code
stringlengths 86
54.5k
| code_codestyle
int64 0
371
| style_context
stringlengths 87
49.2k
| style_context_codestyle
int64 0
349
| label
int64 0
1
|
|---|---|---|---|---|
def snake_case_ ( snake_case , snake_case ) -> str:
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
lowercase__: str = str(bin(snake_case ) )
binary_number += "0" * shift_amount
return binary_number
def snake_case_ ( snake_case , snake_case ) -> str:
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
lowercase__: Optional[Any] = str(bin(snake_case ) )[2:]
if shift_amount >= len(snake_case ):
return "0b0"
lowercase__: Optional[int] = binary_number[: len(snake_case ) - shift_amount]
return "0b" + shifted_binary_number
def snake_case_ ( snake_case , snake_case ) -> str:
if number >= 0: # Get binary representation of positive number
lowercase__: Union[str, Any] = '0' + str(bin(snake_case ) ).strip('-' )[2:]
else: # Get binary (2's complement) representation of negative number
lowercase__: Dict = len(bin(snake_case )[3:] ) # Find 2's complement of number
lowercase__: int = bin(abs(snake_case ) - (1 << binary_number_length) )[3:]
lowercase__: Any = (
'1' + '0' * (binary_number_length - len(snake_case )) + binary_number
)
if shift_amount >= len(snake_case ):
return "0b" + binary_number[0] * len(snake_case )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(snake_case ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 196
|
__lowerCAmelCase = range(2, 20 + 1)
__lowerCAmelCase = [10**k for k in range(ks[-1] + 1)]
__lowerCAmelCase = {}
def snake_case_ ( snake_case , snake_case , snake_case , snake_case ) -> Optional[int]:
lowercase__: str = sum(a_i[j] for j in range(snake_case , len(snake_case ) ) )
lowercase__: Optional[int] = sum(a_i[j] * base[j] for j in range(min(len(snake_case ) , snake_case ) ) )
lowercase__ , lowercase__: str = 0, 0
lowercase__: Tuple = n - i
lowercase__: Dict = memo.get(snake_case )
if sub_memo is not None:
lowercase__: Optional[Any] = sub_memo.get(snake_case )
if jumps is not None and len(snake_case ) > 0:
# find and make the largest jump without going over
lowercase__: int = -1
for _k in range(len(snake_case ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
lowercase__: Union[str, Any] = _k
break
if max_jump >= 0:
lowercase__ , lowercase__ , lowercase__: Any = jumps[max_jump]
# since the difference between jumps is cached, add c
lowercase__: str = diff + c
for j in range(min(snake_case , len(snake_case ) ) ):
lowercase__ , lowercase__: Dict = divmod(snake_case , 10 )
if new_c > 0:
add(snake_case , snake_case , snake_case )
else:
lowercase__: List[Any] = []
else:
lowercase__: Optional[Any] = {c: []}
lowercase__: Union[str, Any] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
lowercase__ , lowercase__: Union[str, Any] = next_term(snake_case , k - 1 , i + dn , snake_case )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
lowercase__ , lowercase__: Dict = compute(snake_case , snake_case , i + dn , snake_case )
diff += _diff
dn += terms_jumped
lowercase__: Any = sub_memo[c]
# keep jumps sorted by # of terms skipped
lowercase__: str = 0
while j < len(snake_case ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(snake_case , (diff, dn, k) )
return (diff, dn)
def snake_case_ ( snake_case , snake_case , snake_case , snake_case ) -> str:
if i >= n:
return 0, i
if k > len(snake_case ):
a_i.extend([0 for _ in range(k - len(snake_case ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
lowercase__: List[Any] = i
lowercase__ , lowercase__ , lowercase__: Any = 0, 0, 0
for j in range(len(snake_case ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
lowercase__: str = ds_c + ds_b
diff += addend
lowercase__: List[str] = 0
for j in range(snake_case ):
lowercase__: Any = a_i[j] + addend
lowercase__ , lowercase__: List[Any] = divmod(snake_case , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(snake_case , snake_case , snake_case )
return diff, i - start_i
def snake_case_ ( snake_case , snake_case , snake_case ) -> int:
for j in range(snake_case , len(snake_case ) ):
lowercase__: str = digits[j] + addend
if s >= 10:
lowercase__ , lowercase__: Any = divmod(snake_case , 10 )
lowercase__: Any = addend // 10 + quotient
else:
lowercase__: Union[str, Any] = s
lowercase__: Union[str, Any] = addend // 10
if addend == 0:
break
while addend > 0:
lowercase__ , lowercase__: Union[str, Any] = divmod(snake_case , 10 )
digits.append(snake_case )
def snake_case_ ( snake_case = 10**15 ) -> int:
lowercase__: Optional[Any] = [1]
lowercase__: int = 1
lowercase__: Tuple = 0
while True:
lowercase__ , lowercase__: str = next_term(snake_case , 20 , i + dn , snake_case )
dn += terms_jumped
if dn == n - i:
break
lowercase__: Dict = 0
for j in range(len(snake_case ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F'''{solution() = }''')
| 196
| 1
|
"""simple docstring"""
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __lowerCamelCase ( TensorFormatter[Mapping, """torch.Tensor""", Mapping] ):
'''simple docstring'''
def __init__( self : int , a_ : Optional[Any]=None , **a_ : Dict ):
super().__init__(features=_UpperCAmelCase )
lowerCAmelCase_ : str = torch_tensor_kwargs
import torch # noqa import torch at initialization
def lowerCamelCase ( self : Any , a_ : Tuple ):
import torch
if isinstance(_UpperCAmelCase , _UpperCAmelCase ) and column:
if all(
isinstance(_UpperCAmelCase , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(_UpperCAmelCase )
return column
def lowerCamelCase ( self : List[str] , a_ : str ):
import torch
if isinstance(_UpperCAmelCase , (str, bytes, type(_UpperCAmelCase )) ):
return value
elif isinstance(_UpperCAmelCase , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
lowerCAmelCase_ : Tuple = {}
if isinstance(_UpperCAmelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
lowerCAmelCase_ : Union[str, Any] = {'dtype': torch.intaa}
elif isinstance(_UpperCAmelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
lowerCAmelCase_ : str = {'dtype': torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(_UpperCAmelCase , PIL.Image.Image ):
lowerCAmelCase_ : str = np.asarray(_UpperCAmelCase )
return torch.tensor(_UpperCAmelCase , **{**default_dtype, **self.torch_tensor_kwargs} )
def lowerCamelCase ( self : Union[str, Any] , a_ : Union[str, Any] ):
import torch
# support for torch, tf, jax etc.
if hasattr(_UpperCAmelCase , "__array__" ) and not isinstance(_UpperCAmelCase , torch.Tensor ):
lowerCAmelCase_ : Dict = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(_UpperCAmelCase , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(_UpperCAmelCase ) for substruct in data_struct] )
elif isinstance(_UpperCAmelCase , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(_UpperCAmelCase ) for substruct in data_struct] )
return self._tensorize(_UpperCAmelCase )
def lowerCamelCase ( self : Optional[int] , a_ : dict ):
return map_nested(self._recursive_tensorize , _UpperCAmelCase , map_list=_UpperCAmelCase )
def lowerCamelCase ( self : List[str] , a_ : pa.Table ):
lowerCAmelCase_ : Dict = self.numpy_arrow_extractor().extract_row(_UpperCAmelCase )
lowerCAmelCase_ : Dict = self.python_features_decoder.decode_row(_UpperCAmelCase )
return self.recursive_tensorize(_UpperCAmelCase )
def lowerCamelCase ( self : Optional[int] , a_ : pa.Table ):
lowerCAmelCase_ : int = self.numpy_arrow_extractor().extract_column(_UpperCAmelCase )
lowerCAmelCase_ : Optional[int] = self.python_features_decoder.decode_column(_UpperCAmelCase , pa_table.column_names[0] )
lowerCAmelCase_ : str = self.recursive_tensorize(_UpperCAmelCase )
lowerCAmelCase_ : int = self._consolidate(_UpperCAmelCase )
return column
def lowerCamelCase ( self : str , a_ : pa.Table ):
lowerCAmelCase_ : Optional[Any] = self.numpy_arrow_extractor().extract_batch(_UpperCAmelCase )
lowerCAmelCase_ : List[str] = self.python_features_decoder.decode_batch(_UpperCAmelCase )
lowerCAmelCase_ : List[str] = self.recursive_tensorize(_UpperCAmelCase )
for column_name in batch:
lowerCAmelCase_ : Union[str, Any] = self._consolidate(batch[column_name] )
return batch
| 369
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
),
}
class __lowerCamelCase ( A__ ):
'''simple docstring'''
a_ : List[Any] = """lilt"""
def __init__( self : Any , a_ : List[str]=3_05_22 , a_ : List[Any]=7_68 , a_ : Tuple=12 , a_ : Tuple=12 , a_ : str=30_72 , a_ : Union[str, Any]="gelu" , a_ : Union[str, Any]=0.1 , a_ : List[Any]=0.1 , a_ : List[Any]=5_12 , a_ : List[str]=2 , a_ : int=0.02 , a_ : Optional[int]=1e-1_2 , a_ : Any=0 , a_ : str="absolute" , a_ : List[Any]=None , a_ : Optional[int]=4 , a_ : str=10_24 , **a_ : Union[str, Any] , ):
super().__init__(pad_token_id=a_ , **a_ )
lowerCAmelCase_ : List[str] = vocab_size
lowerCAmelCase_ : List[str] = hidden_size
lowerCAmelCase_ : int = num_hidden_layers
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : str = hidden_act
lowerCAmelCase_ : str = intermediate_size
lowerCAmelCase_ : List[str] = hidden_dropout_prob
lowerCAmelCase_ : Any = attention_probs_dropout_prob
lowerCAmelCase_ : int = max_position_embeddings
lowerCAmelCase_ : Any = type_vocab_size
lowerCAmelCase_ : List[Any] = initializer_range
lowerCAmelCase_ : str = layer_norm_eps
lowerCAmelCase_ : Tuple = position_embedding_type
lowerCAmelCase_ : Union[str, Any] = classifier_dropout
lowerCAmelCase_ : Optional[Any] = channel_shrink_ratio
lowerCAmelCase_ : Dict = max_ad_position_embeddings
| 161
| 0
|
"""simple docstring"""
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class UpperCamelCase ( lowerCAmelCase__ , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ = BertJapaneseTokenizer
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = True
def a_ ( self) -> str:
super().setUp()
snake_case_ = [
'[UNK]',
'[CLS]',
'[SEP]',
'こんにちは',
'こん',
'にちは',
'ばんは',
'##こん',
'##にちは',
'##ばんは',
'世界',
'##世界',
'、',
'##、',
'。',
'##。',
]
snake_case_ = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
with open(self.vocab_file, 'w', encoding='utf-8') as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
def a_ ( self, lowerCAmelCase__) -> Union[str, Any]:
snake_case_ = 'こんにちは、世界。 \nこんばんは、世界。'
snake_case_ = 'こんにちは 、 世界 。 こんばんは 、 世界 。'
return input_text, output_text
def a_ ( self, lowerCAmelCase__) -> Optional[Any]:
snake_case_ , snake_case_ = self.get_input_output_texts(lowerCAmelCase__)
snake_case_ = tokenizer.encode(lowerCAmelCase__, add_special_tokens=lowerCAmelCase__)
snake_case_ = tokenizer.decode(lowerCAmelCase__, clean_up_tokenization_spaces=lowerCAmelCase__)
return text, ids
def a_ ( self) -> Dict:
pass # TODO add if relevant
def a_ ( self) -> Optional[Any]:
pass # TODO add if relevant
def a_ ( self) -> Dict:
pass # TODO add if relevant
def a_ ( self) -> Union[str, Any]:
snake_case_ = self.tokenizer_class(self.vocab_file)
snake_case_ = tokenizer.tokenize('こんにちは、世界。\nこんばんは、世界。')
self.assertListEqual(lowerCAmelCase__, ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'])
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__), [3, 12, 10, 14, 4, 9, 12, 10, 14])
def a_ ( self) -> str:
snake_case_ = self.tokenizer_class(self.vocab_file, word_tokenizer_type='mecab')
self.assertIsNotNone(lowerCAmelCase__)
snake_case_ = 'こんにちは、世界。\nこんばんは、世界。'
snake_case_ = tokenizer.tokenize(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__, ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'])
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__), [3, 12, 10, 14, 4, 9, 12, 10, 14])
snake_case_ = os.path.join(self.tmpdirname, 'tokenizer.bin')
with open(lowerCAmelCase__, 'wb') as handle:
pickle.dump(lowerCAmelCase__, lowerCAmelCase__)
with open(lowerCAmelCase__, 'rb') as handle:
snake_case_ = pickle.load(lowerCAmelCase__)
snake_case_ = tokenizer_new.tokenize(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__, lowerCAmelCase__)
def a_ ( self) -> Dict:
snake_case_ = MecabTokenizer(mecab_dic='ipadic')
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), ['アップルストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'], )
def a_ ( self) -> Any:
try:
snake_case_ = MecabTokenizer(mecab_dic='unidic_lite')
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'], )
def a_ ( self) -> List[str]:
try:
snake_case_ = MecabTokenizer(mecab_dic='unidic')
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'], )
def a_ ( self) -> Any:
snake_case_ = MecabTokenizer(do_lower_case=lowerCAmelCase__, mecab_dic='ipadic')
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), ['アップルストア', 'で', 'iphone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'], )
def a_ ( self) -> List[str]:
try:
snake_case_ = MecabTokenizer(
do_lower_case=lowerCAmelCase__, normalize_text=lowerCAmelCase__, mecab_option='-d /usr/local/lib/mecab/dic/jumandic')
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れた', '\u3000', '。'], )
def a_ ( self) -> Optional[Any]:
snake_case_ = MecabTokenizer(normalize_text=lowerCAmelCase__, mecab_dic='ipadic')
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れ', 'た', ' ', '。'], )
@require_sudachi
def a_ ( self) -> Optional[Any]:
snake_case_ = self.tokenizer_class(self.vocab_file, word_tokenizer_type='sudachi')
self.assertIsNotNone(lowerCAmelCase__)
snake_case_ = 'こんにちは、世界。\nこんばんは、世界。'
snake_case_ = tokenizer.tokenize(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__, ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'])
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__), [3, 12, 10, 14, 4, 9, 12, 10, 14])
snake_case_ = os.path.join(self.tmpdirname, 'tokenizer.bin')
with open(lowerCAmelCase__, 'wb') as handle:
pickle.dump(lowerCAmelCase__, lowerCAmelCase__)
with open(lowerCAmelCase__, 'rb') as handle:
snake_case_ = pickle.load(lowerCAmelCase__)
snake_case_ = tokenizer_new.tokenize(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__, lowerCAmelCase__)
@require_sudachi
def a_ ( self) -> Optional[int]:
snake_case_ = SudachiTokenizer(sudachi_dict_type='core')
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), [' ', '\t', 'アップル', 'ストア', 'で', 'iPhone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' '], )
@require_sudachi
def a_ ( self) -> List[Any]:
snake_case_ = SudachiTokenizer(sudachi_dict_type='core', sudachi_split_mode='A')
self.assertListEqual(tokenizer.tokenize('外国人参政権'), ['外国', '人', '参政', '権'])
@require_sudachi
def a_ ( self) -> int:
snake_case_ = SudachiTokenizer(sudachi_dict_type='core', sudachi_split_mode='B')
self.assertListEqual(tokenizer.tokenize('外国人参政権'), ['外国人', '参政権'])
@require_sudachi
def a_ ( self) -> Dict:
snake_case_ = SudachiTokenizer(sudachi_dict_type='core', sudachi_split_mode='C')
self.assertListEqual(tokenizer.tokenize('外国人参政権'), ['外国人参政権'])
@require_sudachi
def a_ ( self) -> Any:
snake_case_ = SudachiTokenizer(do_lower_case=lowerCAmelCase__, sudachi_dict_type='core')
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), [' ', '\t', 'アップル', 'ストア', 'で', 'iphone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' '], )
@require_sudachi
def a_ ( self) -> Dict:
snake_case_ = SudachiTokenizer(normalize_text=lowerCAmelCase__, sudachi_dict_type='core')
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), [' ', '\t', 'ｱｯﾌﾟﾙ', 'ストア', 'で', 'iPhone', '８', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', '\u3000', '。', ' ', ' '], )
@require_sudachi
def a_ ( self) -> Optional[int]:
snake_case_ = SudachiTokenizer(trim_whitespace=lowerCAmelCase__, sudachi_dict_type='core')
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'], )
@require_jumanpp
def a_ ( self) -> Optional[int]:
snake_case_ = self.tokenizer_class(self.vocab_file, word_tokenizer_type='jumanpp')
self.assertIsNotNone(lowerCAmelCase__)
snake_case_ = 'こんにちは、世界。\nこんばんは、世界。'
snake_case_ = tokenizer.tokenize(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__, ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'])
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__), [3, 12, 10, 14, 4, 9, 12, 10, 14])
snake_case_ = os.path.join(self.tmpdirname, 'tokenizer.bin')
with open(lowerCAmelCase__, 'wb') as handle:
pickle.dump(lowerCAmelCase__, lowerCAmelCase__)
with open(lowerCAmelCase__, 'rb') as handle:
snake_case_ = pickle.load(lowerCAmelCase__)
snake_case_ = tokenizer_new.tokenize(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__, lowerCAmelCase__)
@require_jumanpp
def a_ ( self) -> List[Any]:
snake_case_ = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), ['アップル', 'ストア', 'で', 'iPhone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'], )
@require_jumanpp
def a_ ( self) -> List[Any]:
snake_case_ = JumanppTokenizer(do_lower_case=lowerCAmelCase__)
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), ['アップル', 'ストア', 'で', 'iphone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'], )
@require_jumanpp
def a_ ( self) -> List[str]:
snake_case_ = JumanppTokenizer(normalize_text=lowerCAmelCase__)
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), ['ｱ', 'ｯ', 'ﾌ', 'ﾟ', 'ﾙ', 'ストア', 'で', 'iPhone', '８', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'], )
@require_jumanpp
def a_ ( self) -> Optional[int]:
snake_case_ = JumanppTokenizer(trim_whitespace=lowerCAmelCase__)
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 '), ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れた', '。'], )
@require_jumanpp
def a_ ( self) -> Tuple:
snake_case_ = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize('ありがとうございますm(_ _)ｍ見つけるのが大変です。'), ['ありがとう', 'ございます', 'm(_ _)m', '見つける', 'の', 'が', '大変です', '。'], )
def a_ ( self) -> Optional[int]:
snake_case_ = ['[UNK]', '[CLS]', '[SEP]', 'こんにちは', 'こん', 'にちは', 'ばんは', '##こん', '##にちは', '##ばんは']
snake_case_ = {}
for i, token in enumerate(lowerCAmelCase__):
snake_case_ = i
snake_case_ = WordpieceTokenizer(vocab=lowerCAmelCase__, unk_token='[UNK]')
self.assertListEqual(tokenizer.tokenize(''), [])
self.assertListEqual(tokenizer.tokenize('こんにちは'), ['こんにちは'])
self.assertListEqual(tokenizer.tokenize('こんばんは'), ['こん', '##ばんは'])
self.assertListEqual(tokenizer.tokenize('こんばんは こんばんにちは こんにちは'), ['こん', '##ばんは', '[UNK]', 'こんにちは'])
def a_ ( self) -> List[str]:
snake_case_ = BertJapaneseTokenizer.from_pretrained('nlp-waseda/roberta-base-japanese-with-auto-jumanpp')
snake_case_ = tokenizer.subword_tokenizer
snake_case_ = subword_tokenizer.tokenize('国境 の 長い トンネル を 抜ける と 雪国 であった 。')
self.assertListEqual(lowerCAmelCase__, ['▁国境', '▁の', '▁長い', '▁トンネル', '▁を', '▁抜ける', '▁と', '▁雪', '国', '▁であった', '▁。'])
snake_case_ = subword_tokenizer.tokenize('こんばんは こんばん にち は こんにちは')
self.assertListEqual(lowerCAmelCase__, ['▁こん', 'ばん', 'は', '▁こん', 'ばん', '▁に', 'ち', '▁は', '▁こんにちは'])
def a_ ( self) -> Optional[Any]:
snake_case_ = self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese')
snake_case_ = tokenizer.encode('ありがとう。', add_special_tokens=lowerCAmelCase__)
snake_case_ = tokenizer.encode('どういたしまして。', add_special_tokens=lowerCAmelCase__)
snake_case_ = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__)
snake_case_ = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__, lowerCAmelCase__)
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class UpperCamelCase ( lowerCAmelCase__ , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ = BertJapaneseTokenizer
SCREAMING_SNAKE_CASE_ = False
def a_ ( self) -> Union[str, Any]:
super().setUp()
snake_case_ = ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']
snake_case_ = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
with open(self.vocab_file, 'w', encoding='utf-8') as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
def a_ ( self, **lowerCAmelCase__) -> Dict:
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname, subword_tokenizer_type='character', **lowerCAmelCase__)
def a_ ( self, lowerCAmelCase__) -> List[Any]:
snake_case_ = 'こんにちは、世界。 \nこんばんは、世界。'
snake_case_ = 'こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。'
return input_text, output_text
def a_ ( self) -> Union[str, Any]:
pass # TODO add if relevant
def a_ ( self) -> List[str]:
pass # TODO add if relevant
def a_ ( self) -> Dict:
pass # TODO add if relevant
def a_ ( self) -> Dict:
snake_case_ = self.tokenizer_class(self.vocab_file, subword_tokenizer_type='character')
snake_case_ = tokenizer.tokenize('こんにちは、世界。 \nこんばんは、世界。')
self.assertListEqual(
lowerCAmelCase__, ['こ', 'ん', 'に', 'ち', 'は', '、', '世', '界', '。', 'こ', 'ん', 'ば', 'ん', 'は', '、', '世', '界', '。'])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase__), [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12])
def a_ ( self) -> Any:
snake_case_ = ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']
snake_case_ = {}
for i, token in enumerate(lowerCAmelCase__):
snake_case_ = i
snake_case_ = CharacterTokenizer(vocab=lowerCAmelCase__, unk_token='[UNK]')
self.assertListEqual(tokenizer.tokenize(''), [])
self.assertListEqual(tokenizer.tokenize('こんにちは'), ['こ', 'ん', 'に', 'ち', 'は'])
self.assertListEqual(tokenizer.tokenize('こんにちほ'), ['こ', 'ん', 'に', 'ち', '[UNK]'])
def a_ ( self) -> str:
snake_case_ = self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese-char')
snake_case_ = tokenizer.encode('ありがとう。', add_special_tokens=lowerCAmelCase__)
snake_case_ = tokenizer.encode('どういたしまして。', add_special_tokens=lowerCAmelCase__)
snake_case_ = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__)
snake_case_ = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__, lowerCAmelCase__)
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class UpperCamelCase ( unittest.TestCase ):
def a_ ( self) -> str:
snake_case_ = 'cl-tohoku/bert-base-japanese'
snake_case_ = AutoTokenizer.from_pretrained(lowerCAmelCase__)
self.assertIsInstance(lowerCAmelCase__, lowerCAmelCase__)
class UpperCamelCase ( unittest.TestCase ):
def a_ ( self) -> List[Any]:
snake_case_ = 'cl-tohoku/bert-base-japanese'
with self.assertLogs('transformers', level='WARNING') as cm:
BertTokenizer.from_pretrained(lowerCAmelCase__)
self.assertTrue(
cm.records[0].message.startswith(
'The tokenizer class you load from this checkpoint is not the same type as the class this function'
' is called from.'))
