extractor.extract(__magic_name__ , __magic_name__ )
| 287
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A : str = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig''',
'''BlipTextConfig''',
'''BlipVisionConfig''',
],
'''processing_blip''': ['''BlipProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ['''BlipImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[Any] = [
'''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlipModel''',
'''BlipPreTrainedModel''',
'''BlipForConditionalGeneration''',
'''BlipForQuestionAnswering''',
'''BlipVisionModel''',
'''BlipTextModel''',
'''BlipForImageTextRetrieval''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = [
'''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBlipModel''',
'''TFBlipPreTrainedModel''',
'''TFBlipForConditionalGeneration''',
'''TFBlipForQuestionAnswering''',
'''TFBlipVisionModel''',
'''TFBlipTextModel''',
'''TFBlipForImageTextRetrieval''',
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
A : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 227
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A : Optional[int] = {
'''configuration_mask2former''': [
'''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Mask2FormerConfig''',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = ['''Mask2FormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
'''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Mask2FormerForUniversalSegmentation''',
'''Mask2FormerModel''',
'''Mask2FormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_maskaformer import MaskaFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskaformer import (
MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskaFormerForUniversalSegmentation,
MaskaFormerModel,
MaskaFormerPreTrainedModel,
)
else:
import sys
A : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 227
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Any = logging.get_logger(__name__)
lowerCAmelCase__ : Optional[Any] = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''studio-ousia/luke-large''': '''https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json''',
}
class __snake_case ( _lowerCamelCase ):
__lowerCamelCase = """luke"""
def __init__( self , __UpperCamelCase=50267 , __UpperCamelCase=500000 , __UpperCamelCase=768 , __UpperCamelCase=256 , __UpperCamelCase=12 , __UpperCamelCase=12 , __UpperCamelCase=3072 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=512 , __UpperCamelCase=2 , __UpperCamelCase=0.0_2 , __UpperCamelCase=1E-12 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=1 , __UpperCamelCase=0 , __UpperCamelCase=2 , **__UpperCamelCase , ) -> Tuple:
'''simple docstring'''
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
snake_case__ : Dict = vocab_size
snake_case__ : List[str] = entity_vocab_size
snake_case__ : List[Any] = hidden_size
snake_case__ : Dict = entity_emb_size
snake_case__ : Tuple = num_hidden_layers
snake_case__ : Any = num_attention_heads
snake_case__ : Union[str, Any] = hidden_act
snake_case__ : List[str] = intermediate_size
snake_case__ : Tuple = hidden_dropout_prob
snake_case__ : str = attention_probs_dropout_prob
snake_case__ : str = max_position_embeddings
snake_case__ : List[str] = type_vocab_size
snake_case__ : Optional[Any] = initializer_range
snake_case__ : Optional[int] = layer_norm_eps
snake_case__ : Optional[int] = use_entity_aware_attention
snake_case__ : Optional[Any] = classifier_dropout
| 143
|
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.distilbert.modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
class __snake_case :
def __init__( self , __UpperCamelCase , ) -> str:
'''simple docstring'''
snake_case__ : Optional[int] = parent
snake_case__ : Union[str, Any] = 13
snake_case__ : int = 7
snake_case__ : str = True
snake_case__ : Dict = True
snake_case__ : Tuple = False
snake_case__ : Union[str, Any] = True
snake_case__ : Dict = 99
snake_case__ : Tuple = 32
snake_case__ : Optional[int] = 2
snake_case__ : Dict = 4
snake_case__ : Dict = 37
snake_case__ : Any = 'gelu'
snake_case__ : Any = 0.1
snake_case__ : Any = 0.1
snake_case__ : List[Any] = 512
snake_case__ : Optional[Any] = 16
snake_case__ : Optional[int] = 2
snake_case__ : List[Any] = 0.0_2
snake_case__ : Tuple = 3
snake_case__ : Dict = 4
snake_case__ : Tuple = None
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case__ : Optional[Any] = None
if self.use_input_mask:
snake_case__ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case__ : Any = None
snake_case__ : Union[str, Any] = None
snake_case__ : Tuple = None
if self.use_labels:
snake_case__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case__ : List[str] = ids_tensor([self.batch_size] , self.num_choices )
snake_case__ : List[str] = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __a ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Dict:
'''simple docstring'''
snake_case__ : Optional[int] = TFDistilBertModel(config=__UpperCamelCase )
snake_case__ : Union[str, Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
snake_case__ : List[str] = model(__UpperCamelCase )
snake_case__ : Union[str, Any] = [input_ids, input_mask]
snake_case__ : Optional[int] = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> List[Any]:
'''simple docstring'''
snake_case__ : str = TFDistilBertForMaskedLM(config=__UpperCamelCase )
snake_case__ : int = {'input_ids': input_ids, 'attention_mask': input_mask}
snake_case__ : str = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __a ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> List[Any]:
'''simple docstring'''
snake_case__ : Dict = TFDistilBertForQuestionAnswering(config=__UpperCamelCase )
snake_case__ : Tuple = {
'input_ids': input_ids,
'attention_mask': input_mask,
}
snake_case__ : Optional[Any] = model(__UpperCamelCase )
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 __a ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ : List[str] = self.num_labels
snake_case__ : Tuple = TFDistilBertForSequenceClassification(__UpperCamelCase )
snake_case__ : Union[str, Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
snake_case__ : str = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __a ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
'''simple docstring'''
snake_case__ : Optional[int] = self.num_choices
snake_case__ : str = TFDistilBertForMultipleChoice(__UpperCamelCase )
snake_case__ : Dict = tf.tile(tf.expand_dims(__UpperCamelCase , 1 ) , (1, self.num_choices, 1) )
snake_case__ : Tuple = tf.tile(tf.expand_dims(__UpperCamelCase , 1 ) , (1, self.num_choices, 1) )
snake_case__ : Dict = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
}
snake_case__ : Any = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __a ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ : Optional[int] = self.num_labels
snake_case__ : Optional[int] = TFDistilBertForTokenClassification(__UpperCamelCase )
snake_case__ : List[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
snake_case__ : str = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ : Optional[Any] = self.prepare_config_and_inputs()
((snake_case__) , (snake_case__) , (snake_case__) , (snake_case__) , (snake_case__) , (snake_case__)) : Tuple = config_and_inputs
snake_case__ : List[str] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( _lowerCamelCase ,_lowerCamelCase ,unittest.TestCase ):
__lowerCamelCase = (
(
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForMultipleChoice,
)
if is_tf_available()
else None
)
__lowerCamelCase = (
{
"""feature-extraction""": TFDistilBertModel,
"""fill-mask""": TFDistilBertForMaskedLM,
"""question-answering""": TFDistilBertForQuestionAnswering,
"""text-classification""": TFDistilBertForSequenceClassification,
"""token-classification""": TFDistilBertForTokenClassification,
"""zero-shot""": TFDistilBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
def __a ( self ) -> Tuple:
'''simple docstring'''
snake_case__ : Dict = TFDistilBertModelTester(self )
snake_case__ : Tuple = ConfigTester(self , config_class=__UpperCamelCase , dim=37 )
def __a ( self ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def __a ( self ) -> Tuple:
'''simple docstring'''
snake_case__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*__UpperCamelCase )
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*__UpperCamelCase )
def __a ( self ) -> List[str]:
'''simple docstring'''
snake_case__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*__UpperCamelCase )
def __a ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*__UpperCamelCase )
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*__UpperCamelCase )
def __a ( self ) -> Optional[int]:
'''simple docstring'''
snake_case__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*__UpperCamelCase )
@slow
def __a ( self ) -> Any:
'''simple docstring'''
for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1] ):
snake_case__ : Optional[Any] = TFDistilBertModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
@require_tf
class __snake_case ( unittest.TestCase ):
@slow
def __a ( self ) -> Any:
'''simple docstring'''
snake_case__ : Optional[int] = TFDistilBertModel.from_pretrained('distilbert-base-uncased' )
snake_case__ : Any = tf.constant([[0, 1, 2, 3, 4, 5]] )
snake_case__ : Dict = model(__UpperCamelCase )[0]
snake_case__ : Optional[int] = [1, 6, 768]
self.assertEqual(output.shape , __UpperCamelCase )
snake_case__ : List[Any] = tf.constant(
[
[
[0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
[0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
[0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 )
| 143
| 1
|
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A__ :
A__ = 42
A__ = None
A__ = None
def _lowerCAmelCase ( ) -> Node | None:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =Node(1 )
_SCREAMING_SNAKE_CASE =Node(2 )
_SCREAMING_SNAKE_CASE =Node(3 )
_SCREAMING_SNAKE_CASE =Node(4 )
_SCREAMING_SNAKE_CASE =Node(5 )
return tree
def _lowerCAmelCase ( _UpperCamelCase : Node | None ) -> list[int]:
"""simple docstring"""
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def _lowerCAmelCase ( _UpperCamelCase : Node | None ) -> list[int]:
"""simple docstring"""
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def _lowerCAmelCase ( _UpperCamelCase : Node | None ) -> list[int]:
"""simple docstring"""
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def _lowerCAmelCase ( _UpperCamelCase : Node | None ) -> int:
"""simple docstring"""
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def _lowerCAmelCase ( _UpperCamelCase : Node | None ) -> Sequence[Node | None]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[]
if root is None:
return output
_SCREAMING_SNAKE_CASE =deque([root] )
while process_queue:
_SCREAMING_SNAKE_CASE =process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def _lowerCAmelCase ( _UpperCamelCase : Node | None , _UpperCamelCase : int ) -> Sequence[Node | None]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[]
def populate_output(_UpperCamelCase : Node | None , _UpperCamelCase : int ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(_UpperCamelCase , _UpperCamelCase )
return output
def _lowerCAmelCase ( _UpperCamelCase : Node | None , _UpperCamelCase : int ) -> Sequence[Node | None]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[]
def populate_output(_UpperCamelCase : Node | None , _UpperCamelCase : int ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(_UpperCamelCase , _UpperCamelCase )
return output
def _lowerCAmelCase ( _UpperCamelCase : Node | None ) -> Sequence[Node | None] | list[Any]:
"""simple docstring"""
if root is None:
return []
_SCREAMING_SNAKE_CASE =[]
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =height(_UpperCamelCase )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(_UpperCamelCase , _UpperCamelCase ) )
_SCREAMING_SNAKE_CASE =1
else:
output.append(get_nodes_from_right_to_left(_UpperCamelCase , _UpperCamelCase ) )
_SCREAMING_SNAKE_CASE =0
return output
def _lowerCAmelCase ( ) -> None: # Main function for testing.