snake_case_ = 'bert-base-cased'
with self.assertLogs('transformers', level='WARNING') as cm:
BertJapaneseTokenizer.from_pretrained(lowerCAmelCase__)
self.assertTrue(
cm.records[0].message.startswith(
'The tokenizer class you load from this checkpoint is not the same type as the class this function'
' is called from.'))
| 69
|
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
_SCREAMING_SNAKE_CASE = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'''{bindir}/../../examples/pytorch/translation'''):
from run_translation import main # noqa
set_seed(42)
_SCREAMING_SNAKE_CASE = 'sshleifer/student_marian_en_ro_6_1'
_SCREAMING_SNAKE_CASE = 'sshleifer/tiny-mbart'
@require_torch
class a ( __lowerCAmelCase ):
"""simple docstring"""
def UpperCAmelCase ( self , lowerCAmelCase_=False , lowerCAmelCase_=None , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=True , ) -> int:
_A = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=lowerCAmelCase_ , num_train_epochs=1 , distributed=lowerCAmelCase_ , extra_args_str=lowerCAmelCase_ , predict_with_generate=lowerCAmelCase_ , do_train=lowerCAmelCase_ , do_eval=lowerCAmelCase_ , do_predict=lowerCAmelCase_ , )
_A = TrainerState.load_from_json(os.path.join(lowerCAmelCase_ , """trainer_state.json""" ) ).log_history
if not do_eval:
return
_A = [log for log in logs if """eval_loss""" in log.keys()]
_A = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
_A = eval_metrics[-1]
assert isinstance(last_step_stats["""eval_bleu"""] , lowerCAmelCase_ )
assert not math.isnan(float(last_step_stats["""eval_loss"""] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def UpperCAmelCase ( self ) -> Optional[int]:
self.run_seqaseq_quick()
@require_torch_multi_gpu
def UpperCAmelCase ( self ) -> Dict:
self.run_seqaseq_quick(distributed=lowerCAmelCase_ )
@require_torch_multi_gpu
def UpperCAmelCase ( self ) -> Dict:
self.run_seqaseq_quick(distributed=lowerCAmelCase_ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCAmelCase ( self ) -> str:
self.run_seqaseq_quick(distributed=lowerCAmelCase_ , extra_args_str="""--sharded_ddp simple""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCAmelCase ( self ) -> Dict:
self.run_seqaseq_quick(distributed=lowerCAmelCase_ , extra_args_str="""--sharded_ddp simple --fp16""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCAmelCase ( self ) -> Optional[Any]:
self.run_seqaseq_quick(distributed=lowerCAmelCase_ , extra_args_str="""--sharded_ddp zero_dp_2""" , predict_with_generate=lowerCAmelCase_ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCAmelCase ( self ) -> Tuple:
self.run_seqaseq_quick(
distributed=lowerCAmelCase_ , extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" , predict_with_generate=lowerCAmelCase_ )
@require_apex
@require_torch_gpu
def UpperCAmelCase ( self ) -> int:
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=lowerCAmelCase_ , extra_args_str="""--fp16 --fp16_backend=apex""" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=lowerCAmelCase_ , extra_args_str="""--fp16 --fp16_backend=apex""" )
@parameterized.expand(["""base""", """low""", """high""", """mixed"""] )
@require_torch_multi_gpu
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> int:
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
_A = {
# test with the default log_level - should be info and thus log info once
"""base""": {"""extra_args_str""": """""", """n_matches""": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"""low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"""high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"""mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
}
_A = experiments[experiment_id]
_A = {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
_A = """Running training"""
with CaptureStderr() as cl:
self.run_seqaseq_quick(**lowerCAmelCase_ , extra_args_str=data["""extra_args_str"""] )
_A = len(re.findall(lowerCAmelCase_ , cl.err ) )
self.assertEqual(lowerCAmelCase_ , data["""n_matches"""] )
@slow
def UpperCAmelCase ( self ) -> Dict:
_A = self.run_trainer(
eval_steps=2 , max_len=1_28 , model_name=lowerCAmelCase_ , learning_rate=3E-4 , num_train_epochs=10 , distributed=lowerCAmelCase_ , )
# Check metrics
_A = TrainerState.load_from_json(os.path.join(lowerCAmelCase_ , """trainer_state.json""" ) ).log_history
_A = [log for log in logs if """eval_loss""" in log.keys()]
_A = eval_metrics[0]
_A = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["""eval_bleu"""] , lowerCAmelCase_ )
# test if do_predict saves generations and metrics
_A = os.listdir(lowerCAmelCase_ )
_A = {os.path.basename(lowerCAmelCase_ ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def UpperCAmelCase ( self ) -> Optional[Any]:
from transformers.training_args import OptimizerNames
def train_and_return_metrics(lowerCAmelCase_ ) -> Tuple[int, float]:
_A = """--skip_memory_metrics 0"""
_A = self.run_trainer(
max_len=1_28 , model_name=lowerCAmelCase_ , learning_rate=3E-4 , num_train_epochs=1 , optim=lowerCAmelCase_ , distributed=lowerCAmelCase_ , extra_args_str=lowerCAmelCase_ , do_eval=lowerCAmelCase_ , do_predict=lowerCAmelCase_ , n_gpus_to_use=1 , )
# Check metrics
_A = TrainerState.load_from_json(Path(lowerCAmelCase_ , """trainer_state.json""" ) ).log_history
_A = int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20 )
_A = int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20 )
_A = logs[0]["""train_loss"""]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
_A , _A , _A = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
_A , _A , _A = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
_A = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
_A = gpu_peak_mem_orig + gpu_alloc_mem_orig
_A = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
_A = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
_A = 1_20
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
lowerCAmelCase_ , lowerCAmelCase_ , """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB''' , )
self.assertGreater(
lowerCAmelCase_ , lowerCAmelCase_ , """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB''' , )
self.assertEqual(
lowerCAmelCase_ , lowerCAmelCase_ , F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}''' )
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 3E-3 , lowerCAmelCase_ = "adafactor" , lowerCAmelCase_ = False , lowerCAmelCase_ = None , lowerCAmelCase_ = 0 , lowerCAmelCase_ = True , lowerCAmelCase_ = True , lowerCAmelCase_ = True , lowerCAmelCase_ = True , lowerCAmelCase_ = None , ) -> str:
_A = self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
_A = self.get_auto_remove_tmp_dir()
_A = F'''
--model_name_or_path {model_name}
--train_file {data_dir}/train.json
--validation_file {data_dir}/val.json
--test_file {data_dir}/test.json
--output_dir {output_dir}
--overwrite_output_dir
--max_train_samples 8
--max_source_length {max_len}
--max_target_length {max_len}
--do_train
--num_train_epochs {str(lowerCAmelCase_ )}
--per_device_train_batch_size 4
--learning_rate {learning_rate}
--warmup_steps 8
--logging_steps 0
--logging_strategy no
--save_steps {str(lowerCAmelCase_ )}
--group_by_length
--label_smoothing_factor 0.1
--target_lang ro_RO
--source_lang en_XX
'''.split()
_A = F'''
--do_eval
--per_device_eval_batch_size 4
--max_eval_samples 8
--val_max_target_length {max_len}
--evaluation_strategy steps
--eval_steps {str(lowerCAmelCase_ )}
'''.split()
_A = """
--do_predict
""".split()
_A = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F'''--optim {optim}'''.split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
_A = get_gpu_count()
_A = get_torch_dist_unique_port()
_A = F'''
-m torch.distributed.run
--nproc_per_node={n_gpus_to_use}
--master_port={master_port}
{self.examples_dir_str}/pytorch/translation/run_translation.py
'''.split()
_A = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(lowerCAmelCase_ , env=self.get_env() )
else:
_A = ["""run_translation.py"""] + args
with patch.object(lowerCAmelCase_ , """argv""" , lowerCAmelCase_ ):
main()
return output_dir
| 180
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:Optional[Any] = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:Tuple = [
"""MEGA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MegaForCausalLM""",
"""MegaForMaskedLM""",
"""MegaForMultipleChoice""",
"""MegaForQuestionAnswering""",
"""MegaForSequenceClassification""",
"""MegaForTokenClassification""",
"""MegaModel""",
"""MegaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_:Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 358
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_:Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Dict = {
"""BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json""",
# See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : int = "altclip_text_model"
def __init__( self, lowerCamelCase__=25_0002, lowerCamelCase__=1024, lowerCamelCase__=24, lowerCamelCase__=16, lowerCamelCase__=4096, lowerCamelCase__="gelu", lowerCamelCase__=0.1, lowerCamelCase__=0.1, lowerCamelCase__=514, lowerCamelCase__=1, lowerCamelCase__=0.02, lowerCamelCase__=0.02, lowerCamelCase__=1e-05, lowerCamelCase__=1, lowerCamelCase__=0, lowerCamelCase__=2, lowerCamelCase__="absolute", lowerCamelCase__=True, lowerCamelCase__=768, **lowerCamelCase__, ):
super().__init__(pad_token_id=lowerCamelCase__, bos_token_id=lowerCamelCase__, eos_token_id=lowerCamelCase__, **lowerCamelCase__ )
A : Union[str, Any] = vocab_size
A : Dict = hidden_size
A : Union[str, Any] = num_hidden_layers
A : List[str] = num_attention_heads
A : str = hidden_act
A : Dict = intermediate_size
A : List[str] = hidden_dropout_prob
A : Optional[Any] = attention_probs_dropout_prob
A : Tuple = max_position_embeddings
A : Optional[Any] = type_vocab_size
A : Optional[Any] = initializer_range
A : Optional[int] = initializer_factor
A : Tuple = layer_norm_eps
A : List[str] = position_embedding_type
A : int = use_cache
A : int = project_dim
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = "altclip_vision_model"
def __init__( self, lowerCamelCase__=768, lowerCamelCase__=3072, lowerCamelCase__=512, lowerCamelCase__=12, lowerCamelCase__=12, lowerCamelCase__=3, lowerCamelCase__=224, lowerCamelCase__=32, lowerCamelCase__="quick_gelu", lowerCamelCase__=1e-5, lowerCamelCase__=0.0, lowerCamelCase__=0.02, lowerCamelCase__=1.0, **lowerCamelCase__, ):
super().__init__(**lowerCamelCase__ )
A : Optional[Any] = hidden_size
A : Optional[int] = intermediate_size
A : Union[str, Any] = projection_dim
A : str = num_hidden_layers
A : int = num_attention_heads
A : Optional[Any] = num_channels
A : Tuple = patch_size
A : List[Any] = image_size
A : Optional[int] = initializer_range
A : Union[str, Any] = initializer_factor
A : List[str] = attention_dropout
A : int = layer_norm_eps
A : str = hidden_act
@classmethod
def _lowerCAmelCase ( cls, lowerCamelCase__, **lowerCamelCase__ ):
cls._set_token_in_kwargs(lowerCamelCase__ )
A , A : Optional[Any] = cls.get_config_dict(lowerCamelCase__, **lowerCamelCase__ )
# get the vision config dict if we are loading from AltCLIPConfig
if config_dict.get("""model_type""" ) == "altclip":
A : Any = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls, """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(lowerCamelCase__, **lowerCamelCase__ )
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : List[Any] = "altclip"
__lowerCamelCase : List[Any] = True
def __init__( self, lowerCamelCase__=None, lowerCamelCase__=None, lowerCamelCase__=768, lowerCamelCase__=2.6592, **lowerCamelCase__ ):
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
A : Dict = kwargs.pop("""text_config_dict""", lowerCamelCase__ )
A : str = kwargs.pop("""vision_config_dict""", lowerCamelCase__ )
super().__init__(**lowerCamelCase__ )
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
A : Dict = {}
# This is the complete result when using `text_config_dict`.
A : str = AltCLIPTextConfig(**lowerCamelCase__ ).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
A : Optional[Any] = (
f'''`{key}` is found in both `text_config_dict` and `text_config` but with different values. '''
f'''The value `text_config_dict["{key}"]` will be used instead.'''
)
# If inferred from default argument values (just to be super careful)
else:
A : Optional[int] = (
f'''`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '''
f'''value `text_config["{key}"]` will be overriden.'''
)
logger.warning(lowerCamelCase__ )
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict )
if vision_config_dict is not None:
if vision_config is None:
A : int = {}
# This is the complete result when using `vision_config_dict`.
A : Union[str, Any] = AltCLIPVisionConfig(**lowerCamelCase__ ).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
A : Optional[int] = {
str(lowerCamelCase__ ): value for key, value in _vision_config_dict["""id2label"""].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
A : Optional[int] = (
f'''`{key}` is found in both `vision_config_dict` and `vision_config` but with different '''
f'''values. The value `vision_config_dict["{key}"]` will be used instead.'''
)
# If inferred from default argument values (just to be super careful)
else:
A : Any = (
f'''`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '''
f'''The value `vision_config["{key}"]` will be overriden.'''
)
logger.warning(lowerCamelCase__ )
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict )
if text_config is None:
A : Tuple = {}
logger.info("""`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.""" )
if vision_config is None:
A : Union[str, Any] = {}
logger.info("""`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.""" )
A : Dict = AltCLIPTextConfig(**lowerCamelCase__ )
A : Optional[int] = AltCLIPVisionConfig(**lowerCamelCase__ )
A : List[str] = projection_dim
A : Any = logit_scale_init_value
A : Tuple = 1.0
@classmethod
def _lowerCAmelCase ( cls, lowerCamelCase__, lowerCamelCase__, **lowerCamelCase__ ):
return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **lowerCamelCase__ )
def _lowerCAmelCase ( self ):
A : str = copy.deepcopy(self.__dict__ )
A : Any = self.text_config.to_dict()
A : List[str] = self.vision_config.to_dict()
A : Union[str, Any] = self.__class__.model_type
return output
| 115
| 0
|
"""simple docstring"""
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase ) -> list:
"""simple docstring"""
lowerCAmelCase_ : Tuple = len(a_ )
lowerCAmelCase_ : Tuple = []
for i in range(len(a_ ) - pat_len + 1 ):
lowerCAmelCase_ : List[Any] = True
for j in range(a_ ):
if s[i + j] != pattern[j]:
lowerCAmelCase_ : Union[str, Any] = False
break
if match_found:
position.append(a_ )
return position
if __name__ == "__main__":
assert naive_pattern_search("""ABCDEFG""", """DE""") == [3]
print(naive_pattern_search("""ABAAABCDBBABCDDEBCABC""", """ABC"""))
| 241
|
def A ( a_ ,a_ ,a_ ) -> int:
def update_area_of_max_square(a_ ,a_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__UpperCamelCase : Optional[int] =update_area_of_max_square(a_ ,col + 1 )
__UpperCamelCase : List[str] =update_area_of_max_square(row + 1 ,col + 1 )
__UpperCamelCase : List[Any] =update_area_of_max_square(row + 1 ,a_ )
if mat[row][col]:
__UpperCamelCase : Optional[Any] =1 + min([right, diagonal, down] )
__UpperCamelCase : Dict =max(largest_square_area[0] ,a_ )
return sub_problem_sol
else:
return 0
__UpperCamelCase : Union[str, Any] =[0]
update_area_of_max_square(0 ,0 )
return largest_square_area[0]
def A ( a_ ,a_ ,a_ ) -> int:
def update_area_of_max_square_using_dp_array(
a_ ,a_ ,a_ ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
__UpperCamelCase : Tuple =update_area_of_max_square_using_dp_array(a_ ,col + 1 ,a_ )
__UpperCamelCase : Optional[int] =update_area_of_max_square_using_dp_array(row + 1 ,col + 1 ,a_ )
__UpperCamelCase : Any =update_area_of_max_square_using_dp_array(row + 1 ,a_ ,a_ )
if mat[row][col]:
__UpperCamelCase : Optional[Any] =1 + min([right, diagonal, down] )
__UpperCamelCase : str =max(largest_square_area[0] ,a_ )
__UpperCamelCase : Any =sub_problem_sol
return sub_problem_sol
else:
return 0
__UpperCamelCase : Tuple =[0]
__UpperCamelCase : List[Any] =[[-1] * cols for _ in range(a_ )]
update_area_of_max_square_using_dp_array(0 ,0 ,a_ )
return largest_square_area[0]
def A ( a_ ,a_ ,a_ ) -> int:
__UpperCamelCase : Dict =[[0] * (cols + 1) for _ in range(rows + 1 )]
__UpperCamelCase : int =0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
__UpperCamelCase : Optional[Any] =dp_array[row][col + 1]
__UpperCamelCase : int =dp_array[row + 1][col + 1]
__UpperCamelCase : Tuple =dp_array[row + 1][col]
if mat[row][col] == 1:
__UpperCamelCase : Tuple =1 + min(a_ ,a_ ,a_ )
__UpperCamelCase : Any =max(dp_array[row][col] ,a_ )
else:
__UpperCamelCase : Dict =0
return largest_square_area
def A ( a_ ,a_ ,a_ ) -> int:
__UpperCamelCase : Any =[0] * (cols + 1)
__UpperCamelCase : List[Any] =[0] * (cols + 1)
__UpperCamelCase : Tuple =0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
__UpperCamelCase : Any =current_row[col + 1]
__UpperCamelCase : Optional[Any] =next_row[col + 1]
__UpperCamelCase : Union[str, Any] =next_row[col]
if mat[row][col] == 1:
__UpperCamelCase : Any =1 + min(a_ ,a_ ,a_ )
__UpperCamelCase : Optional[int] =max(current_row[col] ,a_ )
else:
__UpperCamelCase : List[str] =0
__UpperCamelCase : Optional[Any] =current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 71
| 0
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
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 _lowerCAmelCase ( A__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = KandinskyVaaControlnetImgaImgPipeline
snake_case_ = ["image_embeds", "negative_image_embeds", "image", "hint"]
snake_case_ = ["image_embeds", "negative_image_embeds", "image", "hint"]
snake_case_ = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
snake_case_ = False
@property
def lowerCAmelCase ( self : Dict )-> str:
return 32
@property
def lowerCAmelCase ( self : int )-> List[str]:
return 32
@property
def lowerCAmelCase ( self : List[Any] )-> str:
return self.time_input_dim
@property
def lowerCAmelCase ( self : Optional[Any] )-> Any:
return self.time_input_dim * 4
@property
def lowerCAmelCase ( self : str )-> Union[str, Any]:
return 1_00
@property
def lowerCAmelCase ( self : Tuple )-> Optional[Any]:
torch.manual_seed(0 )
snake_case = {
"""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,
}
snake_case = UNetaDConditionModel(**__snake_case )
return model
@property
def lowerCAmelCase ( self : List[Any] )-> 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 lowerCAmelCase ( self : str )-> List[str]:
torch.manual_seed(0 )
snake_case = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCAmelCase ( self : int )-> Dict:
snake_case = self.dummy_unet
snake_case = self.dummy_movq
snake_case = {
"""num_train_timesteps""": 10_00,
"""beta_schedule""": """linear""",
"""beta_start""": 0.0_00_85,
"""beta_end""": 0.0_12,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
snake_case = DDIMScheduler(**__snake_case )
snake_case = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def lowerCAmelCase ( self : Union[str, Any] , __snake_case : str , __snake_case : Tuple=0 )-> List[Any]:
snake_case = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__snake_case ) ).to(__snake_case )
snake_case = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__snake_case )
# create init_image
snake_case = floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case )
snake_case = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case = Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) )
# create hint
snake_case = floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case )
if str(__snake_case ).startswith("""mps""" ):
snake_case = torch.manual_seed(__snake_case )
else:
snake_case = torch.Generator(device=__snake_case ).manual_seed(__snake_case )
snake_case = {
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 10,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def lowerCAmelCase ( self : Dict )-> Optional[int]:
snake_case = """cpu"""
snake_case = self.get_dummy_components()
snake_case = self.pipeline_class(**__snake_case )
snake_case = pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
snake_case = pipe(**self.get_dummy_inputs(__snake_case ) )
snake_case = output.images
snake_case = pipe(
**self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0]
snake_case = image[0, -3:, -3:, -1]
snake_case = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case = np.array(
[0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36] )
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 _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : List[str] )-> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self : List[Any] )-> Optional[int]:
snake_case = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy""" )
snake_case = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
snake_case = init_image.resize((5_12, 5_12) )
snake_case = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
snake_case = torch.from_numpy(np.array(__snake_case ) ).float() / 2_55.0
snake_case = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
snake_case = """A robot, 4k photo"""
snake_case = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(__snake_case )
snake_case = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" , torch_dtype=torch.floataa )
snake_case = pipeline.to(__snake_case )
pipeline.set_progress_bar_config(disable=__snake_case )
snake_case = torch.Generator(device="""cpu""" ).manual_seed(0 )
snake_case , snake_case = pipe_prior(
__snake_case , image=__snake_case , strength=0.85 , generator=__snake_case , negative_prompt="""""" , ).to_tuple()
snake_case = pipeline(
image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , hint=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=5_12 , width=5_12 , strength=0.5 , output_type="""np""" , )
snake_case = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(__snake_case , __snake_case )
| 356
|
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {"vocab_file": "vocab.txt"}
_SCREAMING_SNAKE_CASE = {
"vocab_file": {
"openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
},
}
_SCREAMING_SNAKE_CASE = {
"openbmb/cpm-ant-10b": 1024,
}
def __lowerCamelCase ( __lowerCAmelCase : List[Any] ) -> str:
snake_case = collections.OrderedDict()
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" ) as reader:
snake_case = reader.readlines()
for index, token in enumerate(__lowerCAmelCase ):
snake_case = token.rstrip("""\n""" )
snake_case = index
return vocab
class _lowerCAmelCase ( A__ ):
"""simple docstring"""
def __init__( self : Optional[int] , __snake_case : int , __snake_case : Union[str, Any]="<unk>" , __snake_case : Union[str, Any]=2_00 )-> List[str]:
snake_case = vocab
snake_case = unk_token
snake_case = max_input_chars_per_word
def lowerCAmelCase ( self : Any , __snake_case : List[str] )-> List[Any]:
snake_case = list(__snake_case )
if len(__snake_case ) > self.max_input_chars_per_word:
return [self.unk_token]
snake_case = 0
snake_case = []
while start < len(__snake_case ):
snake_case = len(__snake_case )
snake_case = None
while start < end:
snake_case = """""".join(chars[start:end] )
if substr in self.vocab:
snake_case = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(__snake_case )
snake_case = end
return sub_tokens
class _lowerCAmelCase ( A__ ):
"""simple docstring"""
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["input_ids", "attention_mask"]
snake_case_ = False
def __init__( self : int , __snake_case : Tuple , __snake_case : Optional[int]="<d>" , __snake_case : int="</d>" , __snake_case : List[Any]="<s>" , __snake_case : List[str]="</s>" , __snake_case : str="<pad>" , __snake_case : Union[str, Any]="<unk>" , __snake_case : str="</n>" , __snake_case : List[str]="</_>" , __snake_case : Union[str, Any]="left" , **__snake_case : Tuple , )-> Union[str, Any]:
requires_backends(self , ["""jieba"""] )
super().__init__(
bod_token=__snake_case , eod_token=__snake_case , bos_token=__snake_case , eos_token=__snake_case , pad_token=__snake_case , unk_token=__snake_case , line_token=__snake_case , space_token=__snake_case , padding_side=__snake_case , **__snake_case , )
snake_case = bod_token
snake_case = eod_token
snake_case = load_vocab(__snake_case )
snake_case = self.encoder[space_token]
snake_case = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
snake_case = collections.OrderedDict(sorted(self.encoder.items() , key=lambda __snake_case : x[1] ) )
snake_case = {v: k for k, v in self.encoder.items()}
snake_case = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def lowerCAmelCase ( self : Optional[int] )-> List[Any]:
return self.encoder[self.bod_token]
@property
def lowerCAmelCase ( self : str )-> Tuple:
return self.encoder[self.eod_token]
@property
def lowerCAmelCase ( self : str )-> List[str]:
return self.encoder["\n"]
@property
def lowerCAmelCase ( self : List[Any] )-> int:
return len(self.encoder )
def lowerCAmelCase ( self : Any )-> Any:
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCAmelCase ( self : Tuple , __snake_case : Any )-> Union[str, Any]:
snake_case = []
for x in jieba.cut(__snake_case , cut_all=__snake_case ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(__snake_case ) )
return output_tokens
def lowerCAmelCase ( self : str , __snake_case : Tuple , **__snake_case : Dict )-> Optional[int]:
snake_case = [i for i in token_ids if i >= 0]
snake_case = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(__snake_case , **__snake_case )
def lowerCAmelCase ( self : Union[str, Any] , __snake_case : Dict )-> Optional[int]:
return token in self.encoder
def lowerCAmelCase ( self : Optional[Any] , __snake_case : List[str] )-> str:
return "".join(__snake_case )
def lowerCAmelCase ( self : Tuple , __snake_case : int )-> Optional[int]:
return self.encoder.get(__snake_case , self.encoder.get(self.unk_token ) )
def lowerCAmelCase ( self : str , __snake_case : List[Any] )-> str:
return self.decoder.get(__snake_case , self.unk_token )
def lowerCAmelCase ( self : int , __snake_case : str , __snake_case : Optional[str] = None )-> Tuple[str]:
if os.path.isdir(__snake_case ):
snake_case = os.path.join(
__snake_case , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
else:
snake_case = (filename_prefix + """-""" if filename_prefix else """""") + save_directory
snake_case = 0
if " " in self.encoder:
snake_case = self.encoder[""" """]
del self.encoder[" "]
if "\n" in self.encoder:
snake_case = self.encoder["""\n"""]
del self.encoder["\n"]
snake_case = collections.OrderedDict(sorted(self.encoder.items() , key=lambda __snake_case : x[1] ) )
with open(__snake_case , """w""" , encoding="""utf-8""" ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
""" Please check that the vocabulary is not corrupted!""" )
snake_case = token_index
writer.write(token + """\n""" )
index += 1
return (vocab_file,)
def lowerCAmelCase ( self : Dict , __snake_case : List[int] , __snake_case : List[int] = None )-> List[int]:
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def lowerCAmelCase ( self : str , __snake_case : List[int] , __snake_case : Optional[List[int]] = None , __snake_case : bool = False )-> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case )
if token_ids_a is not None:
return [1] + ([0] * len(__snake_case )) + [1] + ([0] * len(__snake_case ))
return [1] + ([0] * len(__snake_case ))
| 3
| 0
|
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class __A :
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : Union[str, Any] ,_snake_case : int=13 ,_snake_case : Tuple=7 ,_snake_case : List[Any]=True ,_snake_case : int=True ,_snake_case : Union[str, Any]=False ,_snake_case : List[Any]=True ,_snake_case : Any=99 ,_snake_case : str=32 ,_snake_case : int=5 ,_snake_case : int=4 ,_snake_case : int=37 ,_snake_case : Tuple="gelu" ,_snake_case : int=0.1 ,_snake_case : List[str]=0.1 ,_snake_case : Union[str, Any]=512 ,_snake_case : Optional[int]=16 ,_snake_case : int=2 ,_snake_case : List[Any]=0.02 ,_snake_case : Optional[int]=3 ,_snake_case : List[str]=4 ,_snake_case : Union[str, Any]=None ,) -> int:
"""simple docstring"""
lowercase__ : Optional[int] = parent
lowercase__ : Dict = batch_size
lowercase__ : int = seq_length
lowercase__ : Optional[int] = is_training
lowercase__ : Optional[int] = use_input_mask
lowercase__ : str = use_token_type_ids
lowercase__ : Optional[int] = use_labels
lowercase__ : List[Any] = vocab_size
lowercase__ : List[Any] = hidden_size
lowercase__ : List[str] = num_hidden_layers
lowercase__ : Optional[int] = num_attention_heads
lowercase__ : Union[str, Any] = intermediate_size
lowercase__ : Any = hidden_act
lowercase__ : Optional[Any] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : Any = max_position_embeddings
lowercase__ : Dict = type_vocab_size
lowercase__ : Union[str, Any] = type_sequence_label_size
lowercase__ : Dict = initializer_range
lowercase__ : str = num_labels
lowercase__ : Optional[Any] = num_choices
lowercase__ : Union[str, Any] = scope
def UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowercase__ : Optional[Any] = None
if self.use_input_mask:
lowercase__ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : Union[str, Any] = None
if self.use_token_type_ids:
lowercase__ : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
lowercase__ : Tuple = None
lowercase__ : List[str] = None
lowercase__ : Tuple = None
if self.use_labels:
lowercase__ : str = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowercase__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
lowercase__ : str = ids_tensor([self.batch_size] ,self.num_choices )
lowercase__ : Union[str, Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
return LlamaConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=_snake_case ,initializer_range=self.initializer_range ,)
def UpperCAmelCase ( self : Optional[int] ,_snake_case : List[str] ,_snake_case : Dict ,_snake_case : Optional[int] ,_snake_case : Dict ,_snake_case : Dict ,_snake_case : List[Any] ,_snake_case : str ) -> Any:
"""simple docstring"""
lowercase__ : Any = LlamaModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : List[str] = model(_snake_case ,attention_mask=_snake_case )
lowercase__ : Optional[Any] = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Dict ,_snake_case : Tuple ,_snake_case : int ,_snake_case : List[str] ,_snake_case : Optional[int] ,_snake_case : Optional[Any] ,_snake_case : Dict ,_snake_case : List[str] ,_snake_case : Optional[Any] ,_snake_case : Optional[int] ,) -> List[str]:
"""simple docstring"""
lowercase__ : int = True
lowercase__ : Optional[int] = LlamaModel(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Optional[Any] = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,encoder_attention_mask=_snake_case ,)
lowercase__ : List[Any] = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,)
lowercase__ : List[Any] = model(_snake_case ,attention_mask=_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Dict ,_snake_case : str ,_snake_case : Any ,_snake_case : str ,_snake_case : int ,_snake_case : Dict ,_snake_case : Optional[Any] ,_snake_case : Optional[int] ,_snake_case : str ,_snake_case : Any ,) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : List[Any] = LlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Union[str, Any] = model(_snake_case ,attention_mask=_snake_case ,labels=_snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self : Tuple ,_snake_case : int ,_snake_case : Optional[Any] ,_snake_case : List[str] ,_snake_case : str ,_snake_case : Dict ,_snake_case : Dict ,_snake_case : Tuple ,_snake_case : str ,_snake_case : Any ,) -> Dict:
"""simple docstring"""
lowercase__ : Optional[Any] = True
lowercase__ : Dict = True
lowercase__ : Optional[int] = LlamaForCausalLM(config=_snake_case )
model.to(_snake_case )
model.eval()
# first forward pass
lowercase__ : Any = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,encoder_attention_mask=_snake_case ,use_cache=_snake_case ,)
lowercase__ : Optional[int] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowercase__ : List[Any] = ids_tensor((self.batch_size, 3) ,config.vocab_size )
lowercase__ : Tuple = ids_tensor((self.batch_size, 3) ,vocab_size=2 )