"""simple docstring"""
_SCREAMING_SNAKE_CASE =make_tree()
print(f"In-order Traversal: {inorder(_UpperCamelCase )}" )
print(f"Pre-order Traversal: {preorder(_UpperCamelCase )}" )
print(f"Post-order Traversal: {postorder(_UpperCamelCase )}" , '\n' )
print(f"Height of Tree: {height(_UpperCamelCase )}" , '\n' )
print('Complete Level Order Traversal: ' )
print(level_order(_UpperCamelCase ) , '\n' )
print('Level-wise order Traversal: ' )
for level in range(1 , height(_UpperCamelCase ) + 1 ):
print(f"Level {level}:" , get_nodes_from_left_to_right(_UpperCamelCase , level=_UpperCamelCase ) )
print('\nZigZag order Traversal: ' )
print(zigzag(_UpperCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 114
|
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : int = 50 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 114
| 1
|
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class UpperCAmelCase ( snake_case__ ):
'''simple docstring'''
__UpperCamelCase : Tuple = 42
__UpperCamelCase : Optional[Any] = None
def lowerCamelCase__ ( a , a=0.999 , a="cosine" , ) -> Union[str, Any]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(a ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(a ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
_A: List[str] = []
for i in range(__lowerCAmelCase ):
_A: Union[str, Any] = i / num_diffusion_timesteps
_A: str = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__lowerCAmelCase ) / alpha_bar_fn(__lowerCAmelCase ) , __lowerCAmelCase ) )
return torch.tensor(__lowerCAmelCase , dtype=torch.floataa )
class UpperCAmelCase ( snake_case__ , snake_case__ ):
'''simple docstring'''
__UpperCamelCase : Dict = 1
@register_to_config
def __init__( self : List[Any] , lowerCAmelCase_ : Optional[int] = 1_0_0_0 , lowerCAmelCase_ : List[Any] = 0.0001 , lowerCAmelCase_ : Union[str, Any] = 0.02 , lowerCAmelCase_ : Optional[int] = "linear" , lowerCAmelCase_ : List[str] = None , lowerCAmelCase_ : Any = True , lowerCAmelCase_ : Union[str, Any] = True , lowerCAmelCase_ : str = 0 , lowerCAmelCase_ : List[str] = "epsilon" , lowerCAmelCase_ : Any = 1.0 , **lowerCAmelCase_ : List[Any] , ):
"""simple docstring"""
if kwargs.get('''set_alpha_to_one''' , lowerCAmelCase_ ) is not None:
_A: Tuple = (
'''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
)
deprecate('''set_alpha_to_one''' , '''1.0.0''' , lowerCAmelCase_ , standard_warn=lowerCAmelCase_ )
_A: Dict = kwargs['''set_alpha_to_one''']
if trained_betas is not None:
_A: Dict = torch.tensor(lowerCAmelCase_ , dtype=torch.floataa )
elif beta_schedule == "linear":
_A: Any = torch.linspace(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_A: Optional[Any] = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , lowerCAmelCase_ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_A: Any = betas_for_alpha_bar(lowerCAmelCase_ )
else:
raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""" )
_A: Any = 1.0 - self.betas
_A: Union[str, Any] = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
_A: Dict = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
_A: List[str] = 1.0
# setable values
_A: int = None
_A: Any = torch.from_numpy(np.arange(0 , lowerCAmelCase_ ).copy().astype(np.intaa ) )
def __magic_name__ ( self : List[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple = None ):
"""simple docstring"""
return sample
def __magic_name__ ( self : Any , lowerCAmelCase_ : Any , lowerCAmelCase_ : Union[str, Any] = None ):
"""simple docstring"""
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
F""" maximal {self.config.num_train_timesteps} timesteps.""" )
_A: Any = num_inference_steps
_A: Any = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_A: Union[str, Any] = (np.arange(0 , lowerCAmelCase_ ) * step_ratio).round().copy().astype(np.intaa )
_A: int = torch.from_numpy(lowerCAmelCase_ ).to(lowerCAmelCase_ )
self.timesteps += self.config.steps_offset
def __magic_name__ ( self : Optional[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[Any] = 0.0 , lowerCAmelCase_ : List[str] = False , lowerCAmelCase_ : Union[str, Any] = None , lowerCAmelCase_ : Dict = True , ):
"""simple docstring"""
_A: List[Any] = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
_A: Union[str, Any] = self.alphas_cumprod[timestep]
_A: Tuple = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
_A: List[str] = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
_A: Any = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
_A: Tuple = model_output
elif self.config.prediction_type == "sample":
_A: Any = model_output
_A: Dict = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
_A: int = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
_A: Optional[Any] = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
''' `v_prediction`''' )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
_A: Tuple = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_A: int = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_A: Union[str, Any] = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=lowerCAmelCase_ , pred_original_sample=lowerCAmelCase_ )
def __len__( self : Optional[int] ):
"""simple docstring"""
return self.config.num_train_timesteps
| 121
|
from __future__ import annotations
def __A ( __lowerCAmelCase )-> list[int]:
"""simple docstring"""
_UpperCAmelCase = 2
_UpperCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__lowerCAmelCase )
if n > 1:
factors.append(__lowerCAmelCase )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 39
| 0
|
import cmath
import math
def a__ ( A__, A__, A__, A__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = math.radians(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE_ : Optional[int] = math.radians(SCREAMING_SNAKE_CASE__ )
# Convert voltage and current to rectangular form
SCREAMING_SNAKE_CASE_ : Optional[int] = cmath.rect(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE_ : Dict = cmath.rect(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367
|
# Lint as: python3
import itertools
import os
import re
lowerCAmelCase__ : Optional[int] =re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCAmelCase__ : List[Any] =re.compile(R'([a-z\d])([A-Z])')
lowerCAmelCase__ : Dict =re.compile(R'(?<!_)_(?!_)')
lowerCAmelCase__ : int =re.compile(R'(_{2,})')
lowerCAmelCase__ : Optional[Any] =R'^\w+(\.\w+)*$'
lowerCAmelCase__ : List[Any] =R'<>:/\|?*'
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : Dict = _uppercase_uppercase_re.sub(r'\1_\2', A__ )
SCREAMING_SNAKE_CASE_ : List[str] = _lowercase_uppercase_re.sub(r'\1_\2', A__ )
return name.lower()
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = _single_underscore_re.split(A__ )
SCREAMING_SNAKE_CASE_ : str = [_multiple_underscores_re.split(A__ ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(A__ ) if n != '' )
def a__ ( A__ ):
if os.path.basename(A__ ) != name:
raise ValueError(F'''Should be a dataset name, not a path: {name}''' )
return camelcase_to_snakecase(A__ )
def a__ ( A__, A__ ):
if os.path.basename(A__ ) != name:
raise ValueError(F'''Should be a dataset name, not a path: {name}''' )
if not re.match(_split_re, A__ ):
raise ValueError(F'''Split name should match \'{_split_re}\'\' but got \'{split}\'.''' )
return F'''{filename_prefix_for_name(A__ )}-{split}'''
def a__ ( A__, A__, A__, A__=None ):
SCREAMING_SNAKE_CASE_ : Tuple = filename_prefix_for_split(A__, A__ )
if filetype_suffix:
prefix += F'''.{filetype_suffix}'''
SCREAMING_SNAKE_CASE_ : List[Any] = os.path.join(A__, A__ )
return F'''{filepath}*'''
def a__ ( A__, A__, A__, A__=None, A__=None ):
SCREAMING_SNAKE_CASE_ : Tuple = filename_prefix_for_split(A__, A__ )
SCREAMING_SNAKE_CASE_ : Dict = os.path.join(A__, A__ )
if shard_lengths:
SCREAMING_SNAKE_CASE_ : Dict = len(A__ )
SCREAMING_SNAKE_CASE_ : Any = [F'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(A__ )]
if filetype_suffix:
SCREAMING_SNAKE_CASE_ : Optional[int] = [filename + F'''.{filetype_suffix}''' for filename in filenames]
return filenames
else:
SCREAMING_SNAKE_CASE_ : Optional[Any] = prefix
if filetype_suffix:
filename += F'''.{filetype_suffix}'''
return [filename]
| 162
| 0
|
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ = 1000 ) -> int:
_a , _a : Union[str, Any] = 1, 1
_a : Dict = 2
while True:
_a : Any = 0
_a : Optional[Any] = fa + fa
_a , _a : Union[str, Any] = fa, f
index += 1
for _ in str(lowerCAmelCase_ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 89
|
# 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
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case : Dict = {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : Optional[int] = [
'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MraForMaskedLM',
'MraForMultipleChoice',
'MraForQuestionAnswering',
'MraForSequenceClassification',
'MraForTokenClassification',
'MraLayer',
'MraModel',
'MraPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
_snake_case : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 284
| 0
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
__A = logging.get_logger(__name__)
class lowerCamelCase__ ( __magic_name__ ):
'''simple docstring'''
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> None:
warnings.warn(
"""The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use YolosImageProcessor instead.""" , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
| 341
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Swinv2ForImageClassification',
'Swinv2ForMaskedImageModeling',
'Swinv2Model',
'Swinv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 341
| 1
|
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class _lowercase :
"""simple docstring"""
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return None
class _lowercase :
"""simple docstring"""
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return None
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
__A = [
# (model_name, model_kwargs)
("bert-base-cased", {}),
("gpt2", {"use_cache": False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def UpperCamelCase_ (self ):
"""simple docstring"""
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase_ , "tf" , 12 , **lowerCamelCase_ )
@require_torch
@slow
def UpperCamelCase_ (self ):
"""simple docstring"""
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase_ , "pt" , 12 , **lowerCamelCase_ )
@require_torch
@slow
def UpperCamelCase_ (self ):
"""simple docstring"""
from transformers import BertModel
a = ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
with NamedTemporaryFile(mode="w+t" ) as vocab_file:
vocab_file.write("\n".join(lowerCamelCase_ ) )
vocab_file.flush()
a = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
a = BertModel(BertConfig(vocab_size=len(lowerCamelCase_ ) ) )
model.save_pretrained(lowerCamelCase_ )
self._test_export(lowerCamelCase_ , "pt" , 12 , lowerCamelCase_ )
@require_tf
@slow
def UpperCamelCase_ (self ):
"""simple docstring"""
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
a = self._test_export(lowerCamelCase_ , "tf" , 12 , **lowerCamelCase_ )
a = quantize(Path(lowerCamelCase_ ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase_ ).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model" )
@require_torch
@slow
def UpperCamelCase_ (self ):
"""simple docstring"""
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
a = self._test_export(lowerCamelCase_ , "pt" , 12 , **lowerCamelCase_ )
a = quantize(lowerCamelCase_ )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase_ ).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model" )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=None , **lowerCamelCase_ ):
"""simple docstring"""
try:
# Compute path
with TemporaryDirectory() as tempdir:
a = Path(lowerCamelCase_ ).joinpath("model.onnx" )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
return path
except Exception as e:
self.fail(lowerCamelCase_ )
@require_torch
@require_tokenizers
@slow
def UpperCamelCase_ (self ):
"""simple docstring"""
from transformers import BertModel
a = BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
a = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
self._test_infer_dynamic_axis(lowerCamelCase_ , lowerCamelCase_ , "pt" )
@require_tf
@require_tokenizers
@slow
def UpperCamelCase_ (self ):
"""simple docstring"""
from transformers import TFBertModel
a = TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
a = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
self._test_infer_dynamic_axis(lowerCamelCase_ , lowerCamelCase_ , "tf" )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
a = FeatureExtractionPipeline(lowerCamelCase_ , lowerCamelCase_ )
a = ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
a , a , a , a = infer_shapes(lowerCamelCase_ , lowerCamelCase_ )
# Assert all variables are present
self.assertEqual(len(lowerCamelCase_ ) , len(lowerCamelCase_ ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , lowerCamelCase_ )
self.assertSequenceEqual(variable_names[3:] , lowerCamelCase_ )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] , {0: "batch", 1: "sequence"} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes["output_0"] , {0: "batch", 1: "sequence"} )
self.assertDictEqual(shapes["output_1"] , {0: "batch"} )
def UpperCamelCase_ (self ):
"""simple docstring"""
a = ["input_ids", "attention_mask", "token_type_ids"]
a = {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
a , a = ensure_valid_input(FuncContiguousArgs() , lowerCamelCase_ , lowerCamelCase_ )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(lowerCamelCase_ ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(lowerCamelCase_ ) , set(lowerCamelCase_ ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(lowerCamelCase_ , (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"]) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
a , a = ensure_valid_input(FuncNonContiguousArgs() , lowerCamelCase_ , lowerCamelCase_ )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(lowerCamelCase_ ) , 1 )
self.assertEqual(len(lowerCamelCase_ ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens["input_ids"] )
self.assertEqual(ordered_input_names[0] , "input_ids" )
def UpperCamelCase_ (self ):
"""simple docstring"""
a = generate_identified_filename(Path("/home/something/my_fake_model.onnx" ) , "-test" )
self.assertEqual("/home/something/my_fake_model-test.onnx" , generated.as_posix() )
| 227
|
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
_lowercase: Tuple = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def a( A : Optional[Any] ) -> str:
"""simple docstring"""
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def a( A : Dict , A : List[Any] , A : str ) -> List[str]:
"""simple docstring"""
return max(metric_fn(A , A ) for gt in ground_truths )
def a( A : str , A : Optional[Any] , A : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = [line.strip() for line in open(A , "r" ).readlines()]
a = []
if args.gold_data_mode == "qa":
a = pd.read_csv(A , sep="\t" , header=A )
for answer_list in data[1]:
a = ast.literal_eval(A )
answers.append(A )
else:
a = [line.strip() for line in open(A , "r" ).readlines()]
a = [[reference] for reference in references]
a = a = a = 0
for prediction, ground_truths in zip(A , A ):
total += 1
em += metric_max_over_ground_truths(A , A , A )
fa += metric_max_over_ground_truths(A , A , A )
a = 100.0 * em / total
a = 100.0 * fa / total
logger.info(f'''F1: {fa:.2f}''' )
logger.info(f'''EM: {em:.2f}''' )
def a( A : Dict , A : str , A : List[str] ) -> List[Any]:
"""simple docstring"""
a = args.k
a = [line.strip() for line in open(A , "r" ).readlines()]
a = [line.strip() for line in open(A , "r" ).readlines()]
a = a = 0
for hypo, reference in zip(A , A ):
a = set(hypo.split("\t" )[:k] )
a = set(reference.split("\t" ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
a = 100.0 * em / total
logger.info(f'''Precision@{k}: {em: .2f}''' )
def a( A : Dict , A : Any , A : List[Any] ) -> Any:
"""simple docstring"""
def strip_title(A : Any ):
if title.startswith("\"" ):
a = title[1:]
if title.endswith("\"" ):
a = title[:-1]
return title
a = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
A , return_tensors="pt" , padding=A , truncation=A , )["input_ids"].to(args.device )
a = rag_model.rag.question_encoder(A )
a = question_enc_outputs[0]
a = rag_model.retriever(
A , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors="pt" , )
a = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
a = []
for docs in all_docs:
a = [strip_title(A ) for title in docs["title"]]
provenance_strings.append("\t".join(A ) )
return provenance_strings
def a( A : Union[str, Any] , A : Optional[int] , A : Tuple ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
a = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
A , return_tensors="pt" , padding=A , truncation=A )
a = inputs_dict.input_ids.to(args.device )
a = inputs_dict.attention_mask.to(args.device )
a = rag_model.generate( # rag_model overwrites generate
A , attention_mask=A , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=A , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
a = rag_model.retriever.generator_tokenizer.batch_decode(A , skip_special_tokens=A )
if args.print_predictions:
for q, a in zip(A , A ):
logger.info("Q: {} - A: {}".format(A , A ) )
return answers
def a( ) -> Any:
"""simple docstring"""
a = argparse.ArgumentParser()
parser.add_argument(
"--model_type" , choices=["rag_sequence", "rag_token", "bart"] , type=A , help=(
"RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
" model_name_or_path"
) , )
parser.add_argument(
"--index_name" , default=A , choices=["exact", "compressed", "legacy"] , type=A , help="RAG model retriever type" , )
parser.add_argument(
"--index_path" , default=A , type=A , help="Path to the retrieval index" , )
parser.add_argument("--n_docs" , default=5 , type=A , help="Number of retrieved docs" )
parser.add_argument(
"--model_name_or_path" , default=A , type=A , required=A , help="Path to pretrained checkpoints or model identifier from huggingface.co/models" , )
parser.add_argument(
"--eval_mode" , choices=["e2e", "retrieval"] , default="e2e" , type=A , help=(
"Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
" precision@k."
) , )
parser.add_argument("--k" , default=1 , type=A , help="k for the precision@k calculation" )
parser.add_argument(
"--evaluation_set" , default=A , type=A , required=A , help="Path to a file containing evaluation samples" , )
parser.add_argument(
"--gold_data_path" , default=A , type=A , required=A , help="Path to a tab-separated file with gold samples" , )
parser.add_argument(
"--gold_data_mode" , default="qa" , type=A , choices=["qa", "ans"] , help=(
"Format of the gold data file"
"qa - a single line in the following format: question [tab] answer_list"
"ans - a single line of the gold file contains the expected answer string"
) , )
parser.add_argument(
"--predictions_path" , type=A , default="predictions.txt" , help="Name of the predictions file, to be stored in the checkpoints directory" , )
parser.add_argument(
"--eval_all_checkpoints" , action="store_true" , help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number" , )
parser.add_argument(
"--eval_batch_size" , default=8 , type=A , help="Batch size per GPU/CPU for evaluation." , )
parser.add_argument(
"--recalculate" , help="Recalculate predictions even if the prediction file exists" , action="store_true" , )
parser.add_argument(
"--num_beams" , default=4 , type=A , help="Number of beams to be used when generating answers" , )
parser.add_argument("--min_length" , default=1 , type=A , help="Min length of the generated answers" )
parser.add_argument("--max_length" , default=50 , type=A , help="Max length of the generated answers" )
parser.add_argument(
"--print_predictions" , action="store_true" , help="If True, prints predictions while evaluating." , )
parser.add_argument(
"--print_docs" , action="store_true" , help="If True, prints docs retried while generating." , )
a = parser.parse_args()
a = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
return args
def a( A : Any ) -> Optional[Any]:
"""simple docstring"""
a = {}
if args.model_type is None:
a = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith("rag" ):
a = RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
a = args.n_docs
if args.index_name is not None:
a = args.index_name
if args.index_path is not None:
a = args.index_path
else:
a = BartForConditionalGeneration
a = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info("Evaluate the following checkpoints: %s" , A )
a = get_scores if args.eval_mode == "e2e" else get_precision_at_k
a = evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path ) )
score_fn(A , args.predictions_path , args.gold_data_path )
continue
logger.info("***** Running evaluation for {} *****".format(A ) )
logger.info(" Batch size = %d" , args.eval_batch_size )
logger.info(" Predictions will be stored under {}".format(args.predictions_path ) )
if args.model_type.startswith("rag" ):
a = RagRetriever.from_pretrained(A , **A )
a = model_class.from_pretrained(A , retriever=A , **A )
model.retriever.init_retrieval()
else:
a = model_class.from_pretrained(A , **A )
model.to(args.device )
with open(args.evaluation_set , "r" ) as eval_file, open(args.predictions_path , "w" ) as preds_file:
a = []
for line in tqdm(A ):
questions.append(line.strip() )
if len(A ) == args.eval_batch_size:
a = evaluate_batch_fn(A , A , A )
preds_file.write("\n".join(A ) + "\n" )
preds_file.flush()
a = []
if len(A ) > 0:
a = evaluate_batch_fn(A , A , A )
preds_file.write("\n".join(A ) )
preds_file.flush()
score_fn(A , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
_lowercase: Optional[int] = get_args()
main(args)
| 227
| 1
|
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
UpperCAmelCase_ : Tuple = get_tests_dir('fixtures')
UpperCAmelCase_ : Union[str, Any] = get_tests_dir('fixtures/dummy_feature_extractor_config.json')
UpperCAmelCase_ : Tuple = get_tests_dir('fixtures/dummy-config.json')
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> int:
a_ : Any = 0
def SCREAMING_SNAKE_CASE ( self : int ) -> Dict:
a_ : int = AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h' )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : str ) -> Optional[Any]:
a_ : Tuple = AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
with tempfile.TemporaryDirectory() as tmpdirname:
a_ : List[str] = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
a_ : Dict = AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ ).to_dict()
config_dict.pop('feature_extractor_type' )
a_ : Any = WavaVecaFeatureExtractor(**SCREAMING_SNAKE_CASE__ )
# save in new folder
model_config.save_pretrained(SCREAMING_SNAKE_CASE__ )
config.save_pretrained(SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ )
# make sure private variable is not incorrectly saved
a_ : Tuple = json.loads(config.to_json_string() )
self.assertTrue('_processor_class' not in dict_as_saved )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Union[str, Any]:
a_ : Optional[Any] = AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[int]:
with self.assertRaisesRegex(
SCREAMING_SNAKE_CASE__ , 'bert-base is not a local folder and is not a valid model identifier' ):
a_ : List[Any] = AutoFeatureExtractor.from_pretrained('bert-base' )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
with self.assertRaisesRegex(
SCREAMING_SNAKE_CASE__ , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
a_ : List[str] = AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ , revision='aaaaaa' )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Dict:
with self.assertRaisesRegex(
SCREAMING_SNAKE_CASE__ , 'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.' , ):
a_ : List[Any] = AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model' )
def SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
a_ : List[Any] = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
a_ : Optional[Any] = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=SCREAMING_SNAKE_CASE__ )
a_ : List[str] = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=SCREAMING_SNAKE_CASE__ )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ , trust_remote_code=SCREAMING_SNAKE_CASE__ )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Dict:
try:
AutoConfig.register('custom' , SCREAMING_SNAKE_CASE__ )
AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Now that the config is registered, it can be used as any other config with the auto-API
a_ : int = CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ )
a_ : Any = AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE ( self : Any ) -> List[str]:
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : List[str] = True
try:
AutoConfig.register('custom' , SCREAMING_SNAKE_CASE__ )
AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# If remote code is not set, the default is to use local
a_ : Union[str, Any] = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
a_ : Dict = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=SCREAMING_SNAKE_CASE__ )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
a_ : Optional[int] = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=SCREAMING_SNAKE_CASE__ )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
self.assertTrue(not hasattr(SCREAMING_SNAKE_CASE__ , 'is_local' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
| 120
|
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
UpperCAmelCase_ : str = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase_ : Optional[int] = logging.getLogger()
def SCREAMING_SNAKE_CASE_ ( ) -> Any:
"""simple docstring"""
a_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument('-f' )
a_ : Optional[Any] = parser.parse_args()
return args.f
def SCREAMING_SNAKE_CASE_ ( __A : Optional[int] , __A : List[Any]="eval" ) -> Optional[int]:
"""simple docstring"""
a_ : List[Any] = os.path.join(__A , F"""{split}_results.json""" )
if os.path.exists(__A ):
with open(__A , 'r' ) as f:
return json.load(__A )
raise ValueError(F"""can't find {path}""" )
UpperCAmelCase_ : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def SCREAMING_SNAKE_CASE ( self : Any ) -> Tuple:
a_ : Optional[Any] = self.get_auto_remove_tmp_dir()
a_ : List[str] = F"""
run_glue.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
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(SCREAMING_SNAKE_CASE__ , 'argv' , SCREAMING_SNAKE_CASE__ ):
run_flax_glue.main()
a_ : str = get_results(SCREAMING_SNAKE_CASE__ )
self.assertGreaterEqual(result['eval_accuracy'] , 0.75 )
@slow
def SCREAMING_SNAKE_CASE ( self : Any ) -> str:
a_ : List[str] = self.get_auto_remove_tmp_dir()
a_ : Union[str, Any] = F"""
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(SCREAMING_SNAKE_CASE__ , 'argv' , SCREAMING_SNAKE_CASE__ ):
run_clm_flax.main()
a_ : List[Any] = get_results(SCREAMING_SNAKE_CASE__ )
self.assertLess(result['eval_perplexity'] , 1_0_0 )
@slow
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> int:
a_ : Tuple = self.get_auto_remove_tmp_dir()
a_ : Dict = F"""
run_summarization.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
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(SCREAMING_SNAKE_CASE__ , 'argv' , SCREAMING_SNAKE_CASE__ ):
run_summarization_flax.main()
a_ : List[str] = get_results(SCREAMING_SNAKE_CASE__ , split='test' )
self.assertGreaterEqual(result['test_rouge1'] , 1_0 )
self.assertGreaterEqual(result['test_rouge2'] , 2 )
self.assertGreaterEqual(result['test_rougeL'] , 7 )
self.assertGreaterEqual(result['test_rougeLsum'] , 7 )
@slow
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
a_ : int = self.get_auto_remove_tmp_dir()
a_ : Dict = F"""
run_mlm.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}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
""".split()
with patch.object(SCREAMING_SNAKE_CASE__ , 'argv' , SCREAMING_SNAKE_CASE__ ):
run_mlm_flax.main()
a_ : List[Any] = get_results(SCREAMING_SNAKE_CASE__ )
self.assertLess(result['eval_perplexity'] , 4_2 )
@slow
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[str]:
a_ : str = self.get_auto_remove_tmp_dir()
a_ : List[str] = F"""
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(SCREAMING_SNAKE_CASE__ , 'argv' , SCREAMING_SNAKE_CASE__ ):
run_ta_mlm_flax.main()
a_ : Dict = get_results(SCREAMING_SNAKE_CASE__ )
self.assertGreaterEqual(result['eval_accuracy'] , 0.42 )
@slow
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> List[str]:
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
a_ : int = 7 if get_gpu_count() > 1 else 2
a_ : Dict = self.get_auto_remove_tmp_dir()
a_ : Tuple = F"""
run_flax_ner.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}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
""".split()
with patch.object(SCREAMING_SNAKE_CASE__ , 'argv' , SCREAMING_SNAKE_CASE__ ):
run_flax_ner.main()
a_ : List[str] = get_results(SCREAMING_SNAKE_CASE__ )
self.assertGreaterEqual(result['eval_accuracy'] , 0.75 )
self.assertGreaterEqual(result['eval_f1'] , 0.3 )
@slow
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> int:
a_ : List[str] = self.get_auto_remove_tmp_dir()
a_ : int = F"""
run_qa.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}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
""".split()
with patch.object(SCREAMING_SNAKE_CASE__ , 'argv' , SCREAMING_SNAKE_CASE__ ):
run_qa.main()
a_ : str = get_results(SCREAMING_SNAKE_CASE__ )
self.assertGreaterEqual(result['eval_f1'] , 3_0 )
self.assertGreaterEqual(result['eval_exact'] , 3_0 )
| 120
| 1
|
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : dict , __lowerCamelCase : str ):
__UpperCAmelCase , __UpperCAmelCase : str = set(__lowerCamelCase ), [start]
while stack:
__UpperCAmelCase : Tuple = stack.pop()
explored.add(__lowerCamelCase )
# 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(__lowerCamelCase )
return explored
a : Union[str, Any] = {
"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"))
| 114
|
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : Union[str, Any] ):
__UpperCAmelCase : Tuple = [1]
for i in range(2 , __lowerCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
__UpperCAmelCase : Optional[Any] = []
__UpperCAmelCase : str = list(range(__lowerCamelCase ) )
# Find permutation
while factorials:
__UpperCAmelCase : Any = factorials.pop()
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = divmod(__lowerCamelCase , __lowerCamelCase )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 114
| 1
|
import os
from typing import Dict, List, Tuple, TypeVar, Union
__A : List[Any] = TypeVar('T')
__A : Dict = Union[List[T], Tuple[T, ...]]
__A : str = Union[T, List[T], Dict[str, T]]
__A : Optional[Any] = Union[str, bytes, os.PathLike]
| 49
|
from math import pi, sqrt
def __UpperCamelCase ( _A : float ) ->float:
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(_A ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(_A )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def __UpperCamelCase ( ) ->None:
"""simple docstring"""
assert gamma(0.5 ) == sqrt(_A )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
__A : List[Any] = 1.0
while num:
__A : str = float(input('Gamma of: '))
print(F"""gamma({num}) = {gamma(num)}""")
print('\nEnter 0 to exit...')
| 49
| 1
|
'''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( __A , __A , __A , __A ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ = FunnelConfig.from_json_file(__A )
print(F'''Building PyTorch model from configuration: {config}''' )
UpperCamelCase__ = FunnelBaseModel(__A ) if base_model else FunnelModel(__A )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(__A , __A , __A )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , __A )
if __name__ == "__main__":
a__ : str = 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(
'--config_file',
default=None,
type=str,
required=True,
help='The config json file corresponding to the pre-trained 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(
'--base_model', action='store_true', help='Whether you want just the base model (no decoder) or not.'