# append to next input_ids and
lowercase__ : Union[str, Any] = torch.cat([input_ids, next_tokens] ,dim=-1 )
lowercase__ : Dict = torch.cat([input_mask, next_mask] ,dim=-1 )
lowercase__ : Optional[int] = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,encoder_attention_mask=_snake_case ,output_hidden_states=_snake_case ,)['''hidden_states'''][0]
lowercase__ : str = model(
_snake_case ,attention_mask=_snake_case ,encoder_hidden_states=_snake_case ,encoder_attention_mask=_snake_case ,past_key_values=_snake_case ,output_hidden_states=_snake_case ,)['''hidden_states'''][0]
# select random slice
lowercase__ : Optional[Any] = ids_tensor((1,) ,output_from_past.shape[-1] ).item()
lowercase__ : List[str] = output_from_no_past[:, -3:, random_slice_idx].detach()
lowercase__ : str = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_snake_case ,_snake_case ,atol=1e-3 ) )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) : Union[str, Any] = config_and_inputs
lowercase__ : Any = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( A_ ,A_ ,A_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : List[Any] = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
lowerCAmelCase : int = (LlamaForCausalLM,) if is_torch_available() else ()
lowerCAmelCase : int = (
{
"feature-extraction": LlamaModel,
"text-classification": LlamaForSequenceClassification,
"text-generation": LlamaForCausalLM,
"zero-shot": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase : List[str] = False
lowerCAmelCase : List[Any] = False
def UpperCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
lowercase__ : str = LlamaModelTester(self )
lowercase__ : List[Any] = ConfigTester(self ,config_class=_snake_case ,hidden_size=37 )
def UpperCAmelCase ( self : Any ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
lowercase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCAmelCase ( self : Any ) -> str:
"""simple docstring"""
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase__ : List[str] = type
self.model_tester.create_and_check_model(*_snake_case )
def UpperCAmelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[Any] = 3
lowercase__ : Tuple = input_dict['''input_ids''']
lowercase__ : Tuple = input_ids.ne(1 ).to(_snake_case )
lowercase__ : List[str] = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
lowercase__ : List[Any] = LlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Optional[Any] = model(_snake_case ,attention_mask=_snake_case ,labels=_snake_case )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[Any] = 3
lowercase__ : int = '''single_label_classification'''
lowercase__ : List[str] = input_dict['''input_ids''']
lowercase__ : Any = input_ids.ne(1 ).to(_snake_case )
lowercase__ : Tuple = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
lowercase__ : Optional[Any] = LlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : Optional[Any] = model(_snake_case ,attention_mask=_snake_case ,labels=_snake_case )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCAmelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
lowercase__ , lowercase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Tuple = 3
lowercase__ : List[Any] = '''multi_label_classification'''
lowercase__ : Any = input_dict['''input_ids''']
lowercase__ : Dict = input_ids.ne(1 ).to(_snake_case )
lowercase__ : int = ids_tensor(
[self.model_tester.batch_size, config.num_labels] ,self.model_tester.type_sequence_label_size ).to(torch.float )
lowercase__ : Union[str, Any] = LlamaForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowercase__ : int = model(_snake_case ,attention_mask=_snake_case ,labels=_snake_case )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''LLaMA buffers include complex numbers, which breaks this test''' )
def UpperCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : str ) -> Optional[Any]:
"""simple docstring"""
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[Any] = ids_tensor([1, 10] ,config.vocab_size )
lowercase__ : Union[str, Any] = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] ,config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase__ : Optional[int] = LlamaModel(_snake_case )
original_model.to(_snake_case )
original_model.eval()
lowercase__ : Dict = original_model(_snake_case ).last_hidden_state
lowercase__ : str = original_model(_snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase__ : List[str] = {'''type''': scaling_type, '''factor''': 10.0}
lowercase__ : Tuple = LlamaModel(_snake_case )
scaled_model.to(_snake_case )
scaled_model.eval()
lowercase__ : Union[str, Any] = scaled_model(_snake_case ).last_hidden_state
lowercase__ : Tuple = scaled_model(_snake_case ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(_snake_case ,_snake_case ,atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(_snake_case ,_snake_case ,atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_snake_case ,_snake_case ,atol=1e-5 ) )
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''' )
@slow
def UpperCAmelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ : List[str] = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
lowercase__ : Dict = LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf''' ,device_map='''auto''' )
lowercase__ : Optional[Any] = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
lowercase__ : Optional[int] = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]] )
torch.testing.assert_close(out.mean(-1 ) ,_snake_case ,atol=1e-2 ,rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase__ : str = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] ,_snake_case ,atol=1e-5 ,rtol=1e-5 )
@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''' )
@slow
def UpperCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : str = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
lowercase__ : Union[str, Any] = LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf''' ,device_map='''auto''' )
lowercase__ : List[Any] = model(torch.tensor(_snake_case ) )
# Expected mean on dim = -1
lowercase__ : Tuple = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]] )
torch.testing.assert_close(out.mean(-1 ) ,_snake_case ,atol=1e-2 ,rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase__ : Optional[Any] = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] ,_snake_case ,atol=1e-5 ,rtol=1e-5 )
@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''' )
@slow
def UpperCAmelCase ( self : Tuple ) -> Any:
"""simple docstring"""
lowercase__ : Union[str, Any] = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
lowercase__ : Tuple = LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''' ,device_map='''auto''' )
lowercase__ : str = model(torch.tensor(_snake_case ) )
# Expected mean on dim = -1
lowercase__ : List[Any] = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]] )
torch.testing.assert_close(out.mean(-1 ) ,_snake_case ,atol=1e-2 ,rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase__ : Optional[Any] = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) ,_snake_case ,atol=1e-2 ,rtol=1e-2 )
@unittest.skip(
'''Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test''' )
@slow
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ : Tuple = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
lowercase__ : Tuple = LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf''' ,device_map='''auto''' )
lowercase__ : Optional[Any] = model(torch.tensor(_snake_case ) )
lowercase__ : Union[str, Any] = torch.tensor(
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] ,dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) ,_snake_case ,atol=1e-2 ,rtol=1e-2 )
# fmt: off
lowercase__ : Optional[Any] = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] ,_snake_case ,atol=1e-5 ,rtol=1e-5 )
@unittest.skip('''Model is curently gated''' )
@slow
def UpperCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Any = '''Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'''
lowercase__ : str = '''Simply put, the theory of relativity states that '''
lowercase__ : List[Any] = LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''' )
lowercase__ : int = tokenizer.encode(_snake_case ,return_tensors='''pt''' )
lowercase__ : Any = LlamaForCausalLM.from_pretrained(
'''meta-llama/Llama-2-13b-chat-hf''' ,device_map='''sequential''' ,use_safetensors=_snake_case )
# greedy generation outputs
lowercase__ : List[str] = model.generate(_snake_case ,max_new_tokens=64 ,top_p=_snake_case ,temperature=1 ,do_sample=_snake_case )
lowercase__ : Any = tokenizer.decode(generated_ids[0] ,skip_special_tokens=_snake_case )
self.assertEqual(_snake_case ,_snake_case )
| 16
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __A ( metaclass=A_ ):
'''simple docstring'''
lowerCAmelCase : List[str] = ["torch", "torchsde"]
def __init__( self : Tuple ,*_snake_case : Union[str, Any] ,**_snake_case : Any ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self ,['''torch''', '''torchsde'''] )
@classmethod
def UpperCAmelCase ( cls : List[str] ,*_snake_case : int ,**_snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
requires_backends(cls ,['''torch''', '''torchsde'''] )
@classmethod
def UpperCAmelCase ( cls : List[Any] ,*_snake_case : List[Any] ,**_snake_case : List[str] ) -> List[Any]:
"""simple docstring"""
requires_backends(cls ,['''torch''', '''torchsde'''] )
| 16
| 1
|
'''simple docstring'''
def a ( __a = 3 , __a = 7 , __a = 1000000 ) -> Any:
'''simple docstring'''
UpperCamelCase__ :List[str] = 0
UpperCamelCase__ :List[str] = 1
for current_denominator in range(1 , limit + 1 ):
UpperCamelCase__ :Tuple = current_denominator * numerator // denominator
if current_denominator % denominator == 0:
current_numerator -= 1
if current_numerator * max_denominator > current_denominator * max_numerator:
UpperCamelCase__ :List[str] = current_numerator
UpperCamelCase__ :Dict = current_denominator
return max_numerator
if __name__ == "__main__":
print(solution(numerator=3, denominator=7, limit=1000000))
| 360
|
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__snake_case = abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def a ( __a ) -> Optional[int]:
'''simple docstring'''
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__a )
def a ( __a ) -> str:
'''simple docstring'''
from diffusers.utils.testing_utils import pytest_terminal_summary_main
UpperCamelCase__ :Union[str, Any] = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__a , id=__a )
| 219
| 0
|
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''label_embs_concat''': '''label_embeddings_concat''',
'''mask_emb''': '''masked_spec_embed''',
'''spk_proj''': '''speaker_proj''',
}
lowerCAmelCase__ = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
'''label_embeddings_concat''',
'''speaker_proj''',
'''layer_norm_for_extract''',
]
def _A ( A__ , A__ , A__ , A__ , A__ ):
"""simple docstring"""
for attribute in key.split('''.''' ):
__lowercase = getattr(A__ , A__ )
if weight_type is not None:
__lowercase = getattr(A__ , A__ ).shape
else:
__lowercase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
F" {value.shape} for {full_name}" )
if weight_type == "weight":
__lowercase = value
elif weight_type == "weight_g":
__lowercase = value
elif weight_type == "weight_v":
__lowercase = value
elif weight_type == "bias":
__lowercase = value
else:
__lowercase = value
logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def _A ( A__ , A__ ):
"""simple docstring"""
__lowercase = []
__lowercase = fairseq_model.state_dict()
__lowercase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowercase = False
if "conv_layers" in name:
load_conv_layer(
A__ , A__ , A__ , A__ , hf_model.config.feat_extract_norm == '''group''' , )
__lowercase = True
else:
for key, mapped_key in MAPPING.items():
__lowercase = '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
# special case since naming is very similar
continue
__lowercase = True
if "*" in mapped_key:
__lowercase = name.split(A__ )[0].split('''.''' )[-2]
__lowercase = mapped_key.replace('''*''' , A__ )
if "weight_g" in name:
__lowercase = '''weight_g'''
elif "weight_v" in name:
__lowercase = '''weight_v'''
elif "bias" in name:
__lowercase = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowercase = '''weight'''
else:
__lowercase = 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 _A ( A__ , A__ , A__ , A__ , A__ ):
"""simple docstring"""
__lowercase = full_name.split('''conv_layers.''' )[-1]
__lowercase = name.split('''.''' )
__lowercase = int(items[0] )
__lowercase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." )
__lowercase = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." )
__lowercase = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found." )
__lowercase = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found." )
__lowercase = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(A__ )
@torch.no_grad()
def _A ( A__ , A__ , A__=None , A__=None , A__=True ):
"""simple docstring"""
if config_path is not None:
__lowercase = UniSpeechSatConfig.from_pretrained(A__ )
else:
__lowercase = UniSpeechSatConfig()
__lowercase = ''''''
if is_finetuned:
__lowercase = UniSpeechSatForCTC(A__ )
else:
__lowercase = UniSpeechSatForPreTraining(A__ )
__lowercase , __lowercase , __lowercase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
__lowercase = model[0].eval()
recursively_load_weights(A__ , A__ )
hf_wavavec.save_pretrained(A__ )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
lowerCAmelCase__ = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 104
|
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def snake_case ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )-> float:
"""simple docstring"""
__A = np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCAmelCase )] )
__A = np.array(UpperCAmelCase )
__A = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , UpperCAmelCase ) ) , x.transpose() ) , UpperCAmelCase )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def snake_case ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )-> float:
"""simple docstring"""
__A = (1, 2, 1)
__A = (1, 1, 0, 7)
__A = SARIMAX(
UpperCAmelCase , exog=UpperCAmelCase , order=UpperCAmelCase , seasonal_order=UpperCAmelCase )
__A = model.fit(disp=UpperCAmelCase , maxiter=6_0_0 , method='nm' )
__A = model_fit.predict(1 , len(UpperCAmelCase ) , exog=[test_match] )
return result[0]
def snake_case ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )-> float:
"""simple docstring"""
__A = SVR(kernel='rbf' , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(UpperCAmelCase , UpperCAmelCase )
__A = regressor.predict(UpperCAmelCase )
return y_pred[0]
def snake_case ( UpperCAmelCase )-> float:
"""simple docstring"""
train_user.sort()
__A = np.percentile(UpperCAmelCase , 2_5 )
__A = np.percentile(UpperCAmelCase , 7_5 )
__A = qa - qa
__A = qa - (iqr * 0.1)
return low_lim
def snake_case ( UpperCAmelCase , UpperCAmelCase )-> bool:
"""simple docstring"""
__A = 0
__A = 0
for i in list_vote:
if i > actual_result:
__A = not_safe + 1
else:
if abs(abs(UpperCAmelCase ) - abs(UpperCAmelCase ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
a__ : List[str] = [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
a__ : Optional[int] = pd.DataFrame(
data_input, columns=["total_user", "total_even", "days"]
)
a__ : List[Any] = Normalizer().fit_transform(data_input_df.values)
# split data
a__ : Dict = normalize_df[:, 2].tolist()
a__ : Optional[int] = normalize_df[:, 0].tolist()
a__ : str = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
a__ : Tuple = normalize_df[:, [1, 2]].tolist()
a__ : Dict = x[: len(x) - 1]
a__ : Any = x[len(x) - 1 :]
# for linear regression & sarimax
a__ : Tuple = total_date[: len(total_date) - 1]
a__ : List[Any] = total_user[: len(total_user) - 1]
a__ : List[Any] = total_match[: len(total_match) - 1]
a__ : List[str] = total_date[len(total_date) - 1 :]
a__ : List[str] = total_user[len(total_user) - 1 :]
a__ : Tuple = total_match[len(total_match) - 1 :]
# voting system with forecasting
a__ : Optional[Any] = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
a__ : List[str] = "" if data_safety_checker(res_vote, tst_user) else "not "
print("Today's data is {not_str}safe.")
| 161
| 0
|
_lowerCAmelCase : 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],
]
def __snake_case ( _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any ) -> str:
# Return True if there is node that has not iterated.
A_ : str = [False] * len(_lowerCAmelCase )
A_ : Optional[int] = [s]
A_ : Any = True
while queue:
A_ : str = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_lowerCAmelCase )
A_ : List[str] = True
A_ : Optional[int] = u
return visited[t]
def __snake_case ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : str ) -> Union[str, Any]:
A_ : Optional[int] = [-1] * (len(_lowerCAmelCase ))
A_ : Optional[Any] = 0
A_ : Optional[int] = []
A_ : Any = [i[:] for i in graph] # Record original cut, copy.
while bfs(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
A_ : Union[str, Any] = float("Inf" )
A_ : Optional[Any] = sink
while s != source:
# Find the minimum value in select path
A_ : Optional[int] = min(_lowerCAmelCase , graph[parent[s]][s] )
A_ : Tuple = parent[s]
max_flow += path_flow
A_ : str = sink
while v != source:
A_ : List[str] = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
A_ : int = parent[v]
for i in range(len(_lowerCAmelCase ) ):
for j in range(len(graph[0] ) ):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j) )
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| 70
|
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __snake_case ( ) -> tuple[list[int], int]:
A_ : Dict = [randint(-1000 , 1000 ) for i in range(10 )]
A_ : List[str] = randint(-5000 , 5000 )
return (arr, r)
_lowerCAmelCase : List[Any] = make_dataset()
def __snake_case ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int ) -> tuple[int, ...]:
for triplet in permutations(_lowerCAmelCase , 3 ):
if sum(_lowerCAmelCase ) == target:
return tuple(sorted(_lowerCAmelCase ) )
return (0, 0, 0)
def __snake_case ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int ) -> tuple[int, int, int]:
arr.sort()
A_ : Tuple = len(_lowerCAmelCase )
for i in range(n - 1 ):
A_ , A_ : int = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def __snake_case ( ) -> tuple[float, float]:
A_ : Union[str, Any] = "\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n"
A_ : Tuple = "\ntriplet_sum1(*dataset)\n"
A_ : Optional[Any] = "\ntriplet_sum2(*dataset)\n"
A_ : List[str] = repeat(setup=_lowerCAmelCase , stmt=_lowerCAmelCase , repeat=5 , number=10000 )
A_ : Tuple = repeat(setup=_lowerCAmelCase , stmt=_lowerCAmelCase , repeat=5 , number=10000 )
return (min(_lowerCAmelCase ), min(_lowerCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
_lowerCAmelCase : Optional[Any] = solution_times()
print(F'''The time for naive implementation is {times[0]}.''')
print(F'''The time for optimized implementation is {times[1]}.''')