)
a__ : str = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 80
|
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
return str(UpperCAmelCase__ ) == str(UpperCAmelCase__ )[::-1]
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
return int(UpperCAmelCase__ ) + int(str(UpperCAmelCase__ )[::-1] )
def UpperCAmelCase__ ( UpperCAmelCase__ = 1_00_00 ) -> int:
A_ = []
for num in range(1, UpperCAmelCase__ ):
A_ = 0
A_ = num
while iterations < 50:
A_ = sum_reverse(UpperCAmelCase__ )
iterations += 1
if is_palindrome(UpperCAmelCase__ ):
break
else:
lychrel_nums.append(UpperCAmelCase__ )
return len(UpperCAmelCase__ )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 162
| 0
|
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
a : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
a : int = """
Examples:
```py
>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image
>>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")
>>> repo = \"openai/shap-e-img2img\"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 3.0
>>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
>>> image = load_image(image_url).convert(\"RGB\")
>>> images = pipe(
... image,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
```
"""
@dataclass
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 42
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A , A , ) -> Union[str, Any]:
super().__init__()
self.register_modules(
prior=A , image_encoder=A , image_processor=A , scheduler=A , renderer=A , )
def _lowercase( self , A , A , A , A , A , A ) -> Any:
if latents is None:
UpperCAmelCase : Optional[Any] = randn_tensor(A , generator=A , device=A , dtype=A )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
UpperCAmelCase : Union[str, Any] = latents.to(A )
UpperCAmelCase : Any = latents * scheduler.init_noise_sigma
return latents
def _lowercase( self , A=0 ) -> Optional[int]:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
UpperCAmelCase : Union[str, Any] = torch.device(f'''cuda:{gpu_id}''' )
UpperCAmelCase : Dict = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(A , A )
@property
def _lowercase( self ) -> str:
if self.device != torch.device("""meta""" ) or not hasattr(self.image_encoder , """_hf_hook""" ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(A , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def _lowercase( self , A , A , A , A , ) -> Optional[Any]:
if isinstance(A , A ) and isinstance(image[0] , torch.Tensor ):
UpperCAmelCase : Union[str, Any] = torch.cat(A , axis=0 ) if image[0].ndim == 4 else torch.stack(A , axis=0 )
if not isinstance(A , torch.Tensor ):
UpperCAmelCase : Optional[Any] = self.image_processor(A , return_tensors="""pt""" ).pixel_values[0].unsqueeze(0 )
UpperCAmelCase : List[Any] = image.to(dtype=self.image_encoder.dtype , device=A )
UpperCAmelCase : Optional[int] = self.image_encoder(A )["""last_hidden_state"""]
UpperCAmelCase : Tuple = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
UpperCAmelCase : Tuple = image_embeds.repeat_interleave(A , dim=0 )
if do_classifier_free_guidance:
UpperCAmelCase : Optional[int] = torch.zeros_like(A )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase : Any = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(A )
def __call__( self , A , A = 1 , A = 25 , A = None , A = None , A = 4.0 , A = 64 , A = "pil" , A = True , ) -> str:
if isinstance(A , PIL.Image.Image ):
UpperCAmelCase : Union[str, Any] = 1
elif isinstance(A , torch.Tensor ):
UpperCAmelCase : Any = image.shape[0]
elif isinstance(A , A ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
UpperCAmelCase : Any = len(A )
else:
raise ValueError(
f'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(A )}''' )
UpperCAmelCase : Tuple = self._execution_device
UpperCAmelCase : str = batch_size * num_images_per_prompt
UpperCAmelCase : Optional[int] = guidance_scale > 1.0
UpperCAmelCase : int = self._encode_image(A , A , A , A )
# prior
self.scheduler.set_timesteps(A , device=A )
UpperCAmelCase : Union[str, Any] = self.scheduler.timesteps
UpperCAmelCase : Optional[Any] = self.prior.config.num_embeddings
UpperCAmelCase : int = self.prior.config.embedding_dim
UpperCAmelCase : Optional[Any] = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , A , A , A , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
UpperCAmelCase : Dict = latents.reshape(latents.shape[0] , A , A )
for i, t in enumerate(self.progress_bar(A ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase : int = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase : str = self.scheduler.scale_model_input(A , A )
UpperCAmelCase : int = self.prior(
A , timestep=A , proj_embedding=A , ).predicted_image_embedding
# remove the variance
UpperCAmelCase : Optional[Any] = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
UpperCAmelCase : Dict = noise_pred.chunk(2 )
UpperCAmelCase : List[str] = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
UpperCAmelCase : Tuple = self.scheduler.step(
A , timestep=A , sample=A , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=A )
UpperCAmelCase : Union[str, Any] = []
for i, latent in enumerate(A ):
print()
UpperCAmelCase : List[str] = self.renderer.decode(
latent[None, :] , A , size=A , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(A )
UpperCAmelCase : Any = torch.stack(A )
if output_type not in ["np", "pil"]:
raise ValueError(f'''Only the output types `pil` and `np` are supported not output_type={output_type}''' )
UpperCAmelCase : Dict = images.cpu().numpy()
if output_type == "pil":
UpperCAmelCase : Optional[int] = [self.numpy_to_pil(A ) for image in images]
# Offload last model to CPU
if hasattr(self , """final_offload_hook""" ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=A )
| 369
|
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ): # printing stars
print("""* """ , end="""""" )
print()
def __lowerCamelCase ( _lowercase ) -> Dict:
for i in range(_lowercase , 0 , -1 ):
for _ in range(_lowercase , 0 , -1 ): # printing stars
print("""* """ , end="""""" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(""" """ , end="""""" )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
if n <= 0:
print(""" ... .... nothing printing :(""" )
return
floyd(_lowercase ) # upper half
reverse_floyd(_lowercase ) # lower half
if __name__ == "__main__":
print(R"""| /\ | |- | |- |--| |\ /| |-""")
print(R"""|/ \| |- |_ |_ |__| | \/ | |_""")
a : List[Any] = 1
while K:
a : int = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
a : Tuple = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 338
| 0
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
__lowerCAmelCase = logging.get_logger(__name__)
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , *UpperCAmelCase , **UpperCAmelCase ) -> None:
warnings.warn(
"""The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use YolosImageProcessor instead.""" , UpperCAmelCase , )
super().__init__(*UpperCAmelCase , **UpperCAmelCase )
| 341
|
'''simple docstring'''
import os
from typing import Dict, List, Tuple, TypeVar, Union
__lowerCAmelCase = TypeVar('T')
__lowerCAmelCase = Union[List[T], Tuple[T, ...]]
__lowerCAmelCase = Union[T, List[T], Dict[str, T]]
__lowerCAmelCase = Union[str, bytes, os.PathLike]
| 341
| 1
|
'''simple docstring'''
def UpperCAmelCase ( a_ , a_ ) -> int:
"""simple docstring"""
while second != 0:
A_ : Union[str, Any] = first & second
first ^= second
A_ : List[Any] = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ : Optional[Any] = int(input('Enter the first number: ').strip())
UpperCamelCase__ : Optional[Any] = int(input('Enter the second number: ').strip())
print(f'{add(first, second) = }')
| 164
|
'''simple docstring'''
from statistics import mean, stdev
def UpperCAmelCase ( a_ , a_ = 3 ) -> list:
"""simple docstring"""
A_ : Tuple = min(a_ )
A_ : Union[str, Any] = max(a_ )
# normalize data
return [round((x - x_min) / (x_max - x_min) , a_ ) for x in data]
def UpperCAmelCase ( a_ , a_ = 3 ) -> list:
"""simple docstring"""
A_ : List[str] = mean(a_ )
A_ : List[str] = stdev(a_ )
# standardize data
return [round((x - mu) / (sigma) , a_ ) for x in data]
| 164
| 1
|
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __snake_case ( _SCREAMING_SNAKE_CASE):
"""simple docstring"""
def __lowercase ( self : Dict ) -> Optional[Any]:
lowerCAmelCase_ : Optional[int] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCamelCase , """embed_dim""" ) )
self.parent.assertTrue(hasattr(lowerCamelCase , """num_heads""" ) )
class __snake_case :
"""simple docstring"""
def __init__( self : str , lowerCamelCase : Any , lowerCamelCase : Dict=13 , lowerCamelCase : List[Any]=64 , lowerCamelCase : int=3 , lowerCamelCase : List[Any]=[16, 48, 96] , lowerCamelCase : int=[1, 3, 6] , lowerCamelCase : str=[1, 2, 10] , lowerCamelCase : Optional[Any]=[7, 3, 3] , lowerCamelCase : List[Any]=[4, 2, 2] , lowerCamelCase : Tuple=[2, 1, 1] , lowerCamelCase : Any=[2, 2, 2] , lowerCamelCase : int=[False, False, True] , lowerCamelCase : Optional[int]=[0.0, 0.0, 0.0] , lowerCamelCase : Tuple=0.02 , lowerCamelCase : Any=1E-12 , lowerCamelCase : Tuple=True , lowerCamelCase : Dict=True , lowerCamelCase : int=2 , ) -> int:
lowerCAmelCase_ : Any = parent
lowerCAmelCase_ : Any = batch_size
lowerCAmelCase_ : Any = image_size
lowerCAmelCase_ : Dict = patch_sizes
lowerCAmelCase_ : Dict = patch_stride
lowerCAmelCase_ : Tuple = patch_padding
lowerCAmelCase_ : Optional[int] = is_training
lowerCAmelCase_ : Tuple = use_labels
lowerCAmelCase_ : Union[str, Any] = num_labels
lowerCAmelCase_ : Any = num_channels
lowerCAmelCase_ : Optional[Any] = embed_dim
lowerCAmelCase_ : str = num_heads
lowerCAmelCase_ : Any = stride_kv
lowerCAmelCase_ : List[Any] = depth
lowerCAmelCase_ : int = cls_token
lowerCAmelCase_ : Union[str, Any] = attention_drop_rate
lowerCAmelCase_ : List[str] = initializer_range
lowerCAmelCase_ : Tuple = layer_norm_eps
def __lowercase ( self : Union[str, Any] ) -> Optional[int]:
lowerCAmelCase_ : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ : Any = None
if self.use_labels:
# create a random int32 tensor of given shape
lowerCAmelCase_ : List[str] = ids_tensor([self.batch_size] , self.num_labels )
lowerCAmelCase_ : Tuple = self.get_config()
return config, pixel_values, labels
def __lowercase ( self : List[str] ) -> Optional[int]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def __lowercase ( self : Optional[Any] , lowerCamelCase : Optional[int] , lowerCamelCase : str , lowerCamelCase : Tuple ) -> Optional[int]:
lowerCAmelCase_ : List[str] = TFCvtModel(config=lowerCamelCase )
lowerCAmelCase_ : Dict = model(lowerCamelCase , training=lowerCamelCase )
lowerCAmelCase_ : Dict = (self.image_size, self.image_size)
lowerCAmelCase_, lowerCAmelCase_ : int = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
lowerCAmelCase_ : Optional[Any] = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
lowerCAmelCase_ : List[Any] = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def __lowercase ( self : Union[str, Any] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Tuple , lowerCamelCase : List[str] ) -> List[Any]:
lowerCAmelCase_ : List[str] = self.num_labels
lowerCAmelCase_ : List[Any] = TFCvtForImageClassification(lowerCamelCase )
lowerCAmelCase_ : str = model(lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase ( self : Union[str, Any] ) -> Any:
lowerCAmelCase_ : Tuple = self.prepare_config_and_inputs()
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ : List[str] = config_and_inputs
lowerCAmelCase_ : Optional[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __snake_case ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,unittest.TestCase):
"""simple docstring"""
lowercase = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
lowercase = (
{'feature-extraction': TFCvtModel, 'image-classification': TFCvtForImageClassification}
if is_tf_available()
else {}
)
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def __lowercase ( self : List[Any] ) -> str:
lowerCAmelCase_ : Union[str, Any] = TFCvtModelTester(self )
lowerCAmelCase_ : Dict = TFCvtConfigTester(self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase , hidden_size=37 )
def __lowercase ( self : Any ) -> Union[str, Any]:
self.config_tester.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()
@unittest.skip(reason="""Cvt does not output attentions""" )
def __lowercase ( self : Optional[Any] ) -> List[str]:
pass
@unittest.skip(reason="""Cvt does not use inputs_embeds""" )
def __lowercase ( self : Tuple ) -> Tuple:
pass
@unittest.skip(reason="""Cvt does not support input and output embeddings""" )
def __lowercase ( self : Optional[Any] ) -> Union[str, Any]:
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
def __lowercase ( self : str ) -> str:
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
@slow
def __lowercase ( self : List[Any] ) -> int:
super().test_keras_fit()
@unittest.skip(reason="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8""" )
def __lowercase ( self : Optional[Any] ) -> Any:
lowerCAmelCase_ : Dict = tf.keras.mixed_precision.Policy("""mixed_float16""" )
tf.keras.mixed_precision.set_global_policy(lowerCamelCase )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy("""float32""" )
def __lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
lowerCAmelCase_, lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : str = model_class(lowerCamelCase )
lowerCAmelCase_ : List[Any] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : Dict = [*signature.parameters.keys()]
lowerCAmelCase_ : Union[str, Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCamelCase )
def __lowercase ( self : int ) -> List[str]:
def check_hidden_states_output(lowerCamelCase : List[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Any ):
lowerCAmelCase_ : str = model_class(lowerCamelCase )
lowerCAmelCase_ : str = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) )
lowerCAmelCase_ : int = outputs.hidden_states
lowerCAmelCase_ : List[str] = len(self.model_tester.depth )
self.assertEqual(len(lowerCamelCase ) , lowerCamelCase )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
lowerCAmelCase_, lowerCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Dict = True
check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ : str = True
check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def __lowercase ( self : List[Any] ) -> Optional[Any]:
lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def __lowercase ( self : List[str] ) -> Union[str, Any]:
lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase )
@slow
def __lowercase ( self : Optional[Any] ) -> Optional[int]:
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Union[str, Any] = TFCvtModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def UpperCamelCase_ ( ):
'''simple docstring'''
lowerCAmelCase_ : List[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __snake_case ( unittest.TestCase):
"""simple docstring"""
@cached_property
def __lowercase ( self : Optional[Any] ) -> List[str]:
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def __lowercase ( self : Optional[Any] ) -> List[str]:
lowerCAmelCase_ : int = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowerCAmelCase_ : Optional[int] = self.default_image_processor
lowerCAmelCase_ : List[str] = prepare_img()
lowerCAmelCase_ : List[Any] = image_processor(images=lowerCamelCase , return_tensors="""tf""" )
# forward pass
lowerCAmelCase_ : Union[str, Any] = model(**lowerCamelCase )
# verify the logits
lowerCAmelCase_ : Dict = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , lowerCamelCase )
lowerCAmelCase_ : str = tf.constant([0.9_285, 0.9_015, -0.3_150] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , lowerCamelCase , atol=1E-4 ) )
| 120
|
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( _SCREAMING_SNAKE_CASE ,unittest.TestCase):
"""simple docstring"""
lowercase = BioGptTokenizer
lowercase = False
def __lowercase ( self : List[Any] ) -> List[str]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ : Optional[Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
lowerCAmelCase_ : int = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
lowerCAmelCase_ : int = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
lowerCAmelCase_ : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase_ : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" ) as fp:
fp.write(json.dumps(lowerCamelCase ) )
with open(self.merges_file , """w""" ) as fp:
fp.write("""\n""".join(lowerCamelCase ) )
def __lowercase ( self : Union[str, Any] , lowerCamelCase : Any ) -> List[str]:
lowerCAmelCase_ : Dict = """lower newer"""
lowerCAmelCase_ : str = """lower newer"""
return input_text, output_text
def __lowercase ( self : Optional[int] ) -> str:
lowerCAmelCase_ : Any = BioGptTokenizer(self.vocab_file , self.merges_file )
lowerCAmelCase_ : int = """lower"""
lowerCAmelCase_ : str = ["""low""", """er</w>"""]
lowerCAmelCase_ : Any = tokenizer.tokenize(lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
lowerCAmelCase_ : Dict = tokens + ["""<unk>"""]
lowerCAmelCase_ : Optional[Any] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase ) , lowerCamelCase )
@slow
def __lowercase ( self : str ) -> Optional[Any]:
lowerCAmelCase_ : Dict = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
lowerCAmelCase_ : int = tokenizer.encode("""sequence builders""" , add_special_tokens=lowerCamelCase )
lowerCAmelCase_ : Optional[Any] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowerCamelCase )
lowerCAmelCase_ : str = tokenizer.build_inputs_with_special_tokens(lowerCamelCase )
lowerCAmelCase_ : Optional[int] = tokenizer.build_inputs_with_special_tokens(lowerCamelCase , lowerCamelCase )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 120
| 1
|
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
UpperCamelCase_ = '''http://www.mocksite.com/file1.txt'''
UpperCamelCase_ = '''"text": ["foo", "foo"]'''
UpperCamelCase_ = '''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''
class snake_case :
a_ : Union[str, Any] = 200
a_ : str = {'''Content-Length''': '''100'''}
a_ : Any = {}
def UpperCAmelCase__ ( self , **__UpperCAmelCase) ->Optional[Any]:
return [bytes(__snake_case , "utf-8")]
def UpperCamelCase ( *UpperCAmelCase , **UpperCAmelCase ) ->Tuple:
"""simple docstring"""
return MockResponse()
@pytest.mark.parametrize("urls_type" , [str, list, dict] )
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Dict:
"""simple docstring"""
import requests
monkeypatch.setattr(__lowerCAmelCase , "request" , __lowerCAmelCase )
a_ = URL
if issubclass(__lowerCAmelCase , __lowerCAmelCase ):
a_ = url
elif issubclass(__lowerCAmelCase , __lowerCAmelCase ):
a_ = [url]
elif issubclass(__lowerCAmelCase , __lowerCAmelCase ):
a_ = {"train": url}
a_ = "dummy"
a_ = "downloads"
a_ = tmp_path
a_ = DownloadConfig(
cache_dir=os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , use_etag=__lowerCAmelCase , )
a_ = DownloadManager(dataset_name=__lowerCAmelCase , download_config=__lowerCAmelCase )
a_ = dl_manager.download(__lowerCAmelCase )
a_ = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
a_ = [downloaded_paths]
a_ = [urls]
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
assert "train" in downloaded_paths.keys()
a_ = downloaded_paths.values()
a_ = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(__lowerCAmelCase , __lowerCAmelCase ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
a_ = Path(__lowerCAmelCase )
a_ = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
a_ = downloaded_path.read_text()
assert content == CONTENT
a_ = downloaded_path.with_suffix(".json" )
assert metadata_downloaded_path.exists()
a_ = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize("paths_type" , [str, list, dict] )
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
a_ = str(__lowerCAmelCase )
if issubclass(__lowerCAmelCase , __lowerCAmelCase ):
a_ = filename
elif issubclass(__lowerCAmelCase , __lowerCAmelCase ):
a_ = [filename]
elif issubclass(__lowerCAmelCase , __lowerCAmelCase ):
a_ = {"train": filename}
a_ = "dummy"
a_ = xz_file.parent
a_ = "extracted"
a_ = DownloadConfig(
cache_dir=__lowerCAmelCase , use_etag=__lowerCAmelCase , )
a_ = DownloadManager(dataset_name=__lowerCAmelCase , download_config=__lowerCAmelCase )
a_ = dl_manager.extract(__lowerCAmelCase )
a_ = paths
for extracted_paths in [extracted_paths]:
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
a_ = [extracted_paths]
a_ = [paths]
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
assert "train" in extracted_paths.keys()
a_ = extracted_paths.values()
a_ = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(__lowerCAmelCase , __lowerCAmelCase ):
assert extracted_path == dl_manager.extracted_paths[input_path]
a_ = Path(__lowerCAmelCase )
a_ = extracted_path.parts
assert parts[-1] == hash_url_to_filename(__lowerCAmelCase , etag=__lowerCAmelCase )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
a_ = extracted_path.read_text()
a_ = text_file.read_text()
assert extracted_file_content == expected_file_content
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->Dict:
"""simple docstring"""
assert path.endswith(".jsonl" )
for num_items, line in enumerate(__lowerCAmelCase , start=1 ):
a_ = json.loads(line.decode("utf-8" ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize("archive_jsonl" , ["tar_jsonl_path", "zip_jsonl_path"] )
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->Optional[Any]:
"""simple docstring"""
a_ = request.getfixturevalue(__lowerCAmelCase )
a_ = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__lowerCAmelCase ) , start=1 ):
_test_jsonl(__lowerCAmelCase , __lowerCAmelCase )
assert num_jsonl == 2
@pytest.mark.parametrize("archive_nested_jsonl" , ["tar_nested_jsonl_path", "zip_nested_jsonl_path"] )
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
a_ = request.getfixturevalue(__lowerCAmelCase )
a_ = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__lowerCAmelCase ) , start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__lowerCAmelCase ) , start=1 ):
_test_jsonl(__lowerCAmelCase , __lowerCAmelCase )
assert num_tar == 1
assert num_jsonl == 2
def UpperCamelCase ( UpperCAmelCase ) ->List[str]:
"""simple docstring"""
a_ = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(__lowerCAmelCase ) , start=1 ):
assert os.path.basename(__lowerCAmelCase ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 371
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase_ = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
'SwiftFormerOnnxConfig',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'SwiftFormerForImageClassification',
'SwiftFormerModel',
'SwiftFormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 303
| 0
|
def __snake_case ( _UpperCAmelCase ):
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
__a = len(_UpperCAmelCase )
__a = max(_UpperCAmelCase )
__a = min(_UpperCAmelCase )
# create the counting array
__a = coll_max + 1 - coll_min
__a = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1 , _UpperCAmelCase ):
__a = counting_arr[i] + counting_arr[i - 1]
# create the output collection
__a = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0 , _UpperCAmelCase ) ):
__a = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def __snake_case ( _UpperCAmelCase ):
return "".join([chr(_UpperCAmelCase ) for i in counting_sort([ord(_UpperCAmelCase ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string('''thisisthestring''') == "eghhiiinrsssttt"
__snake_case :Tuple = input('''Enter numbers separated by a comma:\n''').strip()
__snake_case :Dict = [int(item) for item in user_input.split(''',''')]
print(counting_sort(unsorted))
| 49
|
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
__snake_case :Dict = '''bart'''
__snake_case :Tuple = True
@st.cache(allow_output_mutation=_UpperCAmelCase )
def __snake_case ( ):
if LOAD_DENSE_INDEX:
__a = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
__a = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
__a = qar_model.eval()
else:
__a , __a = (None, None)
if MODEL_TYPE == "bart":
__a = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
__a = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
__a = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
__a = sas_model.eval()
else:
__a , __a = make_qa_sas_model(
model_name='''t5-small''' , from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''' , device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=_UpperCAmelCase )
def __snake_case ( ):
if LOAD_DENSE_INDEX:
__a = faiss.StandardGpuResources()
__a = datasets.load_dataset(path='''wiki_snippets''' , name='''wiki40b_en_100_0''' )['''train''']
__a = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''' , dtype='''float32''' , mode='''r''' , shape=(wikiaab_passages.num_rows, 128) , )
__a = faiss.IndexFlatIP(128 )
__a = faiss.index_cpu_to_gpu(_UpperCAmelCase , 1 , _UpperCAmelCase )
wikiaab_gpu_index_flat.add(_UpperCAmelCase ) # TODO fix for larger GPU
else:
__a , __a = (None, None)
__a = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=_UpperCAmelCase )
def __snake_case ( ):
__a = datasets.load_dataset('''eli5''' , name='''LFQA_reddit''' )
__a = elia['''train_eli5''']
__a = np.memmap(
'''eli5_questions_reps.dat''' , dtype='''float32''' , mode='''r''' , shape=(elia_train.num_rows, 128) )
__a = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(_UpperCAmelCase )
return (elia_train, eli5_train_q_index)
__snake_case ,__snake_case ,__snake_case :List[str] = load_indexes()
__snake_case ,__snake_case ,__snake_case ,__snake_case :Dict = load_models()
__snake_case ,__snake_case :Tuple = load_train_data()
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase=10 ):
__a = embed_questions_for_retrieval([question] , _UpperCAmelCase , _UpperCAmelCase )
__a , __a = eli5_train_q_index.search(_UpperCAmelCase , _UpperCAmelCase )
__a = [elia_train[int(_UpperCAmelCase )] for i in I[0]]
return nn_examples
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase="wiki40b" , _UpperCAmelCase="dense" , _UpperCAmelCase=10 ):
if source == "none":
__a , __a = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
__a , __a = query_qa_dense_index(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
else:
__a , __a = query_es_index(
_UpperCAmelCase , _UpperCAmelCase , index_name='''english_wiki40b_snippets_100w''' , n_results=_UpperCAmelCase , )
__a = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
__a = '''question: {} context: {}'''.format(_UpperCAmelCase , _UpperCAmelCase )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda _UpperCAmelCase : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _UpperCAmelCase : None),
} )
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=64 , _UpperCAmelCase=256 , _UpperCAmelCase=False , _UpperCAmelCase=2 , _UpperCAmelCase=0.95 , _UpperCAmelCase=0.8 ):
with torch.no_grad():
__a = qa_sas_generate(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , num_answers=1 , num_beams=_UpperCAmelCase , min_len=_UpperCAmelCase , max_len=_UpperCAmelCase , do_sample=_UpperCAmelCase , temp=_UpperCAmelCase , top_p=_UpperCAmelCase , top_k=_UpperCAmelCase , max_input_length=1024 , device='''cuda:0''' , )[0]
return (answer, support_list)
st.title('''Long Form Question Answering with ELI5''')
# Start sidebar
__snake_case :Dict = '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
__snake_case :int = '''
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class="img-container"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
''' % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
__snake_case :int = '''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)
__snake_case :Union[str, Any] = [
'''Answer the question''',
'''View the retrieved document only''',
'''View the most similar ELI5 question and answer''',
'''Show me everything, please!''',
]
__snake_case :int = st.sidebar.checkbox('''Demo options''')
if demo_options:
__snake_case :str = st.sidebar.selectbox(
'''''',
action_list,
index=3,
)
__snake_case :Tuple = action_list.index(action_st)
__snake_case :Optional[int] = st.sidebar.selectbox(
'''''',
['''Show full text of passages''', '''Show passage section titles'''],
index=0,
)
__snake_case :Dict = show_type == '''Show full text of passages'''
else:
__snake_case :Dict = 3
__snake_case :str = True
__snake_case :Optional[Any] = st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
__snake_case :List[str] = '''
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
'''
st.sidebar.markdown(retriever_info)
__snake_case :Dict = st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none'''])
__snake_case :Optional[int] = st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed'''])
else:
__snake_case :Optional[int] = '''wiki40b'''
__snake_case :Dict = '''dense'''
__snake_case :Dict = '''beam'''
__snake_case :int = 2
__snake_case :str = 64
__snake_case :Tuple = 256
__snake_case :int = None
__snake_case :List[Any] = None
__snake_case :int = st.sidebar.checkbox('''Generation options''')
if generate_options:
__snake_case :Tuple = '''
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder\'s output probabilities.