| 70
| 1
|
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _A ( nn.Module ):
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : int = 16 , __SCREAMING_SNAKE_CASE : int = 88 , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : int = 1 , __SCREAMING_SNAKE_CASE : float = 0.0 , __SCREAMING_SNAKE_CASE : int = 32 , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : str = "geglu" , __SCREAMING_SNAKE_CASE : Optional[int] = None , ):
'''simple docstring'''
super().__init__()
__a = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=__SCREAMING_SNAKE_CASE , attention_head_dim=__SCREAMING_SNAKE_CASE , in_channels=__SCREAMING_SNAKE_CASE , num_layers=__SCREAMING_SNAKE_CASE , dropout=__SCREAMING_SNAKE_CASE , norm_num_groups=__SCREAMING_SNAKE_CASE , cross_attention_dim=__SCREAMING_SNAKE_CASE , attention_bias=__SCREAMING_SNAKE_CASE , sample_size=__SCREAMING_SNAKE_CASE , num_vector_embeds=__SCREAMING_SNAKE_CASE , activation_fn=__SCREAMING_SNAKE_CASE , num_embeds_ada_norm=__SCREAMING_SNAKE_CASE , )
for _ in range(2)
])
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
__a = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
__a = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
__a = [1, 0]
def _lowerCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : bool = True , ):
'''simple docstring'''
__a = hidden_states
__a = []
__a = 0
# attention_mask is not used yet
for i in range(2):
# for each of the two transformers, pass the corresponding condition tokens
__a = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
__a = self.transformer_index_for_condition[i]
__a = self.transformers[transformer_index](
__SCREAMING_SNAKE_CASE , encoder_hidden_states=__SCREAMING_SNAKE_CASE , timestep=__SCREAMING_SNAKE_CASE , cross_attention_kwargs=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , )[0]
encoded_states.append(encoded_state - input_states)
tokens_start += self.condition_lengths[i]
__a = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
__a = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=__SCREAMING_SNAKE_CASE)
| 49
|
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
ImageTextPipelineOutput,
UniDiffuserPipeline,
)
else:
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
| 115
| 0
|
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCamelCase__ = 6378137.0
UpperCamelCase__ = 6356752.314245
UpperCamelCase__ = 6378137
def _a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ):
__lowerCAmelCase = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
__lowerCAmelCase = atan((1 - flattening) * tan(radians(SCREAMING_SNAKE_CASE_ ) ) )
__lowerCAmelCase = atan((1 - flattening) * tan(radians(SCREAMING_SNAKE_CASE_ ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
__lowerCAmelCase = haversine_distance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
__lowerCAmelCase = (b_lata + b_lata) / 2
__lowerCAmelCase = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
__lowerCAmelCase = (sin(SCREAMING_SNAKE_CASE_ ) ** 2) * (cos(SCREAMING_SNAKE_CASE_ ) ** 2)
__lowerCAmelCase = cos(sigma / 2 ) ** 2
__lowerCAmelCase = (sigma - sin(SCREAMING_SNAKE_CASE_ )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
__lowerCAmelCase = (cos(SCREAMING_SNAKE_CASE_ ) ** 2) * (sin(SCREAMING_SNAKE_CASE_ ) ** 2)
__lowerCAmelCase = sin(sigma / 2 ) ** 2
__lowerCAmelCase = (sigma + sin(SCREAMING_SNAKE_CASE_ )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354
|
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCAmelCase , __lowerCAmelCase = [], []
while len(SCREAMING_SNAKE_CASE_ ) > 1:
__lowerCAmelCase , __lowerCAmelCase = min(SCREAMING_SNAKE_CASE_ ), max(SCREAMING_SNAKE_CASE_ )
start.append(SCREAMING_SNAKE_CASE_ )
end.append(SCREAMING_SNAKE_CASE_ )
collection.remove(SCREAMING_SNAKE_CASE_ )
collection.remove(SCREAMING_SNAKE_CASE_ )
end.reverse()
return start + collection + end
if __name__ == "__main__":
UpperCamelCase__ = input("""Enter numbers separated by a comma:\n""").strip()
UpperCamelCase__ = [int(item) for item in user_input.split(""",""")]
print(*merge_sort(unsorted), sep=""",""")
| 102
| 0
|
from __future__ import annotations
def _UpperCAmelCase ( a__ , a__):
'''simple docstring'''
if len(snake_case__) < k or k < 0:
raise ValueError("""Invalid Input""")
a_ : Any = sum(array[:k])
for i in range(len(snake_case__) - k):
a_ : Union[str, Any] = current_sum - array[i] + array[i + k]
a_ : List[Any] = max(snake_case__ , snake_case__)
return max_sum
if __name__ == "__main__":
from doctest import testmod
from random import randint
testmod()
__snake_case : int = [randint(-10_00, 10_00) for i in range(1_00)]
__snake_case : List[str] = randint(0, 1_10)
print(F"""The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}""")
| 248
|
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
smartaaa_timesteps,
smartaaa_timesteps,
superaa_timesteps,
superaa_timesteps,
superaaa_timesteps,
)
@dataclass
class A ( __snake_case ):
__magic_name__ = 42
__magic_name__ = 42
__magic_name__ = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_if import IFPipeline
from .pipeline_if_imgaimg import IFImgaImgPipeline
from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
from .pipeline_if_inpainting import IFInpaintingPipeline
from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
from .pipeline_if_superresolution import IFSuperResolutionPipeline
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
| 3
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase : Any ={
'''configuration_efficientformer''': [
'''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EfficientFormerConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Tuple =['''EfficientFormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : int =[
'''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''EfficientFormerForImageClassification''',
'''EfficientFormerForImageClassificationWithTeacher''',
'''EfficientFormerModel''',
'''EfficientFormerPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : List[Any] =[
'''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFEfficientFormerForImageClassification''',
'''TFEfficientFormerForImageClassificationWithTeacher''',
'''TFEfficientFormerModel''',
'''TFEfficientFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientformer import EfficientFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
else:
import sys
lowerCamelCase : List[str] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 196
|
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 CLIPImageProcessor, CLIPProcessor
@require_vision
class __a ( unittest.TestCase ):
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__ : Dict = tempfile.mkdtemp()
# fmt: off
UpperCamelCase__ : List[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
UpperCamelCase__ : List[Any] = dict(zip(SCREAMING_SNAKE_CASE , range(len(SCREAMING_SNAKE_CASE ) ) ) )
UpperCamelCase__ : Tuple = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
UpperCamelCase__ : Tuple = {"unk_token": "<unk>"}
UpperCamelCase__ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
UpperCamelCase__ : 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(SCREAMING_SNAKE_CASE ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(SCREAMING_SNAKE_CASE ) )
UpperCamelCase__ : List[str] = {
"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],
}
UpperCamelCase__ : int = os.path.join(self.tmpdirname , SCREAMING_SNAKE_CASE )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __lowercase ( self : List[str] , **SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE )
def __lowercase ( self : List[Any] , **SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE )
def __lowercase ( self : Any , **SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCamelCase__ : int = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
UpperCamelCase__ : int = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = self.get_tokenizer()
UpperCamelCase__ : List[str] = self.get_rust_tokenizer()
UpperCamelCase__ : str = self.get_image_processor()
UpperCamelCase__ : List[str] = CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE , image_processor=SCREAMING_SNAKE_CASE )
processor_slow.save_pretrained(self.tmpdirname )
UpperCamelCase__ : List[Any] = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Tuple = CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE , image_processor=SCREAMING_SNAKE_CASE )
processor_fast.save_pretrained(self.tmpdirname )
UpperCamelCase__ : Any = CLIPProcessor.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 , SCREAMING_SNAKE_CASE )
self.assertIsInstance(processor_fast.tokenizer , SCREAMING_SNAKE_CASE )
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 , SCREAMING_SNAKE_CASE )
self.assertIsInstance(processor_fast.image_processor , SCREAMING_SNAKE_CASE )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCamelCase__ : str = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase__ : Union[str, Any] = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
UpperCamelCase__ : int = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE , padding_value=1.0 )
UpperCamelCase__ : Tuple = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=SCREAMING_SNAKE_CASE , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCamelCase__ : List[str] = self.get_image_processor()
UpperCamelCase__ : Union[str, Any] = self.get_tokenizer()
UpperCamelCase__ : Any = CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE , image_processor=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = self.prepare_image_inputs()
UpperCamelCase__ : List[Any] = image_processor(SCREAMING_SNAKE_CASE , return_tensors="np" )
UpperCamelCase__ : Optional[Any] = processor(images=SCREAMING_SNAKE_CASE , 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 __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__ : str = self.get_image_processor()
UpperCamelCase__ : int = self.get_tokenizer()
UpperCamelCase__ : Any = CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE , image_processor=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = "lower newer"
UpperCamelCase__ : int = processor(text=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Tuple = tokenizer(SCREAMING_SNAKE_CASE )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCamelCase__ : List[str] = self.get_image_processor()
UpperCamelCase__ : Dict = self.get_tokenizer()
UpperCamelCase__ : Optional[Any] = CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE , image_processor=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = "lower newer"
UpperCamelCase__ : List[Any] = self.prepare_image_inputs()
UpperCamelCase__ : Tuple = processor(text=SCREAMING_SNAKE_CASE , images=SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(SCREAMING_SNAKE_CASE ):
processor()
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = self.get_image_processor()
UpperCamelCase__ : Optional[int] = self.get_tokenizer()
UpperCamelCase__ : Optional[Any] = CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE , image_processor=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase__ : Optional[Any] = processor.batch_decode(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = tokenizer.batch_decode(SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCamelCase__ : Dict = self.get_image_processor()
UpperCamelCase__ : Tuple = self.get_tokenizer()
UpperCamelCase__ : Dict = CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE , image_processor=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = "lower newer"
UpperCamelCase__ : List[str] = self.prepare_image_inputs()
UpperCamelCase__ : str = processor(text=SCREAMING_SNAKE_CASE , images=SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 196
| 1
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class __A ( unittest.TestCase ):
"""simple docstring"""
UpperCamelCase__ : Optional[int] =ViTImageProcessor if is_vision_available() else None
@property
def __lowercase ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =(3, 32, 128)
__UpperCamelCase : Optional[Any] =tempfile.mkdtemp()
# fmt: off
__UpperCamelCase : str =['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
# fmt: on
__UpperCamelCase : Optional[Any] =dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
__UpperCamelCase : Dict =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) + '\n' )
__UpperCamelCase : int ={
'do_normalize': False,
'do_resize': True,
'image_processor_type': 'ViTImageProcessor',
'resample': 3,
'size': {'height': 32, 'width': 128},
}
__UpperCamelCase : Optional[Any] =os.path.join(self.tmpdirname , lowerCamelCase__ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(lowerCamelCase__ , lowerCamelCase__ )
def __lowercase ( self , **lowerCamelCase__ ):
"""simple docstring"""
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase__ )
def __lowercase ( self , **lowerCamelCase__ ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )
__UpperCamelCase : List[str] =Image.fromarray(np.moveaxis(lowerCamelCase__ , 0 , -1 ) )
return image_input
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] =self.get_tokenizer()
__UpperCamelCase : Union[str, Any] =self.get_image_processor()
__UpperCamelCase : Union[str, Any] =MgpstrProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
processor.save_pretrained(self.tmpdirname )
__UpperCamelCase : Dict =MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCamelCase__ )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , lowerCamelCase__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =self.get_tokenizer()
__UpperCamelCase : List[str] =self.get_image_processor()
__UpperCamelCase : int =MgpstrProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
processor.save_pretrained(self.tmpdirname )
__UpperCamelCase : Union[str, Any] =self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__UpperCamelCase : str =self.get_image_processor(do_normalize=lowerCamelCase__ , padding_value=1.0 )
__UpperCamelCase : Dict =MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=lowerCamelCase__ , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , lowerCamelCase__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =self.get_image_processor()
__UpperCamelCase : Optional[int] =self.get_tokenizer()
__UpperCamelCase : Dict =MgpstrProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
__UpperCamelCase : Optional[int] =self.prepare_image_inputs()
__UpperCamelCase : List[Any] =image_processor(lowerCamelCase__ , return_tensors='np' )
__UpperCamelCase : Dict =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 __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : int =self.get_image_processor()
__UpperCamelCase : int =self.get_tokenizer()
__UpperCamelCase : str =MgpstrProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
__UpperCamelCase : List[Any] ='test'
__UpperCamelCase : Optional[Any] =processor(text=lowerCamelCase__ )
__UpperCamelCase : str =tokenizer(lowerCamelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Dict =self.get_image_processor()
__UpperCamelCase : int =self.get_tokenizer()
__UpperCamelCase : Optional[int] =MgpstrProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
__UpperCamelCase : Dict ='test'
__UpperCamelCase : Any =self.prepare_image_inputs()
__UpperCamelCase : List[Any] =processor(text=lowerCamelCase__ , images=lowerCamelCase__ )
self.assertListEqual(list(inputs.keys() ) , ['pixel_values', 'labels'] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase__ ):
processor()
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] =self.get_image_processor()
__UpperCamelCase : Union[str, Any] =self.get_tokenizer()
__UpperCamelCase : List[str] =MgpstrProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
__UpperCamelCase : Union[str, Any] =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
__UpperCamelCase : str =processor.char_decode(lowerCamelCase__ )
__UpperCamelCase : Tuple =tokenizer.batch_decode(lowerCamelCase__ )
__UpperCamelCase : Tuple =[seq.replace(' ' , '' ) for seq in decoded_tok]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] =self.get_image_processor()
__UpperCamelCase : str =self.get_tokenizer()
__UpperCamelCase : str =MgpstrProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
__UpperCamelCase : List[str] =None
__UpperCamelCase : int =self.prepare_image_inputs()
__UpperCamelCase : List[Any] =processor(text=lowerCamelCase__ , images=lowerCamelCase__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] =self.get_image_processor()
__UpperCamelCase : List[str] =self.get_tokenizer()
__UpperCamelCase : Tuple =MgpstrProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
__UpperCamelCase : Optional[int] =torch.randn(1 , 27 , 38 )
__UpperCamelCase : Any =torch.randn(1 , 27 , 50257 )
__UpperCamelCase : Any =torch.randn(1 , 27 , 30522 )
__UpperCamelCase : List[str] =processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds'] )
| 71
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
__lowerCamelCase : Union[str, Any] = '''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 219
| 0
|
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowerCamelCase_ (UpperCamelCase__ : Tuple ):
_UpperCAmelCase : Optional[Any] = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''_float_tensor''',
'''decoder.output_projection.weight''',
]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def lowerCamelCase_ (UpperCamelCase__ : Any ):
_UpperCAmelCase : str = emb.weight.shape
_UpperCAmelCase : List[str] = nn.Linear(__lowerCAmelCase , __lowerCAmelCase , bias=__lowerCAmelCase )
_UpperCAmelCase : int = emb.weight.data
return lin_layer
def lowerCamelCase_ (UpperCamelCase__ : Tuple , UpperCamelCase__ : Any="facebook/mbart-large-en-ro" , UpperCamelCase__ : str=False , UpperCamelCase__ : Optional[Any]=False ):
_UpperCAmelCase : int = torch.load(__lowerCAmelCase , map_location='''cpu''' )['''model''']
remove_ignore_keys_(__lowerCAmelCase )
_UpperCAmelCase : int = state_dict['''encoder.embed_tokens.weight'''].shape[0]
_UpperCAmelCase : str = MBartConfig.from_pretrained(__lowerCAmelCase , vocab_size=__lowerCAmelCase )
if mbart_aa and finetuned:
_UpperCAmelCase : str = '''relu'''
_UpperCAmelCase : int = state_dict['''decoder.embed_tokens.weight''']
_UpperCAmelCase : int = MBartForConditionalGeneration(__lowerCAmelCase )
model.model.load_state_dict(__lowerCAmelCase )
if finetuned:
_UpperCAmelCase : str = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
_lowerCAmelCase :str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'fairseq_path', type=str, help='bart.large, bart.large.cnn or a 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.')
parser.add_argument(
'--hf_config',
default='facebook/mbart-large-cc25',
type=str,
help='Which huggingface architecture to use: mbart-large',
)
parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
_lowerCAmelCase :Any = parser.parse_args()
_lowerCAmelCase :Dict = convert_fairseq_mbart_checkpoint_from_disk(
args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
)
model.save_pretrained(args.pytorch_dump_folder_path)
| 350
|
"""simple docstring"""
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( a ,unittest.TestCase ):
'''simple docstring'''
a__ =TransfoXLTokenizer
a__ =False
a__ =False
def __lowerCAmelCase ( self ) -> List[str]:
super().setUp()
_UpperCAmelCase : Dict = [
'''<unk>''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''unwanted''',
'''wa''',
'''un''',
'''running''',
''',''',
'''low''',
'''l''',
]
_UpperCAmelCase : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __lowerCAmelCase ( self , **A ) -> Dict:
_UpperCAmelCase : Union[str, Any] = True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname , **A )
def __lowerCAmelCase ( self , A ) -> str:
_UpperCAmelCase : str = '''<unk> UNwanted , running'''
_UpperCAmelCase : Union[str, Any] = '''<unk> unwanted, running'''
return input_text, output_text
def __lowerCAmelCase ( self ) -> List[str]:
_UpperCAmelCase : Any = TransfoXLTokenizer(vocab_file=self.vocab_file , lower_case=A )
_UpperCAmelCase : Union[str, Any] = tokenizer.tokenize('''<unk> UNwanted , running''' )
self.assertListEqual(A , ['''<unk>''', '''unwanted''', ''',''', '''running'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , [0, 4, 8, 7] )
def __lowerCAmelCase ( self ) -> str:
_UpperCAmelCase : str = TransfoXLTokenizer(lower_case=A )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo ! how \n Are yoU ? ''' ) , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
_UpperCAmelCase : Tuple = TransfoXLTokenizer(lower_case=A )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo ! how \n Are yoU ? ''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __lowerCAmelCase ( self ) -> int:
_UpperCAmelCase : Tuple = TransfoXLTokenizer(lower_case=A )
_UpperCAmelCase : Optional[Any] = '''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
_UpperCAmelCase : Optional[Any] = [
'''Hello''',
'''(''',
'''bracket''',
''')''',
'''and''',
'''side''',
'''@-@''',
'''scrolled''',
'''[''',
'''and''',
''']''',
'''Henry''',
'''\'s''',
'''$''',
'''5''',
'''@,@''',
'''000''',
'''with''',
'''3''',
'''@.@''',
'''34''',
'''m''',
'''.''',
'''What''',
'''\'s''',
'''up''',
'''!''',
'''?''',
]
self.assertListEqual(tokenizer.tokenize(A ) , A )
self.assertEqual(tokenizer.convert_tokens_to_string(A ) , A )
def __lowerCAmelCase ( self ) -> Optional[int]:
_UpperCAmelCase : str = self.get_tokenizer()
_UpperCAmelCase : List[Any] = len(A )
tokenizer.add_tokens(['''new1''', '''new2'''] )
tokenizer.move_added_token('''new1''' , 1 )
# Check that moved token is not copied (duplicate)
self.assertEqual(len(A ) , original_len + 2 )
# Check that token is moved to specified id
self.assertEqual(tokenizer.encode('''new1''' ) , [1] )
self.assertEqual(tokenizer.decode([1] ) , '''new1''' )
| 68
| 0
|
'''simple docstring'''
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 UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = 3_84
_lowerCAmelCase = 7
if "tiny" in model_name:
_lowerCAmelCase = 96
_lowerCAmelCase = (2, 2, 6, 2)
_lowerCAmelCase = (3, 6, 12, 24)
elif "small" in model_name:
_lowerCAmelCase = 96
_lowerCAmelCase = (2, 2, 18, 2)
_lowerCAmelCase = (3, 6, 12, 24)
elif "base" in model_name:
_lowerCAmelCase = 1_28
_lowerCAmelCase = (2, 2, 18, 2)
_lowerCAmelCase = (4, 8, 16, 32)
_lowerCAmelCase = 12
_lowerCAmelCase = 5_12
elif "large" in model_name:
_lowerCAmelCase = 1_92
_lowerCAmelCase = (2, 2, 18, 2)
_lowerCAmelCase = (6, 12, 24, 48)
_lowerCAmelCase = 12
_lowerCAmelCase = 7_68
# set label information
_lowerCAmelCase = 1_50
_lowerCAmelCase = """huggingface/label-files"""
_lowerCAmelCase = """ade20k-id2label.json"""
_lowerCAmelCase = json.load(open(hf_hub_download(lowerCAmelCase , lowerCAmelCase , repo_type="""dataset""" ) , """r""" ) )
_lowerCAmelCase = {int(lowerCAmelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase = {v: k for k, v in idalabel.items()}
_lowerCAmelCase = SwinConfig(
embed_dim=lowerCAmelCase , depths=lowerCAmelCase , num_heads=lowerCAmelCase , window_size=lowerCAmelCase , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] , )
_lowerCAmelCase = UperNetConfig(
backbone_config=lowerCAmelCase , auxiliary_in_channels=lowerCAmelCase , num_labels=lowerCAmelCase , idalabel=lowerCAmelCase , labelaid=lowerCAmelCase , )
return config
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = []
# 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 UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = dct.pop(lowerCAmelCase )
_lowerCAmelCase = val
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_lowerCAmelCase = 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)
_lowerCAmelCase = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight" )
_lowerCAmelCase = 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
_lowerCAmelCase = in_proj_weight[:dim, :]
_lowerCAmelCase = in_proj_bias[: dim]
_lowerCAmelCase = in_proj_weight[
dim : dim * 2, :
]
_lowerCAmelCase = in_proj_bias[
dim : dim * 2
]
_lowerCAmelCase = in_proj_weight[
-dim :, :
]
_lowerCAmelCase = in_proj_bias[-dim :]
# fmt: on
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = x.shape
_lowerCAmelCase = x.reshape(lowerCAmelCase , 4 , in_channel // 4 )
_lowerCAmelCase = x[:, [0, 2, 1, 3], :].transpose(1 , 2 ).reshape(lowerCAmelCase , lowerCAmelCase )
return x
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = x.shape
_lowerCAmelCase = x.reshape(lowerCAmelCase , in_channel // 4 , 4 )
_lowerCAmelCase = x[:, :, [0, 2, 1, 3]].transpose(1 , 2 ).reshape(lowerCAmelCase , lowerCAmelCase )
return x
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = x.shape[0]
_lowerCAmelCase = x.reshape(4 , in_channel // 4 )
_lowerCAmelCase = x[[0, 2, 1, 3], :].transpose(0 , 1 ).reshape(lowerCAmelCase )
return x
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = x.shape[0]
_lowerCAmelCase = x.reshape(in_channel // 4 , 4 )
_lowerCAmelCase = x[:, [0, 2, 1, 3]].transpose(0 , 1 ).reshape(lowerCAmelCase )
return x
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = {
"""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""",
}
_lowerCAmelCase = model_name_to_url[model_name]
_lowerCAmelCase = torch.hub.load_state_dict_from_url(lowerCAmelCase , map_location="""cpu""" , file_name=lowerCAmelCase )[
"""state_dict"""
]
for name, param in state_dict.items():
print(lowerCAmelCase , param.shape )
_lowerCAmelCase = get_upernet_config(lowerCAmelCase )
_lowerCAmelCase = UperNetForSemanticSegmentation(lowerCAmelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
_lowerCAmelCase = state_dict.pop(lowerCAmelCase )
if "bn" in key:
_lowerCAmelCase = key.replace("""bn""" , """batch_norm""" )
_lowerCAmelCase = val
# rename keys
_lowerCAmelCase = create_rename_keys(lowerCAmelCase )
for src, dest in rename_keys:
rename_key(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
read_in_q_k_v(lowerCAmelCase , config.backbone_config )
# fix downsample parameters
for key, value in state_dict.items():
if "downsample" in key:
if "reduction" in key:
_lowerCAmelCase = reverse_correct_unfold_reduction_order(lowerCAmelCase )
if "norm" in key:
_lowerCAmelCase = reverse_correct_unfold_norm_order(lowerCAmelCase )
model.load_state_dict(lowerCAmelCase )
# verify on image
_lowerCAmelCase = """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
_lowerCAmelCase = Image.open(requests.get(lowerCAmelCase , stream=lowerCAmelCase ).raw ).convert("""RGB""" )
_lowerCAmelCase = SegformerImageProcessor()
_lowerCAmelCase = processor(lowerCAmelCase , return_tensors="""pt""" ).pixel_values
with torch.no_grad():
_lowerCAmelCase = model(lowerCAmelCase )
_lowerCAmelCase = outputs.logits
print(logits.shape )
print("""First values of logits:""" , logits[0, 0, :3, :3] )
# assert values
if model_name == "upernet-swin-tiny":
_lowerCAmelCase = 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":
_lowerCAmelCase = 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":
_lowerCAmelCase = 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":
_lowerCAmelCase = 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] , lowerCAmelCase , 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(lowerCAmelCase )
print(f"Saving processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(lowerCAmelCase )
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__":
A__ : Tuple =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.'''