'''
st.sidebar.markdown(generate_info)
__snake_case :Tuple = st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
__snake_case :Dict = st.sidebar.slider(
'''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
__snake_case :Dict = st.sidebar.slider(
'''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
__snake_case :List[str] = st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
__snake_case :Tuple = st.sidebar.slider(
'''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.9_5, step=0.0_1, format=None, key=None
)
__snake_case :Any = st.sidebar.slider(
'''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
)
__snake_case :Any = None
# start main text
__snake_case :Dict = [
'''<MY QUESTION>''',
'''How do people make chocolate?''',
'''Why do we get a fever when we are sick?''',
'''How can different animals perceive different colors?''',
'''What is natural language processing?''',
'''What\'s the best way to treat a sunburn?''',
'''What exactly are vitamins ?''',
'''How does nuclear energy provide electricity?''',
'''What\'s the difference between viruses and bacteria?''',
'''Why are flutes classified as woodwinds when most of them are made out of metal ?''',
'''Why do people like drinking coffee even though it tastes so bad?''',
'''What happens when wine ages? How does it make the wine taste better?''',
'''If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?''',
'''How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?''',
'''How does New Zealand have so many large bird predators?''',
]
__snake_case :int = st.selectbox(
'''What would you like to ask? ---- select <MY QUESTION> to enter a new query''',
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
__snake_case :Optional[int] = st.text_input('''Enter your question here:''', '''''')
else:
__snake_case :Optional[int] = question_s
if st.button('''Show me!'''):
if action in [0, 1, 3]:
if index_type == "mixed":
__snake_case ,__snake_case :int = make_support(question, source=wiki_source, method='''dense''', n_results=10)
__snake_case ,__snake_case :Optional[int] = make_support(question, source=wiki_source, method='''sparse''', n_results=10)
__snake_case :Optional[Any] = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
__snake_case :Union[str, Any] = support_list[:10]
__snake_case :Optional[int] = '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
else:
__snake_case ,__snake_case :Tuple = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
__snake_case ,__snake_case :Optional[int] = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == '''sampled'''),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown('''### The model generated answer is:''')
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown('''--- \n ### The model is drawing information from the following Wikipedia passages:''')
for i, res in enumerate(support_list):
__snake_case :Dict = '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
__snake_case :int = res[1].strip()
if sec_titles == "":
__snake_case :List[Any] = '''[{}]({})'''.format(res[0], wiki_url)
else:
__snake_case :Optional[int] = sec_titles.split(''' & ''')
__snake_case :str = ''' & '''.join(
['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
)
st.markdown(
'''{0:02d} - **Article**: {1:<18} <br> _Section_: {2}'''.format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
'''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
)
if action in [2, 3]:
__snake_case :str = find_nearest_training(question)
__snake_case :str = nn_train_list[0]
st.markdown(
'''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
)
__snake_case :Optional[Any] = [
'''{}. {}'''.format(i + 1, ''' \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
if i == 0 or sc > 2
]
st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))
__snake_case :Tuple = '''
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 49
| 1
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class lowerCAmelCase__ :
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=13 , __SCREAMING_SNAKE_CASE=7 , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=99 , __SCREAMING_SNAKE_CASE=32 , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE=4 , __SCREAMING_SNAKE_CASE=37 , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=5_12 , __SCREAMING_SNAKE_CASE=16 , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE=0.02 , __SCREAMING_SNAKE_CASE=3 , __SCREAMING_SNAKE_CASE=4 , __SCREAMING_SNAKE_CASE=None , ):
"""simple docstring"""
lowercase_ : int = parent
lowercase_ : List[str] = 13
lowercase_ : Union[str, Any] = 7
lowercase_ : Tuple = True
lowercase_ : List[Any] = True
lowercase_ : List[Any] = True
lowercase_ : Tuple = True
lowercase_ : List[Any] = 99
lowercase_ : int = 32
lowercase_ : Optional[int] = 2
lowercase_ : Optional[int] = 4
lowercase_ : Optional[Any] = 37
lowercase_ : List[str] = '''gelu'''
lowercase_ : Union[str, Any] = 0.1
lowercase_ : Union[str, Any] = 0.1
lowercase_ : Any = 5_12
lowercase_ : Union[str, Any] = 16
lowercase_ : Optional[Any] = 2
lowercase_ : Any = 0.02
lowercase_ : List[Any] = 3
lowercase_ : int = 4
lowercase_ : Optional[Any] = None
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ : Optional[Any] = None
if self.use_input_mask:
lowercase_ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase_ : Tuple = None
if self.use_token_type_ids:
lowercase_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase_ : Dict = None
lowercase_ : List[str] = None
lowercase_ : Union[str, Any] = None
if self.use_labels:
lowercase_ : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase_ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
lowercase_ : Union[str, Any] = 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 , initializer_range=self.initializer_range , return_dict=__SCREAMING_SNAKE_CASE , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase_ : Optional[int] = TFRoFormerModel(config=__SCREAMING_SNAKE_CASE )
lowercase_ : int = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowercase_ : Dict = [input_ids, input_mask]
lowercase_ : Union[str, Any] = model(__SCREAMING_SNAKE_CASE )
lowercase_ : Any = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase_ : Optional[Any] = True
lowercase_ : Tuple = TFRoFormerForCausalLM(config=__SCREAMING_SNAKE_CASE )
lowercase_ : str = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase_ : Any = model(__SCREAMING_SNAKE_CASE )['''logits''']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase_ : str = TFRoFormerForMaskedLM(config=__SCREAMING_SNAKE_CASE )
lowercase_ : Union[str, Any] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase_ : Dict = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase_ : Optional[int] = self.num_labels
lowercase_ : List[str] = TFRoFormerForSequenceClassification(config=__SCREAMING_SNAKE_CASE )
lowercase_ : str = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase_ : Union[str, Any] = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase_ : Union[str, Any] = self.num_choices
lowercase_ : str = TFRoFormerForMultipleChoice(config=__SCREAMING_SNAKE_CASE )
lowercase_ : Tuple = tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
lowercase_ : Dict = tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
lowercase_ : Optional[int] = tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
lowercase_ : Dict = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
lowercase_ : Tuple = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase_ : List[Any] = self.num_labels
lowercase_ : Optional[int] = TFRoFormerForTokenClassification(config=__SCREAMING_SNAKE_CASE )
lowercase_ : Any = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase_ : Optional[int] = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase_ : Optional[Any] = TFRoFormerForQuestionAnswering(config=__SCREAMING_SNAKE_CASE )
lowercase_ : Union[str, Any] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase_ : Tuple = model(__SCREAMING_SNAKE_CASE )
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 _snake_case ( self ):
"""simple docstring"""
lowercase_ : List[str] = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) : Any = config_and_inputs
lowercase_ : List[str] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase__ ( snake_case_ , snake_case_ , unittest.TestCase ):
lowerCAmelCase_ = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
lowerCAmelCase_ = (
{
'''feature-extraction''': TFRoFormerModel,
'''fill-mask''': TFRoFormerForMaskedLM,
'''question-answering''': TFRoFormerForQuestionAnswering,
'''text-classification''': TFRoFormerForSequenceClassification,
'''text-generation''': TFRoFormerForCausalLM,
'''token-classification''': TFRoFormerForTokenClassification,
'''zero-shot''': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[Any] = TFRoFormerModelTester(self )
lowercase_ : Tuple = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , hidden_size=37 )
def _snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE )
@slow
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Any = TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''' )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
@slow
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : str = TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
lowercase_ : List[Any] = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowercase_ : Dict = model(__SCREAMING_SNAKE_CASE )[0]
# TODO Replace vocab size
lowercase_ : str = 5_00_00
lowercase_ : List[Any] = [1, 6, vocab_size]
self.assertEqual(output.shape , __SCREAMING_SNAKE_CASE )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
lowercase_ : Optional[int] = tf.constant(
[
[
[-0.12_053_341, -1.0_264_901, 0.29_221_946],
[-1.5_133_783, 0.197_433, 0.15_190_607],
[-5.0_135_403, -3.900_256, -0.84_038_764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 )
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
lowerCAmelCase_ = 1e-4
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[int] = tf.constant([[4, 10]] )
lowercase_ : Any = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
lowercase_ : int = emba(input_ids.shape )
lowercase_ : List[str] = tf.constant(
[[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]] )
tf.debugging.assert_near(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=self.tolerance )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Tuple = tf.constant(
[
[0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
[0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
[0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
] )
lowercase_ : Optional[int] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12 )
emba([2, 16, 5_12] )
lowercase_ : Union[str, Any] = emba.weight[:3, :5]
tf.debugging.assert_near(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=self.tolerance )
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
lowerCAmelCase_ = 1e-4
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : List[Any] = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
lowercase_ : int = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
lowercase_ : Optional[Any] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
lowercase_ : List[Any] = embed_positions([2, 16, 7_68] )[None, None, :, :]
lowercase_ , lowercase_ : Optional[Any] = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase_ : int = tf.constant(
[
[0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
[-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
[-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
[-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
[0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
[3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
] )
lowercase_ : Dict = tf.constant(
[
[0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
[0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
[1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
[2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
[-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
[-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , __SCREAMING_SNAKE_CASE , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , __SCREAMING_SNAKE_CASE , atol=self.tolerance )
| 353
|
'''simple docstring'''
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 : Optional[Any] = logging.get_logger(__name__)
_lowercase : str = "▁"
_lowercase : Optional[int] = {"vocab_file": "sentencepiece.bpe.model"}
_lowercase : Dict = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
_lowercase : Optional[Any] = {
"facebook/xglm-564M": 2_0_4_8,
}
class lowerCAmelCase__ ( lowerCamelCase_ ):
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = ['''input_ids''', '''attention_mask''']
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE="<s>" , __SCREAMING_SNAKE_CASE="</s>" , __SCREAMING_SNAKE_CASE="</s>" , __SCREAMING_SNAKE_CASE="<s>" , __SCREAMING_SNAKE_CASE="<unk>" , __SCREAMING_SNAKE_CASE="<pad>" , __SCREAMING_SNAKE_CASE = None , **__SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
lowercase_ : Any = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
lowercase_ : Optional[Any] = 7
lowercase_ : List[Any] = [F'''<madeupword{i}>''' for i in range(self.num_madeup_words )]
lowercase_ : Tuple = kwargs.get('''additional_special_tokens''' , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , sep_token=__SCREAMING_SNAKE_CASE , cls_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **__SCREAMING_SNAKE_CASE , )
lowercase_ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__SCREAMING_SNAKE_CASE ) )
lowercase_ : Dict = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
lowercase_ : List[Any] = 1
# Mimic fairseq token-to-id alignment for the first 4 token
lowercase_ : Optional[Any] = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
lowercase_ : Dict = len(self.sp_model )
lowercase_ : int = {F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(__SCREAMING_SNAKE_CASE )
lowercase_ : List[str] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
"""simple docstring"""
lowercase_ : List[Any] = self.__dict__.copy()
lowercase_ : Optional[Any] = None
lowercase_ : List[Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase_ : List[Any] = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowercase_ : Optional[Any] = {}
lowercase_ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
lowercase_ : Optional[Any] = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__SCREAMING_SNAKE_CASE , token_ids_a=__SCREAMING_SNAKE_CASE , already_has_special_tokens=__SCREAMING_SNAKE_CASE )
if token_ids_a is None:
return [1] + ([0] * len(__SCREAMING_SNAKE_CASE ))
return [1] + ([0] * len(__SCREAMING_SNAKE_CASE )) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE ))
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None ):
"""simple docstring"""
lowercase_ : List[Any] = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def _snake_case ( self ):
"""simple docstring"""
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Any = {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _snake_case ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return self.sp_model.encode(__SCREAMING_SNAKE_CASE , out_type=__SCREAMING_SNAKE_CASE )
def _snake_case ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowercase_ : str = self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _snake_case ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _snake_case ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase_ : Dict = ''''''.join(__SCREAMING_SNAKE_CASE ).replace(__SCREAMING_SNAKE_CASE , ''' ''' ).strip()
return out_string
def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None ):
"""simple docstring"""
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowercase_ : str = os.path.join(
__SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(__SCREAMING_SNAKE_CASE , '''wb''' ) as fi:
lowercase_ : Optional[int] = self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 264
| 0
|
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
SCREAMING_SNAKE_CASE_ = '''platform'''
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class UpperCamelCase__ :
'''simple docstring'''
__snake_case : str = PegasusConfig
__snake_case : List[Any] = {}
__snake_case : Optional[Any] = """gelu"""
def __init__( self : str ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Dict=13 ,lowerCamelCase__ : Union[str, Any]=7 ,lowerCamelCase__ : Tuple=True ,lowerCamelCase__ : Tuple=False ,lowerCamelCase__ : str=99 ,lowerCamelCase__ : Tuple=32 ,lowerCamelCase__ : List[Any]=5 ,lowerCamelCase__ : List[Any]=4 ,lowerCamelCase__ : List[str]=37 ,lowerCamelCase__ : Dict=0.1 ,lowerCamelCase__ : List[Any]=0.1 ,lowerCamelCase__ : List[Any]=20 ,lowerCamelCase__ : str=2 ,lowerCamelCase__ : Any=1 ,lowerCamelCase__ : int=0 ,) -> List[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_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_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = eos_token_id
SCREAMING_SNAKE_CASE = pad_token_id
SCREAMING_SNAKE_CASE = bos_token_id
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size ).clip(3 ,self.vocab_size )
SCREAMING_SNAKE_CASE = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) ,1 )
SCREAMING_SNAKE_CASE = np.concatenate([input_ids, eos_tensor] ,axis=1 )
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE = self.config_cls(
vocab_size=self.vocab_size ,d_model=self.hidden_size ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,eos_token_ids=[2] ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.pad_token_id ,**self.config_updates ,)
SCREAMING_SNAKE_CASE = prepare_pegasus_inputs_dict(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
return config, inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : Any ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Tuple ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 20
SCREAMING_SNAKE_CASE = model_class_name(__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = model.encode(inputs_dict["""input_ids"""] )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
SCREAMING_SNAKE_CASE = model.init_cache(decoder_input_ids.shape[0] ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,dtype="""i4""" )
SCREAMING_SNAKE_CASE = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
SCREAMING_SNAKE_CASE = model.decode(
decoder_input_ids[:, :-1] ,__SCREAMING_SNAKE_CASE ,decoder_attention_mask=__SCREAMING_SNAKE_CASE ,past_key_values=__SCREAMING_SNAKE_CASE ,decoder_position_ids=__SCREAMING_SNAKE_CASE ,)
SCREAMING_SNAKE_CASE = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype="""i4""" )
SCREAMING_SNAKE_CASE = model.decode(
decoder_input_ids[:, -1:] ,__SCREAMING_SNAKE_CASE ,decoder_attention_mask=__SCREAMING_SNAKE_CASE ,past_key_values=outputs_cache.past_key_values ,decoder_position_ids=__SCREAMING_SNAKE_CASE ,)
SCREAMING_SNAKE_CASE = model.decode(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 ,msg=F"""Max diff is {diff}""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : List[str] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 20
SCREAMING_SNAKE_CASE = model_class_name(__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = model.encode(inputs_dict["""input_ids"""] )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
SCREAMING_SNAKE_CASE = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] ,axis=-1 ,)
SCREAMING_SNAKE_CASE = model.init_cache(decoder_input_ids.shape[0] ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
SCREAMING_SNAKE_CASE = model.decode(
decoder_input_ids[:, :-1] ,__SCREAMING_SNAKE_CASE ,decoder_attention_mask=__SCREAMING_SNAKE_CASE ,past_key_values=__SCREAMING_SNAKE_CASE ,decoder_position_ids=__SCREAMING_SNAKE_CASE ,)
SCREAMING_SNAKE_CASE = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype="""i4""" )
SCREAMING_SNAKE_CASE = model.decode(
decoder_input_ids[:, -1:] ,__SCREAMING_SNAKE_CASE ,past_key_values=outputs_cache.past_key_values ,decoder_attention_mask=__SCREAMING_SNAKE_CASE ,decoder_position_ids=__SCREAMING_SNAKE_CASE ,)
SCREAMING_SNAKE_CASE = model.decode(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,decoder_attention_mask=__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 ,msg=F"""Max diff is {diff}""" )
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ) -> Optional[int]:
'''simple docstring'''
if attention_mask is None:
SCREAMING_SNAKE_CASE = np.not_equal(snake_case__ , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class UpperCamelCase__ ( UpperCamelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : Optional[int] = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
__snake_case : int = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
__snake_case : int = True
__snake_case : str = False
__snake_case : Dict = False
__snake_case : Tuple = False
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = FlaxPegasusModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self ,config_class=__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE = self._prepare_for_class(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
@jax.jit
def encode_jitted(lowerCamelCase__ : Dict ,lowerCamelCase__ : List[str]=None ,**lowerCamelCase__ : Dict ):
return model.encode(input_ids=__SCREAMING_SNAKE_CASE ,attention_mask=__SCREAMING_SNAKE_CASE )
with self.subTest("""JIT Enabled""" ):
SCREAMING_SNAKE_CASE = encode_jitted(**__SCREAMING_SNAKE_CASE ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE = encode_jitted(**__SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) ,len(__SCREAMING_SNAKE_CASE ) )
for jitted_output, output in zip(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ):
self.assertEqual(jitted_output.shape ,output.shape )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = model.encode(inputs_dict["""input_ids"""] ,inputs_dict["""attention_mask"""] )
SCREAMING_SNAKE_CASE = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCamelCase__ : int ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Any ):
return model.decode(
decoder_input_ids=__SCREAMING_SNAKE_CASE ,decoder_attention_mask=__SCREAMING_SNAKE_CASE ,encoder_outputs=__SCREAMING_SNAKE_CASE ,)
with self.subTest("""JIT Enabled""" ):
SCREAMING_SNAKE_CASE = decode_jitted(**__SCREAMING_SNAKE_CASE ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE = decode_jitted(**__SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) ,len(__SCREAMING_SNAKE_CASE ) )
for jitted_output, output in zip(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ):
self.assertEqual(jitted_output.shape ,output.shape )
@slow
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class_name.from_pretrained("""google/pegasus-large""" ,from_pt=__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = np.ones((1, 1) )
SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
@slow
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""" )
SCREAMING_SNAKE_CASE = PegasusTokenizer.from_pretrained("""google/pegasus-xsum""" )
SCREAMING_SNAKE_CASE = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
SCREAMING_SNAKE_CASE = [
"""California\'s largest electricity provider has turned off power to hundreds of thousands of customers.""",
"""Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.""",
]
SCREAMING_SNAKE_CASE = tokenizer(__SCREAMING_SNAKE_CASE ,return_tensors="""np""" ,truncation=__SCREAMING_SNAKE_CASE ,max_length=512 ,padding=__SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = model.generate(**__SCREAMING_SNAKE_CASE ,num_beams=2 ).sequences
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE ,skip_special_tokens=__SCREAMING_SNAKE_CASE )
assert tgt_text == decoded
| 296
|
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
UpperCAmelCase_ : str = (DDPMScheduler,)
def SCREAMING_SNAKE_CASE_ ( self , **__SCREAMING_SNAKE_CASE ) ->Optional[Any]:
lowerCAmelCase = {
'''num_train_timesteps''': 1000,
'''beta_start''': 0.0_0_0_1,
'''beta_end''': 0.0_2,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**__SCREAMING_SNAKE_CASE )
return config
def SCREAMING_SNAKE_CASE_ ( self ) ->Tuple:
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE , beta_end=__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[Any]:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self ) ->Dict:
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self ) ->List[str]:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self ) ->Any:
self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__SCREAMING_SNAKE_CASE , prediction_type=__SCREAMING_SNAKE_CASE , sample_max_value=__SCREAMING_SNAKE_CASE , )
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[int]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[Any]:
for t in [0, 500, 999]:
self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self ) ->Tuple:
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config()
lowerCAmelCase = scheduler_class(**__SCREAMING_SNAKE_CASE )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1e-5
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config()
lowerCAmelCase = scheduler_class(**__SCREAMING_SNAKE_CASE )
lowerCAmelCase = len(__SCREAMING_SNAKE_CASE )
lowerCAmelCase = self.dummy_model()
lowerCAmelCase = self.dummy_sample_deter
lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(__SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
lowerCAmelCase = model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
lowerCAmelCase = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
lowerCAmelCase = pred_prev_sample
lowerCAmelCase = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
lowerCAmelCase = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config(prediction_type='''v_prediction''' )
lowerCAmelCase = scheduler_class(**__SCREAMING_SNAKE_CASE )
lowerCAmelCase = len(__SCREAMING_SNAKE_CASE )
lowerCAmelCase = self.dummy_model()
lowerCAmelCase = self.dummy_sample_deter
lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(__SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
lowerCAmelCase = model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
lowerCAmelCase = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
lowerCAmelCase = pred_prev_sample
lowerCAmelCase = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
lowerCAmelCase = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def SCREAMING_SNAKE_CASE_ ( self ) ->Any:
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config()
lowerCAmelCase = scheduler_class(**__SCREAMING_SNAKE_CASE )
lowerCAmelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE )
lowerCAmelCase = scheduler.timesteps
for i, timestep in enumerate(__SCREAMING_SNAKE_CASE ):
if i == len(__SCREAMING_SNAKE_CASE ) - 1:
lowerCAmelCase = -1
else:
lowerCAmelCase = timesteps[i + 1]
lowerCAmelCase = scheduler.previous_timestep(__SCREAMING_SNAKE_CASE )
lowerCAmelCase = prev_t.item()
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[Any]:
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config()
lowerCAmelCase = scheduler_class(**__SCREAMING_SNAKE_CASE )
lowerCAmelCase = [100, 87, 50, 51, 0]
with self.assertRaises(__SCREAMING_SNAKE_CASE , msg='''`custom_timesteps` must be in descending order.''' ):
scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config()
lowerCAmelCase = scheduler_class(**__SCREAMING_SNAKE_CASE )
lowerCAmelCase = [100, 87, 50, 1, 0]
lowerCAmelCase = len(__SCREAMING_SNAKE_CASE )
with self.assertRaises(__SCREAMING_SNAKE_CASE , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.''' ):
scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE , timesteps=__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self ) ->Any:
lowerCAmelCase = self.scheduler_classes[0]
lowerCAmelCase = self.get_scheduler_config()
lowerCAmelCase = scheduler_class(**__SCREAMING_SNAKE_CASE )
lowerCAmelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__SCREAMING_SNAKE_CASE , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE )
| 338
| 0
|
"""simple docstring"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
UpperCAmelCase : Optional[int] = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def _UpperCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None):
"""simple docstring"""
lowercase_ = self.layer[current_layer](lowerCAmelCase_ , lowerCAmelCase_ , head_mask[current_layer])