)
A__ : Tuple =parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 70
|
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = len(lowerCAmelCase )
for i in range(length - 1 ):
_lowerCAmelCase = i
for k in range(i + 1 , lowerCAmelCase ):
if collection[k] < collection[least]:
_lowerCAmelCase = k
if least != i:
_lowerCAmelCase , _lowerCAmelCase = (collection[i], collection[least])
return collection
if __name__ == "__main__":
A__ : str =input('''Enter numbers separated by a comma:\n''').strip()
A__ : Optional[int] =[int(item) for item in user_input.split(''',''')]
print(selection_sort(unsorted))
| 70
| 1
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : UNetaDModel
__lowerCAmelCase : KarrasVeScheduler
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
super().__init__()
self.register_modules(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __call__( self , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = 50 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "pil" , _SCREAMING_SNAKE_CASE = True , **_SCREAMING_SNAKE_CASE , ) -> Union[Tuple, ImagePipelineOutput]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] = self.unet.config.sample_size
UpperCAmelCase : Optional[int] = (batch_size, 3, img_size, img_size)
UpperCAmelCase : str = self.unet
# sample x_0 ~ N(0, sigma_0^2 * I)
UpperCAmelCase : List[Any] = randn_tensor(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , device=self.device ) * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
for t in self.progress_bar(self.scheduler.timesteps ):
# here sigma_t == t_i from the paper
UpperCAmelCase : Any = self.scheduler.schedule[t]
UpperCAmelCase : str = self.scheduler.schedule[t - 1] if t > 0 else 0
# 1. Select temporarily increased noise level sigma_hat
# 2. Add new noise to move from sample_i to sample_hat
UpperCAmelCase , UpperCAmelCase : List[Any] = self.scheduler.add_noise_to_input(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE )
# 3. Predict the noise residual given the noise magnitude `sigma_hat`
# The model inputs and output are adjusted by following eq. (213) in [1].
UpperCAmelCase : Dict = (sigma_hat / 2) * model((sample_hat + 1) / 2 , sigma_hat / 2 ).sample
# 4. Evaluate dx/dt at sigma_hat
# 5. Take Euler step from sigma to sigma_prev
UpperCAmelCase : Dict = self.scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if sigma_prev != 0:
# 6. Apply 2nd order correction
# The model inputs and output are adjusted by following eq. (213) in [1].
UpperCAmelCase : Union[str, Any] = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2 , sigma_prev / 2 ).sample
UpperCAmelCase : Tuple = self.scheduler.step_correct(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , step_output.prev_sample , step_output["""derivative"""] , )
UpperCAmelCase : Dict = step_output.prev_sample
UpperCAmelCase : str = (sample / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase : Tuple = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase : List[Any] = self.numpy_to_pil(_SCREAMING_SNAKE_CASE )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE )
| 76
|
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( UpperCamelCase : list[list[float]] ):
UpperCAmelCase : int = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(UpperCamelCase ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
UpperCAmelCase : Union[str, Any] = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError("""This matrix has no inverse.""" )
# Creates a copy of the matrix with swapped positions of the elements
UpperCAmelCase : Dict = [[0.0, 0.0], [0.0, 0.0]]
UpperCAmelCase , UpperCAmelCase : Dict = matrix[1][1], matrix[0][0]
UpperCAmelCase , UpperCAmelCase : Optional[Any] = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(UpperCamelCase ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(UpperCamelCase ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
UpperCAmelCase : Optional[int] = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError("""This matrix has no inverse.""" )
# Creating cofactor matrix
UpperCAmelCase : List[Any] = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
UpperCAmelCase : Dict = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
UpperCAmelCase : List[Any] = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
UpperCAmelCase : int = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
UpperCAmelCase : Dict = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
UpperCAmelCase : Optional[int] = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
UpperCAmelCase : Optional[Any] = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
UpperCAmelCase : Optional[Any] = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
UpperCAmelCase : str = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
UpperCAmelCase : Optional[Any] = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
UpperCAmelCase : Any = array(UpperCamelCase )
for i in range(3 ):
for j in range(3 ):
UpperCAmelCase : Optional[int] = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
UpperCAmelCase : int = array(UpperCamelCase )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(UpperCamelCase )
# Calculate the inverse of the matrix
return [[float(d(UpperCamelCase ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError("""Please provide a matrix of size 2x2 or 3x3.""" )
| 76
| 1
|
import argparse
import json
from tqdm import tqdm
def lowerCamelCase__ ( ) -> Tuple:
__snake_case = 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''' , )
__snake_case = 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:
__snake_case = json.load(snake_case_ )
for dpr_record in tqdm(snake_case_ ):
__snake_case = dpr_record['''question''']
__snake_case = [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()
| 24
|
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , a_ , a_ , a_ , a_ , a_ , a_ , a_ , ):
'''simple docstring'''
super().__init__()
self.register_modules(
vae=a_ , text_encoder=a_ , tokenizer=a_ , unet=a_ , scheduler=a_ , safety_checker=a_ , feature_extractor=a_ , )
def SCREAMING_SNAKE_CASE (self , a_ = "auto" ):
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
__snake_case : Optional[int] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(a_ )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
self.enable_attention_slicing(a_ )
@torch.no_grad()
def __call__(self , a_ , a_ = 5_12 , a_ = 5_12 , a_ = 50 , a_ = 7.5 , a_ = None , a_ = 1 , a_ = 0.0 , a_ = None , a_ = None , a_ = "pil" , a_ = True , a_ = None , a_ = 1 , a_ = None , **a_ , ):
'''simple docstring'''
if isinstance(a_ , a_ ):
__snake_case : Any = 1
elif isinstance(a_ , a_ ):
__snake_case : Any = len(a_ )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a_ )}""" )
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 (callback_steps is None) or (
callback_steps is not None and (not isinstance(a_ , a_ ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(a_ )}.""" )
# get prompt text embeddings
__snake_case : int = self.tokenizer(
a_ , padding='''max_length''' , max_length=self.tokenizer.model_max_length , return_tensors='''pt''' , )
__snake_case : int = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
__snake_case : List[Any] = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
__snake_case : Optional[int] = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
__snake_case : Optional[int] = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
__snake_case , __snake_case , __snake_case : Union[str, Any] = text_embeddings.shape
__snake_case : Optional[int] = text_embeddings.repeat(1 , a_ , 1 )
__snake_case : Dict = text_embeddings.view(bs_embed * num_images_per_prompt , a_ , -1 )
# 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.
__snake_case : Dict = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
__snake_case : List[str]
if negative_prompt is None:
__snake_case : List[Any] = ['''''']
elif type(a_ ) is not type(a_ ):
raise TypeError(
f"""`negative_prompt` should be the same type to `prompt`, but got {type(a_ )} !="""
f""" {type(a_ )}.""" )
elif isinstance(a_ , a_ ):
__snake_case : List[str] = [negative_prompt]
elif batch_size != len(a_ ):
raise ValueError(
f"""`negative_prompt`: {negative_prompt} has batch size {len(a_ )}, but `prompt`:"""
f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
''' the batch size of `prompt`.''' )
else:
__snake_case : Optional[int] = negative_prompt
__snake_case : Optional[int] = text_input_ids.shape[-1]
__snake_case : List[Any] = self.tokenizer(
a_ , padding='''max_length''' , max_length=a_ , truncation=a_ , return_tensors='''pt''' , )
__snake_case : str = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__snake_case : str = uncond_embeddings.shape[1]
__snake_case : int = uncond_embeddings.repeat(a_ , a_ , 1 )
__snake_case : int = uncond_embeddings.view(batch_size * num_images_per_prompt , a_ , -1 )
# 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
__snake_case : Any = 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`.
__snake_case : Any = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
__snake_case : Dict = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
__snake_case : Dict = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
__snake_case : Union[str, Any] = torch.randn(
a_ , generator=a_ , device='''cpu''' , dtype=a_ ).to(self.device )
__snake_case : Tuple = torch.randn(a_ , generator=a_ , device='''cpu''' , dtype=a_ ).to(
self.device )
else:
__snake_case : Dict = torch.randn(
a_ , generator=a_ , device=self.device , dtype=a_ )
__snake_case : Dict = torch.randn(a_ , generator=a_ , device=self.device , dtype=a_ )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
__snake_case : Union[str, Any] = latents_reference.to(self.device )
__snake_case : Dict = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
__snake_case : int = (latents_shape[3] - latents_shape_reference[3]) // 2
__snake_case : Tuple = (latents_shape[2] - latents_shape_reference[2]) // 2
__snake_case : Optional[int] = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
__snake_case : Union[str, Any] = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
__snake_case : int = 0 if dx < 0 else dx
__snake_case : Union[str, Any] = 0 if dy < 0 else dy
__snake_case : str = max(-dx , 0 )
__snake_case : Tuple = max(-dy , 0 )
# import pdb
# pdb.set_trace()
__snake_case : Any = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(a_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
__snake_case : Optional[Any] = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
__snake_case : List[Any] = 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]
__snake_case : Any = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__snake_case : Tuple = {}
if accepts_eta:
__snake_case : List[str] = eta
for i, t in enumerate(self.progress_bar(a_ ) ):
# expand the latents if we are doing classifier free guidance
__snake_case : str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__snake_case : Tuple = self.scheduler.scale_model_input(a_ , a_ )
# predict the noise residual
__snake_case : int = self.unet(a_ , a_ , encoder_hidden_states=a_ ).sample
# perform guidance
if do_classifier_free_guidance:
__snake_case , __snake_case : Tuple = noise_pred.chunk(2 )
__snake_case : str = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
__snake_case : Optional[Any] = self.scheduler.step(a_ , a_ , a_ , **a_ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(a_ , a_ , a_ )
__snake_case : Union[str, Any] = 1 / 0.1_8215 * latents
__snake_case : Optional[Any] = self.vae.decode(a_ ).sample
__snake_case : Union[str, Any] = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
__snake_case : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
__snake_case : Optional[int] = self.feature_extractor(self.numpy_to_pil(a_ ) , return_tensors='''pt''' ).to(
self.device )
__snake_case , __snake_case : List[Any] = self.safety_checker(
images=a_ , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
__snake_case : Union[str, Any] = None
if output_type == "pil":
__snake_case : Union[str, Any] = self.numpy_to_pil(a_ )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=a_ , nsfw_content_detected=a_ )
| 102
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase_ = {"processing_layoutxlm": ["LayoutXLMProcessor"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["LayoutXLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["LayoutXLMTokenizerFast"]
if TYPE_CHECKING:
from .processing_layoutxlm import LayoutXLMProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm import LayoutXLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 344
|
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( _snake_case ):
UpperCamelCase__ : List[Any] =(PNDMScheduler,)
UpperCamelCase__ : Optional[Any] =(("num_inference_steps", 50),)
def __a ( self :Union[str, Any] , **_lowercase :Any) -> Union[str, Any]:
UpperCAmelCase_ = {
'''num_train_timesteps''': 1000,
'''beta_start''': 0.0_001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**_lowercase)
return config
def __a ( self :str , _lowercase :List[Any]=0 , **_lowercase :str) -> Union[str, Any]:
UpperCAmelCase_ = dict(self.forward_default_kwargs)
UpperCAmelCase_ = kwargs.pop('''num_inference_steps''' , _lowercase)
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
UpperCAmelCase_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.get_scheduler_config(**_lowercase)
UpperCAmelCase_ = scheduler_class(**_lowercase)
scheduler.set_timesteps(_lowercase)
# copy over dummy past residuals
UpperCAmelCase_ = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowercase)
UpperCAmelCase_ = scheduler_class.from_pretrained(_lowercase)
new_scheduler.set_timesteps(_lowercase)
# copy over dummy past residuals
UpperCAmelCase_ = dummy_past_residuals[:]
UpperCAmelCase_ = scheduler.step_prk(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = new_scheduler.step_prk(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
UpperCAmelCase_ = scheduler.step_plms(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = new_scheduler.step_plms(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def __a ( self :Any) -> Optional[Any]:
pass
def __a ( self :str , _lowercase :int=0 , **_lowercase :Union[str, Any]) -> List[Any]:
UpperCAmelCase_ = dict(self.forward_default_kwargs)
UpperCAmelCase_ = kwargs.pop('''num_inference_steps''' , _lowercase)
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
UpperCAmelCase_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**_lowercase)
scheduler.set_timesteps(_lowercase)
# copy over dummy past residuals (must be after setting timesteps)
UpperCAmelCase_ = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowercase)
UpperCAmelCase_ = scheduler_class.from_pretrained(_lowercase)
# copy over dummy past residuals
new_scheduler.set_timesteps(_lowercase)
# copy over dummy past residual (must be after setting timesteps)
UpperCAmelCase_ = dummy_past_residuals[:]
UpperCAmelCase_ = scheduler.step_prk(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = new_scheduler.step_prk(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
UpperCAmelCase_ = scheduler.step_plms(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = new_scheduler.step_plms(_lowercase , _lowercase , _lowercase , **_lowercase).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def __a ( self :int , **_lowercase :str) -> Optional[Any]:
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config(**_lowercase)
UpperCAmelCase_ = scheduler_class(**_lowercase)
UpperCAmelCase_ = 10
UpperCAmelCase_ = self.dummy_model()
UpperCAmelCase_ = self.dummy_sample_deter
scheduler.set_timesteps(_lowercase)
for i, t in enumerate(scheduler.prk_timesteps):
UpperCAmelCase_ = model(_lowercase , _lowercase)
UpperCAmelCase_ = scheduler.step_prk(_lowercase , _lowercase , _lowercase).prev_sample
for i, t in enumerate(scheduler.plms_timesteps):
UpperCAmelCase_ = model(_lowercase , _lowercase)
UpperCAmelCase_ = scheduler.step_plms(_lowercase , _lowercase , _lowercase).prev_sample
return sample
def __a ( self :Union[str, Any]) -> int:
UpperCAmelCase_ = dict(self.forward_default_kwargs)
UpperCAmelCase_ = kwargs.pop('''num_inference_steps''' , _lowercase)
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**_lowercase)
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
if num_inference_steps is not None and hasattr(_lowercase , '''set_timesteps'''):
scheduler.set_timesteps(_lowercase)
elif num_inference_steps is not None and not hasattr(_lowercase , '''set_timesteps'''):
UpperCAmelCase_ = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
UpperCAmelCase_ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
UpperCAmelCase_ = dummy_past_residuals[:]
UpperCAmelCase_ = scheduler.step_prk(_lowercase , 0 , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = scheduler.step_prk(_lowercase , 1 , _lowercase , **_lowercase).prev_sample
self.assertEqual(output_a.shape , sample.shape)
self.assertEqual(output_a.shape , output_a.shape)
UpperCAmelCase_ = scheduler.step_plms(_lowercase , 0 , _lowercase , **_lowercase).prev_sample
UpperCAmelCase_ = scheduler.step_plms(_lowercase , 1 , _lowercase , **_lowercase).prev_sample
self.assertEqual(output_a.shape , sample.shape)
self.assertEqual(output_a.shape , output_a.shape)
def __a ( self :Any) -> Dict:
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=_lowercase)
def __a ( self :List[Any]) -> Any:
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=_lowercase)
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config(steps_offset=1)
UpperCAmelCase_ = scheduler_class(**_lowercase)
scheduler.set_timesteps(10)
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]) , )
def __a ( self :Optional[int]) -> str:
for beta_start, beta_end in zip([0.0_001, 0.001] , [0.002, 0.02]):
self.check_over_configs(beta_start=_lowercase , beta_end=_lowercase)
def __a ( self :Any) -> List[str]:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_lowercase)
def __a ( self :List[Any]) -> Dict:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_lowercase)
def __a ( self :Any) -> Tuple:
for t in [1, 5, 10]:
self.check_over_forward(time_step=_lowercase)
def __a ( self :Tuple) -> Dict:
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100]):
self.check_over_forward(num_inference_steps=_lowercase)
def __a ( self :str) -> List[Any]:
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
UpperCAmelCase_ = 27
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ = self.dummy_sample
UpperCAmelCase_ = 0.1 * sample
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**_lowercase)
scheduler.set_timesteps(_lowercase)
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2]):
UpperCAmelCase_ = scheduler.step_prk(_lowercase , _lowercase , _lowercase).prev_sample
def __a ( self :List[str]) -> int:
with self.assertRaises(_lowercase):
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**_lowercase)
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample).prev_sample
def __a ( self :List[str]) -> Dict:
UpperCAmelCase_ = self.full_loop()
UpperCAmelCase_ = torch.sum(torch.abs(_lowercase))
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_sum.item() - 198.1_318) < 1E-2
assert abs(result_mean.item() - 0.2_580) < 1E-3
def __a ( self :Any) -> Tuple:
UpperCAmelCase_ = self.full_loop(prediction_type='''v_prediction''')
UpperCAmelCase_ = torch.sum(torch.abs(_lowercase))
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_sum.item() - 67.3_986) < 1E-2
assert abs(result_mean.item() - 0.0_878) < 1E-3
def __a ( self :int) -> Any:
# We specify different beta, so that the first alpha is 0.99
UpperCAmelCase_ = self.full_loop(set_alpha_to_one=_lowercase , beta_start=0.01)
UpperCAmelCase_ = torch.sum(torch.abs(_lowercase))
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_sum.item() - 230.0_399) < 1E-2
assert abs(result_mean.item() - 0.2_995) < 1E-3
def __a ( self :Any) -> Dict:
# We specify different beta, so that the first alpha is 0.99
UpperCAmelCase_ = self.full_loop(set_alpha_to_one=_lowercase , beta_start=0.01)
UpperCAmelCase_ = torch.sum(torch.abs(_lowercase))
UpperCAmelCase_ = torch.mean(torch.abs(_lowercase))
assert abs(result_sum.item() - 186.9_482) < 1E-2
assert abs(result_mean.item() - 0.2_434) < 1E-3
| 344
| 1
|
def snake_case_ ( snake_case=2_81_23 ) -> List[str]:
lowercase__: List[str] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
lowercase__: str = set()
lowercase__: List[Any] = 0
for n in range(1 , limit + 1 ):
if sum_divs[n] > n:
abundants.add(snake_case )
if not any((n - a in abundants) for a in abundants ):
res += n
return res
if __name__ == "__main__":
print(solution())
| 196
|
__lowerCAmelCase = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def snake_case_ ( snake_case , snake_case , snake_case ) -> list[str]:
lowercase__: int = set()
# keep track of all the paths to be checked
lowercase__: Optional[int] = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
lowercase__: List[Any] = queue.pop(0 )
# get the last node from the path
lowercase__: Optional[int] = path[-1]
if node not in explored:
lowercase__: Optional[Any] = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
lowercase__: Tuple = list(snake_case )
new_path.append(snake_case )
queue.append(snake_case )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(snake_case )
# in case there's no path between the 2 nodes
return []
def snake_case_ ( snake_case , snake_case , snake_case ) -> int:
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
lowercase__: Tuple = [start]
lowercase__: List[Any] = set(snake_case )
# Keep tab on distances from `start` node.
lowercase__: Tuple = {start: 0, target: -1}
while queue:
lowercase__: Dict = queue.pop(0 )
if node == target:
lowercase__: str = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(snake_case )
queue.append(snake_case )
lowercase__: List[str] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 196
| 1
|
import os
from distutils.util import strtobool
def UpperCamelCase ( __lowercase : Optional[Any] ,__lowercase : List[Any] ):
'''simple docstring'''
for e in env_keys:
A_ : Any = int(os.environ.get(__lowercase ,-1 ) )
if val >= 0:
return val
return default
def UpperCamelCase ( __lowercase : Dict ,__lowercase : List[Any]=False ):
'''simple docstring'''
A_ : Tuple = os.environ.get(__lowercase ,str(__lowercase ) )
return strtobool(__lowercase ) == 1 # As its name indicates `strtobool` actually returns an int...
def UpperCamelCase ( __lowercase : Dict ,__lowercase : Tuple="no" ):
'''simple docstring'''
A_ : List[Any] = os.environ.get(__lowercase ,str(__lowercase ) )
return value
| 192
|
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""tensor(bool)""": np.bool_,
"""tensor(int8)""": np.inta,
"""tensor(uint8)""": np.uinta,
"""tensor(int16)""": np.intaa,
"""tensor(uint16)""": np.uintaa,
"""tensor(int32)""": np.intaa,
"""tensor(uint32)""": np.uintaa,
"""tensor(int64)""": np.intaa,
"""tensor(uint64)""": np.uintaa,
"""tensor(float16)""": np.floataa,
"""tensor(float)""": np.floataa,
"""tensor(double)""": np.floataa,
}
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , lowercase=None , **lowercase ):
"""simple docstring"""
logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.' )
A_ : List[Any] = model
A_ : Dict = kwargs.get('model_save_dir' , lowercase )
A_ : List[str] = kwargs.get('latest_model_name' , lowercase )
def __call__( self , **lowercase ):
"""simple docstring"""
A_ : str = {k: np.array(lowercase ) for k, v in kwargs.items()}
return self.model.run(lowercase , lowercase )
@staticmethod
def lowerCAmelCase_ ( lowercase , lowercase=None , lowercase=None ):
"""simple docstring"""
if provider is None:
logger.info('No onnxruntime provider specified, using CPUExecutionProvider' )
A_ : List[Any] = 'CPUExecutionProvider'
return ort.InferenceSession(lowercase , providers=[provider] , sess_options=lowercase )
def lowerCAmelCase_ ( self , lowercase , lowercase = None , **lowercase ):
"""simple docstring"""
A_ : str = file_name if file_name is not None else ONNX_WEIGHTS_NAME
A_ : Optional[int] = self.model_save_dir.joinpath(self.latest_model_name )
A_ : int = Path(lowercase ).joinpath(lowercase )
try:
shutil.copyfile(lowercase , lowercase )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
A_ : Optional[Any] = self.model_save_dir.joinpath(lowercase )
if src_path.exists():
A_ : int = Path(lowercase ).joinpath(lowercase )
try:
shutil.copyfile(lowercase , lowercase )
except shutil.SameFileError:
pass
def lowerCAmelCase_ ( self , lowercase , **lowercase , ):
"""simple docstring"""
if os.path.isfile(lowercase ):
logger.error(F'''Provided path ({save_directory}) should be a directory, not a file''' )
return
os.makedirs(lowercase , exist_ok=lowercase )
# saving model weights/files
self._save_pretrained(lowercase , **lowercase )
@classmethod
def lowerCAmelCase_ ( cls , lowercase , lowercase = None , lowercase = None , lowercase = False , lowercase = None , lowercase = None , lowercase = None , lowercase = None , **lowercase , ):
"""simple docstring"""
A_ : Any = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(lowercase ):
A_ : Optional[int] = OnnxRuntimeModel.load_model(
os.path.join(lowercase , lowercase ) , provider=lowercase , sess_options=lowercase )
A_ : Dict = Path(lowercase )
# load model from hub
else:
# download model
A_ : List[str] = hf_hub_download(
repo_id=lowercase , filename=lowercase , use_auth_token=lowercase , revision=lowercase , cache_dir=lowercase , force_download=lowercase , )
A_ : int = Path(lowercase ).parent
A_ : Optional[Any] = Path(lowercase ).name
A_ : Any = OnnxRuntimeModel.load_model(lowercase , provider=lowercase , sess_options=lowercase )
return cls(model=lowercase , **lowercase )
@classmethod
def lowerCAmelCase_ ( cls , lowercase , lowercase = True , lowercase = None , lowercase = None , **lowercase , ):
"""simple docstring"""
A_ : List[Any] = None
if len(str(lowercase ).split('@' ) ) == 2:
A_ , A_ : int = model_id.split('@' )
return cls._from_pretrained(
model_id=lowercase , revision=lowercase , cache_dir=lowercase , force_download=lowercase , use_auth_token=lowercase , **lowercase , )
| 192
| 1
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=_lowerCamelCase ):
__lowercase = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def lowerCamelCase ( cls , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def lowerCamelCase ( cls , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class __UpperCAmelCase ( metaclass=_lowerCamelCase ):
__lowercase = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def lowerCamelCase ( cls , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def lowerCamelCase ( cls , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class __UpperCAmelCase ( metaclass=_lowerCamelCase ):
__lowercase = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def lowerCamelCase ( cls , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def lowerCamelCase ( cls , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class __UpperCAmelCase ( metaclass=_lowerCamelCase ):
__lowercase = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def lowerCamelCase ( cls , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def lowerCamelCase ( cls , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class __UpperCAmelCase ( metaclass=_lowerCamelCase ):
__lowercase = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def lowerCamelCase ( cls , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def lowerCamelCase ( cls , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class __UpperCAmelCase ( metaclass=_lowerCamelCase ):
__lowercase = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def lowerCamelCase ( cls , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def lowerCamelCase ( cls , *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 42
|
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCAmelCase__ = logging.getLogger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
A__ = label_idx
def UpperCamelCase ( self , lowercase , lowercase ) -> List[InputExample]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = mode.value
A__ = os.path.join(lowercase , F'{mode}.txt' )
A__ = 1
A__ = []
with open(lowercase , encoding="utf-8" ) as f:
A__ = []
A__ = []
for line in f:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
guid_index += 1
A__ = []
A__ = []
else:
A__ = line.split(" " )
words.append(splits[0] )
if len(lowercase ) > 1:
labels.append(splits[self.label_idx].replace("\n" , "" ) )
else:
# Examples could have no label for mode = "test"
labels.append("O" )
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
return examples
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = 0
for line in test_input_reader:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
writer.write(lowercase )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
A__ = line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"
writer.write(lowercase )
else:
logger.warning("Maximum sequence length exceeded: No prediction for '%s'." , line.split()[0] )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
A__ = f.read().splitlines()
if "O" not in labels:
A__ = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(label_idx=-2 )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
A__ = f.read().splitlines()
if "O" not in labels:
A__ = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class a__ ( snake_case ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase , lowercase ) -> List[InputExample]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = mode.value
A__ = os.path.join(lowercase , F'{mode}.txt' )
A__ = 1
A__ = []
with open(lowercase , encoding="utf-8" ) as f:
for sentence in parse_incr(lowercase ):
A__ = []
A__ = []
for token in sentence:
words.append(token["form"] )
labels.append(token["upos"] )
assert len(lowercase ) == len(lowercase )
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
guid_index += 1
return examples
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = 0
for sentence in parse_incr(lowercase ):
A__ = preds_list[example_id]
A__ = ""
for token in sentence:
out += F'{token["form"]} ({token["upos"]}|{s_p.pop(0 )}) '
out += "\n"
writer.write(lowercase )
example_id += 1
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 68
| 0
|
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _lowerCamelCase ( lowercase : Tuple , lowercase : Dict , lowercase : Dict ) -> List[Any]:
_a = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
_a = (
("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(lowercase ):
os.makedirs(lowercase )
_a = model.state_dict()
def to_tf_var_name(lowercase : Union[str, Any] ):
for patt, repl in iter(lowercase ):
_a = name.replace(lowercase , lowercase )
return F'bert/{name}'
def create_tf_var(lowercase : str , lowercase : List[Any] , lowercase : Optional[int] ):
_a = tf.dtypes.as_dtype(tensor.dtype )
_a = tf.get_variable(dtype=lowercase , shape=tensor.shape , name=lowercase , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(lowercase )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
_a = to_tf_var_name(lowercase )
_a = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
_a = torch_tensor.T
_a = create_tf_var(tensor=lowercase , name=lowercase , session=lowercase )
tf.keras.backend.set_value(lowercase , lowercase )
_a = session.run(lowercase )
print(F'Successfully created {tf_name}: {np.allclose(lowercase , lowercase )}' )
_a = tf.train.Saver(tf.trainable_variables() )
saver.save(lowercase , os.path.join(lowercase , model_name.replace("-" , "_" ) + ".ckpt" ) )
def _lowerCamelCase ( lowercase : Optional[Any]=None ) -> Optional[int]:
_a = argparse.ArgumentParser()
parser.add_argument("--model_name" , type=lowercase , required=lowercase , help="model name e.g. bert-base-uncased" )
parser.add_argument(
"--cache_dir" , type=lowercase , default=lowercase , required=lowercase , help="Directory containing pytorch model" )
parser.add_argument("--pytorch_model_path" , type=lowercase , required=lowercase , help="/path/to/<pytorch-model-name>.bin" )
parser.add_argument("--tf_cache_dir" , type=lowercase , required=lowercase , help="Directory in which to save tensorflow model" )
_a = parser.parse_args(lowercase )
_a = 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=lowercase , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 359
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase_ : Optional[int] = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase_ : Tuple = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _lowerCamelCase ( lowercase : str ) -> str:
re.sub("<n>" , "" , lowercase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(lowercase ) )
| 346
| 0
|
from collections.abc import Callable
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : float = a
SCREAMING_SNAKE_CASE : float = b
if function(_a) == 0: # one of the a or b is a root for the function
return a
elif function(_a) == 0:
return b
elif (
function(_a) * function(_a) > 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:
SCREAMING_SNAKE_CASE : float = start + (end - start) / 2.0
while abs(start - mid) > 10**-7: # until precisely equals to 10^-7
if function(_a) == 0:
return mid
elif function(_a) * function(_a) < 0:
SCREAMING_SNAKE_CASE : List[Any] = mid
else:
SCREAMING_SNAKE_CASE : List[str] = mid
SCREAMING_SNAKE_CASE : Any = start + (end - start) / 2.0
return mid
def lowerCamelCase__ ( _a):
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 76
|
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =CustomTokenizer
pass
| 76
| 1
|
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : Dict=1_3 , UpperCamelCase__ : Any=3_0 , UpperCamelCase__ : Union[str, Any]=2 , UpperCamelCase__ : Any=3 , UpperCamelCase__ : Tuple=True , UpperCamelCase__ : Optional[int]=True , UpperCamelCase__ : Union[str, Any]=3_2 , UpperCamelCase__ : Union[str, Any]=5 , UpperCamelCase__ : List[str]=4 , UpperCamelCase__ : List[str]=3_7 , UpperCamelCase__ : Tuple="gelu" , UpperCamelCase__ : List[str]=0.1 , UpperCamelCase__ : int=0.1 , UpperCamelCase__ : str=1_0 , UpperCamelCase__ : Tuple=0.0_2 , UpperCamelCase__ : Optional[int]=3 , UpperCamelCase__ : Union[str, Any]=None , UpperCamelCase__ : Dict=2 , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = image_size
UpperCamelCase = patch_size
UpperCamelCase = num_channels
UpperCamelCase = is_training
UpperCamelCase = use_labels
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = scope
UpperCamelCase = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
UpperCamelCase = (image_size // patch_size) ** 2
UpperCamelCase = num_patches + 2
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = self.get_config()
return config, pixel_values, labels
def A ( self : Optional[int] ):
"""simple docstring"""
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCamelCase__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def A ( self : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] ):
"""simple docstring"""
UpperCamelCase = DeiTModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Dict , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : int ):
"""simple docstring"""
UpperCamelCase = DeiTForMaskedImageModeling(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
UpperCamelCase = 1
UpperCamelCase = DeiTForMaskedImageModeling(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase = model(UpperCamelCase__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def A ( self : Any , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[Any] ):
"""simple docstring"""
UpperCamelCase = self.type_sequence_label_size
UpperCamelCase = DeiTForImageClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCamelCase = 1
UpperCamelCase = DeiTForImageClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase = model(UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A ( self : Dict ):
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( _a , _a , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
_SCREAMING_SNAKE_CASE = (
{
"""feature-extraction""": DeiTModel,
"""image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
def A ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = DeiTModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ , hidden_size=3_7 )
def A ( self : Union[str, Any] ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds' )
def A ( self : Tuple ):
"""simple docstring"""
pass
def A ( self : Tuple ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(UpperCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCamelCase__ , nn.Linear ) )
def A ( self : str ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(UpperCamelCase__ )
UpperCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase = [*signature.parameters.keys()]
UpperCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , UpperCamelCase__ )
def A ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def A ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__ )
def A ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
def A ( self : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : str , UpperCamelCase__ : str=False ):
"""simple docstring"""
UpperCamelCase = super()._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def A ( self : List[str] ):
"""simple docstring"""
if not self.model_tester.is_training:
return
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(UpperCamelCase__ )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
UpperCamelCase = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.train()
UpperCamelCase = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
UpperCamelCase = model(**UpperCamelCase__ ).loss
loss.backward()
def A ( self : int ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
UpperCamelCase = False
UpperCamelCase = True
for model_class in self.all_model_classes:
if model_class in get_values(UpperCamelCase__ ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
UpperCamelCase = model_class(UpperCamelCase__ )
model.gradient_checkpointing_enable()
model.to(UpperCamelCase__ )
model.train()
UpperCamelCase = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
UpperCamelCase = model(**UpperCamelCase__ ).loss
loss.backward()
def A ( self : List[str] ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = [
{'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
{'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
{'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(UpperCamelCase__ ),
*get_values(UpperCamelCase__ ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}""" ):
UpperCamelCase = problem_type['title']
UpperCamelCase = problem_type['num_labels']
UpperCamelCase = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.train()
UpperCamelCase = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
if problem_type["num_labels"] > 1:
UpperCamelCase = inputs['labels'].unsqueeze(1 ).repeat(1 , problem_type['num_labels'] )
UpperCamelCase = inputs['labels'].to(problem_type['dtype'] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=UpperCamelCase__ ) as warning_list:
UpperCamelCase = model(**UpperCamelCase__ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"""Something is going wrong in the regression problem: intercepted {w.message}""" )
loss.backward()
@slow
def A ( self : int ):
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = DeiTModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def __lowerCamelCase ( ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def A ( self : Optional[int] ):
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224' )
if is_vision_available()
else None
)
@slow
def A ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224' ).to(
UpperCamelCase__ )
UpperCamelCase = self.default_image_processor
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(images=UpperCamelCase__ , return_tensors='pt' ).to(UpperCamelCase__ )
# forward pass
with torch.no_grad():
UpperCamelCase = model(**UpperCamelCase__ )
# verify the logits
UpperCamelCase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , UpperCamelCase__ )
UpperCamelCase = torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1] ).to(UpperCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCamelCase__ , atol=1E-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase = DeiTModel.from_pretrained(
'facebook/deit-base-distilled-patch16-224' , torch_dtype=torch.floataa , device_map='auto' )
UpperCamelCase = self.default_image_processor
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(images=UpperCamelCase__ , return_tensors='pt' )
UpperCamelCase = inputs.pixel_values.to(UpperCamelCase__ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
UpperCamelCase = model(UpperCamelCase__ )
| 363
|
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowerCamelCase ( A__ , A__ , A__ , A__ , A__ ) -> Optional[Any]:
"""simple docstring"""
# Load configuration defined in the metadata file
with open(A__ ) as metadata_file:
UpperCamelCase = json.load(A__ )
UpperCamelCase = LukeConfig(use_entity_aware_attention=A__ , **metadata['model_config'] )
# Load in the weights from the checkpoint_path
UpperCamelCase = torch.load(A__ , map_location='cpu' )['module']
# Load the entity vocab file
UpperCamelCase = load_original_entity_vocab(A__ )
# add an entry for [MASK2]
UpperCamelCase = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
UpperCamelCase = XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )
# Add special tokens to the token vocabulary for downstream tasks
UpperCamelCase = AddedToken('<ent>' , lstrip=A__ , rstrip=A__ )
UpperCamelCase = AddedToken('<ent2>' , lstrip=A__ , rstrip=A__ )
tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F"""Saving tokenizer to {pytorch_dump_folder_path}""" )
tokenizer.save_pretrained(A__ )
with open(os.path.join(A__ , 'tokenizer_config.json' ) , 'r' ) as f:
UpperCamelCase = json.load(A__ )
UpperCamelCase = 'MLukeTokenizer'
with open(os.path.join(A__ , 'tokenizer_config.json' ) , 'w' ) as f:
json.dump(A__ , A__ )
with open(os.path.join(A__ , MLukeTokenizer.vocab_files_names['entity_vocab_file'] ) , 'w' ) as f:
json.dump(A__ , A__ )
UpperCamelCase = MLukeTokenizer.from_pretrained(A__ )
# Initialize the embeddings of the special tokens
UpperCamelCase = tokenizer.convert_tokens_to_ids(['@'] )[0]
UpperCamelCase = tokenizer.convert_tokens_to_ids(['#'] )[0]
UpperCamelCase = state_dict['embeddings.word_embeddings.weight']
UpperCamelCase = word_emb[ent_init_index].unsqueeze(0 )
UpperCamelCase = word_emb[enta_init_index].unsqueeze(0 )
UpperCamelCase = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
UpperCamelCase = state_dict[bias_name]
UpperCamelCase = decoder_bias[ent_init_index].unsqueeze(0 )
UpperCamelCase = decoder_bias[enta_init_index].unsqueeze(0 )
UpperCamelCase = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
UpperCamelCase = F"""encoder.layer.{layer_index}.attention.self."""