lowercase_ = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top." , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Any , lowerCAmelCase_ : Dict):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = BertEncoderWithPabee(lowerCAmelCase_)
self.init_weights()
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Dict , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = threshold
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Optional[Any]):
"""simple docstring"""
lowercase_ = patience
def _UpperCAmelCase ( self : Dict):
"""simple docstring"""
lowercase_ = 0
lowercase_ = 0
def _UpperCAmelCase ( self : Optional[int]):
"""simple docstring"""
lowercase_ = self.inference_layers_num / self.inference_instances_num
lowercase_ = (
F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
)
print(lowerCAmelCase_)
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : str=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=False , ):
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""")
elif input_ids is not None:
lowercase_ = input_ids.size()
elif inputs_embeds is not None:
lowercase_ = inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""")
lowercase_ = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
if token_type_ids is None:
lowercase_ = torch.zeros(lowerCAmelCase_ , dtype=torch.long , device=lowerCAmelCase_)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
lowercase_ = self.get_extended_attention_mask(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
lowercase_ , lowercase_ , lowercase_ = encoder_hidden_states.size()
lowercase_ = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
lowercase_ = torch.ones(lowerCAmelCase_ , device=lowerCAmelCase_)
lowercase_ = self.invert_attention_mask(lowerCAmelCase_)
else:
lowercase_ = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
lowercase_ = self.get_head_mask(lowerCAmelCase_ , self.config.num_hidden_layers)
lowercase_ = self.embeddings(
input_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_)
lowercase_ = embedding_output
if self.training:
lowercase_ = []
for i in range(self.config.num_hidden_layers):
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](output_dropout(lowerCAmelCase_))
res.append(lowerCAmelCase_)
elif self.patience == 0: # Use all layers for inference
lowercase_ = self.encoder(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , encoder_attention_mask=lowerCAmelCase_ , )
lowercase_ = self.pooler(encoder_outputs[0])
lowercase_ = [output_layers[self.config.num_hidden_layers - 1](lowerCAmelCase_)]
else:
lowercase_ = 0
lowercase_ = None
lowercase_ = 0
for i in range(self.config.num_hidden_layers):
calculated_layer_num += 1
lowercase_ = self.encoder.adaptive_forward(
lowerCAmelCase_ , current_layer=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_)
lowercase_ = self.pooler(lowerCAmelCase_)
lowercase_ = output_layers[i](lowerCAmelCase_)
if regression:
lowercase_ = logits.detach()
if patient_result is not None:
lowercase_ = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
patient_counter += 1
else:
lowercase_ = 0
else:
lowercase_ = logits.detach().argmax(dim=1)
if patient_result is not None:
lowercase_ = patient_result.detach().argmax(dim=1)
if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase_)):
patient_counter += 1
else:
lowercase_ = 0
lowercase_ = logits
if patient_counter == self.patience:
break
lowercase_ = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. " , __UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
def __init__( self : Optional[Any] , lowerCAmelCase_ : str):
"""simple docstring"""
super().__init__(lowerCAmelCase_)
lowercase_ = config.num_labels
lowercase_ = BertModelWithPabee(lowerCAmelCase_)
lowercase_ = nn.Dropout(config.hidden_dropout_prob)
lowercase_ = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels) for _ in range(config.num_hidden_layers)])
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : List[str]=None , ):
"""simple docstring"""
lowercase_ = self.bert(
input_ids=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , position_ids=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , inputs_embeds=lowerCAmelCase_ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
lowercase_ = (logits[-1],)
if labels is not None:
lowercase_ = None
lowercase_ = 0
for ix, logits_item in enumerate(lowerCAmelCase_):
if self.num_labels == 1:
# We are doing regression
lowercase_ = MSELoss()
lowercase_ = loss_fct(logits_item.view(-1) , labels.view(-1))
else:
lowercase_ = CrossEntropyLoss()
lowercase_ = loss_fct(logits_item.view(-1 , self.num_labels) , labels.view(-1))
if total_loss is None:
lowercase_ = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
lowercase_ = (total_loss / total_weights,) + outputs
return outputs
| 362
|
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , lowerCAmelCase_ : int = 6):
"""simple docstring"""
lowercase_ = None
lowercase_ = None
self.create_linked_list(lowerCAmelCase_)
def _UpperCAmelCase ( self : List[str] , lowerCAmelCase_ : int):
"""simple docstring"""
lowercase_ = Node()
lowercase_ = current_node
lowercase_ = current_node
lowercase_ = current_node
for _ in range(1 , lowerCAmelCase_):
lowercase_ = Node()
lowercase_ = current_node
lowercase_ = previous_node
lowercase_ = current_node
lowercase_ = self.front
lowercase_ = previous_node
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
self.check_can_perform_operation()
return self.front.data if self.front else None
def _UpperCAmelCase ( self : int , lowerCAmelCase_ : Any):
"""simple docstring"""
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
lowercase_ = self.rear.next
if self.rear:
lowercase_ = data
def _UpperCAmelCase ( self : str):
"""simple docstring"""
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
lowercase_ = self.front.data
lowercase_ = None
return data
lowercase_ = self.front
lowercase_ = old_front.next
lowercase_ = old_front.data
lowercase_ = None
return data
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
if self.is_empty():
raise Exception("""Empty Queue""")
def _UpperCAmelCase ( self : Tuple):
"""simple docstring"""
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""")
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str]):
"""simple docstring"""
lowercase_ = None
lowercase_ = None
lowercase_ = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313
| 0
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class A :
# setable values
lowerCamelCase : Optional[int] = None
lowerCamelCase : Optional[jnp.ndarray] = None
lowerCamelCase : Optional[jnp.ndarray] = None # sigma(t_i)
@classmethod
def A__ ( cls ) -> List[str]:
'''simple docstring'''
return cls()
@dataclass
class A ( __UpperCAmelCase ):
lowerCamelCase : jnp.ndarray
lowerCamelCase : jnp.ndarray
lowerCamelCase : KarrasVeSchedulerState
class A ( __UpperCAmelCase , __UpperCAmelCase ):
@property
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
return True
@register_to_config
def __init__( self , lowerCamelCase__ = 0.02 , lowerCamelCase__ = 100 , lowerCamelCase__ = 1.0_07 , lowerCamelCase__ = 80 , lowerCamelCase__ = 0.05 , lowerCamelCase__ = 50 , ) -> Optional[Any]:
'''simple docstring'''
pass
def A__ ( self ) -> str:
'''simple docstring'''
return KarrasVeSchedulerState.create()
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = () ) -> KarrasVeSchedulerState:
'''simple docstring'''
lowercase__ = jnp.arange(0 , lowerCamelCase__ )[::-1].copy()
lowercase__ = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=lowerCamelCase__ , schedule=jnp.array(lowerCamelCase__ , dtype=jnp.floataa ) , timesteps=lowerCamelCase__ , )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> Tuple[jnp.ndarray, float]:
'''simple docstring'''
if self.config.s_min <= sigma <= self.config.s_max:
lowercase__ = min(self.config.s_churn / state.num_inference_steps , 2**0.5 - 1 )
else:
lowercase__ = 0
# sample eps ~ N(0, S_noise^2 * I)
lowercase__ = random.split(lowerCamelCase__ , num=1 )
lowercase__ = self.config.s_noise * random.normal(key=lowerCamelCase__ , shape=sample.shape )
lowercase__ = sigma + gamma * sigma
lowercase__ = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = True , ) -> Union[FlaxKarrasVeOutput, Tuple]:
'''simple docstring'''
lowercase__ = sample_hat + sigma_hat * model_output
lowercase__ = (sample_hat - pred_original_sample) / sigma_hat
lowercase__ = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=lowerCamelCase__ , derivative=lowerCamelCase__ , state=lowerCamelCase__ )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = True , ) -> Union[FlaxKarrasVeOutput, Tuple]:
'''simple docstring'''
lowercase__ = sample_prev + sigma_prev * model_output
lowercase__ = (sample_prev - pred_original_sample) / sigma_prev
lowercase__ = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=lowerCamelCase__ , derivative=lowerCamelCase__ , state=lowerCamelCase__ )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
raise NotImplementedError()
| 164
|
'''simple docstring'''
def _A ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
__A = generate_large_matrix()
__A = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def _A ( lowercase__ ):
assert all(row == sorted(lowercase__ , reverse=lowercase__ ) for row in grid )
assert all(list(lowercase__ ) == sorted(lowercase__ , reverse=lowercase__ ) for col in zip(*lowercase__ ) )
def _A ( lowercase__ ):
lowercase__ = 0
lowercase__ = len(lowercase__ ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
lowercase__ = (left + right) // 2
lowercase__ = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
lowercase__ = mid + 1
else:
lowercase__ = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(lowercase__ )
def _A ( lowercase__ ):
lowercase__ = 0
lowercase__ = len(grid[0] )
for i in range(len(lowercase__ ) ):
lowercase__ = find_negative_index(grid[i][:bound] )
total += bound
return (len(lowercase__ ) * len(grid[0] )) - total
def _A ( lowercase__ ):
return len([number for row in grid for number in row if number < 0] )
def _A ( lowercase__ ):
lowercase__ = 0
for row in grid:
for i, number in enumerate(lowercase__ ):
if number < 0:
total += len(lowercase__ ) - i
break
return total
def _A ( ):
from timeit import timeit
print("""Running benchmarks""" )
lowercase__ = (
"""from __main__ import count_negatives_binary_search, """
"""count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
lowercase__ = timeit(f'''{func}(grid=grid)''' , setup=lowercase__ , number=500 )
print(f'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 164
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase = {
"""configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
"""tokenization_canine""": ["""CanineTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
"""CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CanineForMultipleChoice""",
"""CanineForQuestionAnswering""",
"""CanineForSequenceClassification""",
"""CanineForTokenClassification""",
"""CanineLayer""",
"""CanineModel""",
"""CaninePreTrainedModel""",
"""load_tf_weights_in_canine""",
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 369
|
'''simple docstring'''
from __future__ import annotations
_lowerCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a__ ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] , ) -> tuple[list[list[int]], list[list[int]]]:
"""simple docstring"""
UpperCAmelCase_ : int = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_SCREAMING_SNAKE_CASE ) )
] # the reference grid
UpperCAmelCase_ : Tuple = 1
UpperCAmelCase_ : str = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_SCREAMING_SNAKE_CASE ) )
] # the action grid
UpperCAmelCase_ : Tuple = init[0]
UpperCAmelCase_ : List[Any] = init[1]
UpperCAmelCase_ : Optional[Any] = 0
UpperCAmelCase_ : Optional[Any] = g + heuristic[x][y] # cost from starting cell to destination cell
UpperCAmelCase_ : List[str] = [[f, g, x, y]]
UpperCAmelCase_ : Tuple = False # flag that is set when search is complete
UpperCAmelCase_ : Union[str, Any] = False # flag set if we can't find expand
while not found and not resign:
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("Algorithm is unable to find solution" )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
UpperCAmelCase_ : Dict = cell.pop()
UpperCAmelCase_ : Tuple = next_cell[2]
UpperCAmelCase_ : str = next_cell[3]
UpperCAmelCase_ : List[Any] = next_cell[1]
if x == goal[0] and y == goal[1]:
UpperCAmelCase_ : Optional[Any] = True
else:
for i in range(len(_SCREAMING_SNAKE_CASE ) ): # to try out different valid actions
UpperCAmelCase_ : Union[str, Any] = x + DIRECTIONS[i][0]
UpperCAmelCase_ : Optional[Any] = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(_SCREAMING_SNAKE_CASE ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
UpperCAmelCase_ : Any = g + cost
UpperCAmelCase_ : Optional[Any] = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
UpperCAmelCase_ : List[Any] = 1
UpperCAmelCase_ : List[str] = i
UpperCAmelCase_ : Union[str, Any] = []
UpperCAmelCase_ : List[Any] = goal[0]
UpperCAmelCase_ : str = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
UpperCAmelCase_ : Optional[int] = x - DIRECTIONS[action[x][y]][0]
UpperCAmelCase_ : Optional[int] = y - DIRECTIONS[action[x][y]][1]
UpperCAmelCase_ : Optional[Any] = xa
UpperCAmelCase_ : List[str] = ya
invpath.append([x, y] )
UpperCAmelCase_ : Tuple = []
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
path.append(invpath[len(_SCREAMING_SNAKE_CASE ) - 1 - i] )
return path, action
if __name__ == "__main__":
_lowerCamelCase = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
_lowerCamelCase = [0, 0]
# all coordinates are given in format [y,x]
_lowerCamelCase = [len(grid) - 1, len(grid[0]) - 1]
_lowerCamelCase = 1
# the cost map which pushes the path closer to the goal
_lowerCamelCase = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
_lowerCamelCase = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
_lowerCamelCase = 99
_lowerCamelCase , _lowerCamelCase = search(grid, init, goal, cost, heuristic)
print("""ACTION MAP""")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 67
| 0
|
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