UpperCamelCase = state_dict[prefix + matrix_name]
UpperCamelCase = state_dict[prefix + matrix_name]
UpperCamelCase = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
UpperCamelCase = state_dict['entity_embeddings.entity_embeddings.weight']
UpperCamelCase = entity_emb[entity_vocab['[MASK]']].unsqueeze(0 )
UpperCamelCase = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
UpperCamelCase = state_dict['entity_predictions.bias']
UpperCamelCase = entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0 )
UpperCamelCase = torch.cat([entity_prediction_bias, entity_mask_bias] )
UpperCamelCase = LukeForMaskedLM(config=A__ ).eval()
state_dict.pop('entity_predictions.decoder.weight' )
state_dict.pop('lm_head.decoder.weight' )
state_dict.pop('lm_head.decoder.bias' )
UpperCamelCase = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('lm_head' ) or key.startswith('entity_predictions' )):
UpperCamelCase = state_dict[key]
else:
UpperCamelCase = state_dict[key]
UpperCamelCase , UpperCamelCase = model.load_state_dict(A__ , strict=A__ )
if set(A__ ) != {"luke.embeddings.position_ids"}:
raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}""" )
if set(A__ ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F"""Unexpected missing_keys: {missing_keys}""" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
UpperCamelCase = MLukeTokenizer.from_pretrained(A__ , task='entity_classification' )
UpperCamelCase = 'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
UpperCamelCase = (0, 9)
UpperCamelCase = tokenizer(A__ , entity_spans=[span] , return_tensors='pt' )
UpperCamelCase = model(**A__ )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
UpperCamelCase = torch.Size((1, 33, 768) )
UpperCamelCase = torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , A__ , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
UpperCamelCase = torch.Size((1, 1, 768) )
UpperCamelCase = torch.tensor([[-0.1_482, 0.0_609, 0.0_322]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
F""" {expected_shape}""" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , A__ , atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
UpperCamelCase = MLukeTokenizer.from_pretrained(A__ )
UpperCamelCase = 'Tokyo is the capital of <mask>.'
UpperCamelCase = (24, 30)
UpperCamelCase = tokenizer(A__ , entity_spans=[span] , return_tensors='pt' )
UpperCamelCase = model(**A__ )
UpperCamelCase = encoding['input_ids'][0].tolist()
UpperCamelCase = input_ids.index(tokenizer.convert_tokens_to_ids('<mask>' ) )
UpperCamelCase = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(A__ )
UpperCamelCase = outputs.entity_logits[0][0].argmax().item()
UpperCamelCase = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('en:' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('Saving PyTorch model to {}'.format(A__ ) )
model.save_pretrained(A__ )
def __lowerCamelCase ( A__ ) -> int:
"""simple docstring"""
UpperCamelCase = ['[MASK]', '[PAD]', '[UNK]']
UpperCamelCase = [json.loads(A__ ) for line in open(A__ )]
UpperCamelCase = {}
for entry in data:
UpperCamelCase = entry['id']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
UpperCamelCase = entity_id
break
UpperCamelCase = F"""{language}:{entity_name}"""
UpperCamelCase = entity_id
return new_mapping
if __name__ == "__main__":
_lowerCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
parser.add_argument(
"--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
)
parser.add_argument(
"--entity_vocab_path",
default=None,
type=str,
help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
)
parser.add_argument(
"--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
)
_lowerCamelCase : Optional[Any] = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 249
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ : int = {'processing_layoutxlm': ['LayoutXLMProcessor']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = ['LayoutXLMTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[Any] = ['LayoutXLMTokenizerFast']
if TYPE_CHECKING:
from .processing_layoutxlm import LayoutXLMProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm import LayoutXLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
else:
import sys
UpperCamelCase__ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 344
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Optional[int] = {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
),
'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
),
'distilbert-base-uncased-finetuned-sst-2-english': (
'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
),
}
class _lowerCAmelCase ( __A ):
"""simple docstring"""
lowerCamelCase = '''distilbert'''
lowerCamelCase = {
'''hidden_size''': '''dim''',
'''num_attention_heads''': '''n_heads''',
'''num_hidden_layers''': '''n_layers''',
}
def __init__( self , _lowerCamelCase=3_0522 , _lowerCamelCase=512 , _lowerCamelCase=False , _lowerCamelCase=6 , _lowerCamelCase=12 , _lowerCamelCase=768 , _lowerCamelCase=4 * 768 , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase="gelu" , _lowerCamelCase=0.02 , _lowerCamelCase=0.1 , _lowerCamelCase=0.2 , _lowerCamelCase=0 , **_lowerCamelCase , ) -> Optional[Any]:
A_ : Tuple = vocab_size
A_ : List[Any] = max_position_embeddings
A_ : int = sinusoidal_pos_embds
A_ : int = n_layers
A_ : str = n_heads
A_ : Optional[int] = dim
A_ : int = hidden_dim
A_ : Tuple = dropout
A_ : List[Any] = attention_dropout
A_ : int = activation
A_ : Dict = initializer_range
A_ : List[Any] = qa_dropout
A_ : int = seq_classif_dropout
super().__init__(**_lowerCamelCase , pad_token_id=_lowerCamelCase )
class _lowerCAmelCase ( __A ):
"""simple docstring"""
@property
def UpperCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
A_ : Union[str, Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A_ : int = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 344
| 1
|
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_UpperCAmelCase : str = re.compile(R"\b(a|an|the)\b", re.UNICODE)
_UpperCAmelCase : List[Any] = None
def A ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase = argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.' )
parser.add_argument('data_file' , metavar='data.json' , help='Input data JSON file.' )
parser.add_argument('pred_file' , metavar='pred.json' , help='Model predictions.' )
parser.add_argument(
'--out-file' , '-o' , metavar='eval.json' , help='Write accuracy metrics to file (default is stdout).' )
parser.add_argument(
'--na-prob-file' , '-n' , metavar='na_prob.json' , help='Model estimates of probability of no answer.' )
parser.add_argument(
'--na-prob-thresh' , '-t' , type=lowercase , default=1.0 , help='Predict "" if no-answer probability exceeds this (default = 1.0).' , )
parser.add_argument(
'--out-image-dir' , '-p' , metavar='out_images' , default=lowercase , help='Save precision-recall curves to directory.' )
parser.add_argument('--verbose' , '-v' , action='store_true' )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def A ( lowercase ) -> str:
'''simple docstring'''
UpperCamelCase = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
UpperCamelCase = bool(qa['answers']['text'] )
return qid_to_has_ans
def A ( lowercase ) -> Union[str, Any]:
'''simple docstring'''
def remove_articles(lowercase ):
return ARTICLES_REGEX.sub(' ' , lowercase )
def white_space_fix(lowercase ):
return " ".join(text.split() )
def remove_punc(lowercase ):
UpperCamelCase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase ) ) ) )
def A ( lowercase ) -> int:
'''simple docstring'''
if not s:
return []
return normalize_answer(lowercase ).split()
def A ( lowercase , lowercase ) -> Any:
'''simple docstring'''
return int(normalize_answer(lowercase ) == normalize_answer(lowercase ) )
def A ( lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
UpperCamelCase = get_tokens(lowercase )
UpperCamelCase = get_tokens(lowercase )
UpperCamelCase = collections.Counter(lowercase ) & collections.Counter(lowercase )
UpperCamelCase = sum(common.values() )
if len(lowercase ) == 0 or len(lowercase ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
UpperCamelCase = 1.0 * num_same / len(lowercase )
UpperCamelCase = 1.0 * num_same / len(lowercase )
UpperCamelCase = (2 * precision * recall) / (precision + recall)
return fa
def A ( lowercase , lowercase ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase = {}
UpperCamelCase = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
UpperCamelCase = qa['id']
UpperCamelCase = [t for t in qa['answers']['text'] if normalize_answer(lowercase )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
UpperCamelCase = ['']
if qid not in preds:
print(f'''Missing prediction for {qid}''' )
continue
UpperCamelCase = preds[qid]
# Take max over all gold answers
UpperCamelCase = max(compute_exact(lowercase , lowercase ) for a in gold_answers )
UpperCamelCase = max(compute_fa(lowercase , lowercase ) for a in gold_answers )
return exact_scores, fa_scores
def A ( lowercase , lowercase , lowercase , lowercase ) -> str:
'''simple docstring'''
UpperCamelCase = {}
for qid, s in scores.items():
UpperCamelCase = na_probs[qid] > na_prob_thresh
if pred_na:
UpperCamelCase = float(not qid_to_has_ans[qid] )
else:
UpperCamelCase = s
return new_scores
def A ( lowercase , lowercase , lowercase=None ) -> Dict:
'''simple docstring'''
if not qid_list:
UpperCamelCase = len(lowercase )
return collections.OrderedDict(
[
('exact', 100.0 * sum(exact_scores.values() ) / total),
('f1', 100.0 * sum(fa_scores.values() ) / total),
('total', total),
] )
else:
UpperCamelCase = len(lowercase )
return collections.OrderedDict(
[
('exact', 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
('f1', 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
('total', total),
] )
def A ( lowercase , lowercase , lowercase ) -> Tuple:
'''simple docstring'''
for k in new_eval:
UpperCamelCase = new_eval[k]
def A ( lowercase , lowercase , lowercase , lowercase ) -> Tuple:
'''simple docstring'''
plt.step(lowercase , lowercase , color='b' , alpha=0.2 , where='post' )
plt.fill_between(lowercase , lowercase , step='post' , alpha=0.2 , color='b' )
plt.xlabel('Recall' )
plt.ylabel('Precision' )
plt.xlim([0.0, 1.0_5] )
plt.ylim([0.0, 1.0_5] )
plt.title(lowercase )
plt.savefig(lowercase )
plt.clf()
def A ( lowercase , lowercase , lowercase , lowercase , lowercase=None , lowercase=None ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase = sorted(lowercase , key=lambda lowercase : na_probs[k] )
UpperCamelCase = 0.0
UpperCamelCase = 1.0
UpperCamelCase = 0.0
UpperCamelCase = [1.0]
UpperCamelCase = [0.0]
UpperCamelCase = 0.0
for i, qid in enumerate(lowercase ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
UpperCamelCase = true_pos / float(i + 1 )
UpperCamelCase = true_pos / float(lowercase )
if i == len(lowercase ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(lowercase )
recalls.append(lowercase )
if out_image:
plot_pr_curve(lowercase , lowercase , lowercase , lowercase )
return {"ap": 100.0 * avg_prec}
def A ( lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Tuple:
'''simple docstring'''
if out_image_dir and not os.path.exists(lowercase ):
os.makedirs(lowercase )
UpperCamelCase = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
UpperCamelCase = make_precision_recall_eval(
lowercase , lowercase , lowercase , lowercase , out_image=os.path.join(lowercase , 'pr_exact.png' ) , title='Precision-Recall curve for Exact Match score' , )
UpperCamelCase = make_precision_recall_eval(
lowercase , lowercase , lowercase , lowercase , out_image=os.path.join(lowercase , 'pr_f1.png' ) , title='Precision-Recall curve for F1 score' , )
UpperCamelCase = {k: float(lowercase ) for k, v in qid_to_has_ans.items()}
UpperCamelCase = make_precision_recall_eval(
lowercase , lowercase , lowercase , lowercase , out_image=os.path.join(lowercase , 'pr_oracle.png' ) , title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)' , )
merge_eval(lowercase , lowercase , 'pr_exact' )
merge_eval(lowercase , lowercase , 'pr_f1' )
merge_eval(lowercase , lowercase , 'pr_oracle' )
def A ( lowercase , lowercase , lowercase , lowercase ) -> str:
'''simple docstring'''
if not qid_list:
return
UpperCamelCase = [na_probs[k] for k in qid_list]
UpperCamelCase = np.ones_like(lowercase ) / float(len(lowercase ) )
plt.hist(lowercase , weights=lowercase , bins=20 , range=(0.0, 1.0) )
plt.xlabel('Model probability of no-answer' )
plt.ylabel('Proportion of dataset' )
plt.title(f'''Histogram of no-answer probability: {name}''' )
plt.savefig(os.path.join(lowercase , f'''na_prob_hist_{name}.png''' ) )
plt.clf()
def A ( lowercase , lowercase , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
UpperCamelCase = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
UpperCamelCase = num_no_ans
UpperCamelCase = cur_score
UpperCamelCase = 0.0
UpperCamelCase = sorted(lowercase , key=lambda lowercase : na_probs[k] )
for i, qid in enumerate(lowercase ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
UpperCamelCase = scores[qid]
else:
if preds[qid]:
UpperCamelCase = -1
else:
UpperCamelCase = 0
cur_score += diff
if cur_score > best_score:
UpperCamelCase = cur_score
UpperCamelCase = na_probs[qid]
return 100.0 * best_score / len(lowercase ), best_thresh
def A ( lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> List[str]:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = find_best_thresh(lowercase , lowercase , lowercase , lowercase )
UpperCamelCase , UpperCamelCase = find_best_thresh(lowercase , lowercase , lowercase , lowercase )
UpperCamelCase = best_exact
UpperCamelCase = exact_thresh
UpperCamelCase = best_fa
UpperCamelCase = fa_thresh
def A ( ) -> List[str]:
'''simple docstring'''
with open(OPTS.data_file ) as f:
UpperCamelCase = json.load(lowercase )
UpperCamelCase = dataset_json['data']
with open(OPTS.pred_file ) as f:
UpperCamelCase = json.load(lowercase )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
UpperCamelCase = json.load(lowercase )
else:
UpperCamelCase = {k: 0.0 for k in preds}
UpperCamelCase = make_qid_to_has_ans(lowercase ) # maps qid to True/False
UpperCamelCase = [k for k, v in qid_to_has_ans.items() if v]
UpperCamelCase = [k for k, v in qid_to_has_ans.items() if not v]
UpperCamelCase , UpperCamelCase = get_raw_scores(lowercase , lowercase )
UpperCamelCase = apply_no_ans_threshold(lowercase , lowercase , lowercase , OPTS.na_prob_thresh )
UpperCamelCase = apply_no_ans_threshold(lowercase , lowercase , lowercase , OPTS.na_prob_thresh )
UpperCamelCase = make_eval_dict(lowercase , lowercase )
if has_ans_qids:
UpperCamelCase = make_eval_dict(lowercase , lowercase , qid_list=lowercase )
merge_eval(lowercase , lowercase , 'HasAns' )
if no_ans_qids:
UpperCamelCase = make_eval_dict(lowercase , lowercase , qid_list=lowercase )
merge_eval(lowercase , lowercase , 'NoAns' )
if OPTS.na_prob_file:
find_all_best_thresh(lowercase , lowercase , lowercase , lowercase , lowercase , lowercase )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(lowercase , lowercase , lowercase , lowercase , lowercase , OPTS.out_image_dir )
histogram_na_prob(lowercase , lowercase , OPTS.out_image_dir , 'hasAns' )
histogram_na_prob(lowercase , lowercase , OPTS.out_image_dir , 'noAns' )
if OPTS.out_file:
with open(OPTS.out_file , 'w' ) as f:
json.dump(lowercase , lowercase )
else:
print(json.dumps(lowercase , indent=2 ) )
if __name__ == "__main__":
_UpperCAmelCase : List[str] = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 360
|
_UpperCAmelCase : str = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
_UpperCAmelCase : Any = [
999,
976,
952,
928,
905,
882,
858,
857,
810,
762,
715,
714,
572,
429,
428,
286,
285,
238,
190,
143,
142,
118,
95,
71,
47,
24,
0,
]
_UpperCAmelCase : List[Any] = [
999,
988,
977,
966,
955,
944,
933,
922,
911,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
350,
300,
299,
266,
233,
200,
199,
179,
159,
140,
120,
100,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
_UpperCAmelCase : Tuple = [
999,
995,
992,
989,
985,
981,
978,
975,
971,
967,
964,
961,
957,
956,
951,
947,
942,
937,
933,
928,
923,
919,
914,
913,
908,
903,
897,
892,
887,
881,
876,
871,
870,
864,
858,
852,
846,
840,
834,
828,
827,
820,
813,
806,
799,
792,
785,
784,
777,
770,
763,
756,
749,
742,
741,
733,
724,
716,
707,
699,
698,
688,
677,
666,
656,
655,
645,
634,
623,
613,
612,
598,
584,
570,
569,
555,
541,
527,
526,
505,
484,
483,
462,
440,
439,
396,
395,
352,
351,
308,
307,
264,
263,
220,
219,
176,
132,
88,
44,
0,
]
_UpperCAmelCase : Union[str, Any] = [
999,
997,
995,
992,
990,
988,
986,
984,
981,
979,
977,
975,
972,
970,
968,
966,
964,
961,
959,
957,
956,
954,
951,
949,
946,
944,
941,
939,
936,
934,
931,
929,
926,
924,
921,
919,
916,
914,
913,
910,
907,
905,
902,
899,
896,
893,
891,
888,
885,
882,
879,
877,
874,
871,
870,
867,
864,
861,
858,
855,
852,
849,
846,
843,
840,
837,
834,
831,
828,
827,
824,
821,
817,
814,
811,
808,
804,
801,
798,
795,
791,
788,
785,
784,
780,
777,
774,
770,
766,
763,
760,
756,
752,
749,
746,
742,
741,
737,
733,
730,
726,
722,
718,
714,
710,
707,
703,
699,
698,
694,
690,
685,
681,
677,
673,
669,
664,
660,
656,
655,
650,
646,
641,
636,
632,
627,
622,
618,
613,
612,
607,
602,
596,
591,
586,
580,
575,
570,
569,
563,
557,
551,
545,
539,
533,
527,
526,
519,
512,
505,
498,
491,
484,
483,
474,
466,
457,
449,
440,
439,
428,
418,
407,
396,
395,
381,
366,
352,
351,
330,
308,
307,
286,
264,
263,
242,
220,
219,
176,
175,
132,
131,
88,
44,
0,
]
_UpperCAmelCase : List[str] = [
999,
991,
982,
974,
966,
958,
950,
941,
933,
925,
916,
908,
900,
899,
874,
850,
825,
800,
799,
700,
600,
500,
400,
300,
200,
100,
0,
]
_UpperCAmelCase : Tuple = [
999,
992,
985,
978,
971,
964,
957,
949,
942,
935,
928,
921,
914,
907,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
300,
299,
200,
199,
100,
99,
0,
]
_UpperCAmelCase : Dict = [
999,
996,
992,
989,
985,
982,
979,
975,
972,
968,
965,
961,
958,
955,
951,
948,
944,
941,
938,
934,
931,
927,
924,
920,
917,
914,
910,
907,
903,
900,
899,
891,
884,
876,
869,
861,
853,
846,
838,
830,
823,
815,
808,
800,
799,
788,
777,
766,
755,
744,
733,
722,
711,
700,
699,
688,
677,
666,
655,
644,
633,
622,
611,
600,
599,
585,
571,
557,
542,
528,
514,
500,
499,
485,
471,
457,
442,
428,
414,
400,
399,
379,
359,
340,
320,
300,
299,
279,
259,
240,
220,
200,
199,
166,
133,
100,
99,
66,
33,
0,
]
| 110
| 0
|
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _a (unittest.TestCase ):
'''simple docstring'''
@property
def __A ( self ):
torch.manual_seed(0 )
A__ : Tuple = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
def __A ( self ):
A__ : List[Any] = self.dummy_uncond_unet
A__ : Dict = PNDMScheduler()
A__ : int = PNDMPipeline(unet=A__ , scheduler=A__ )
pndm.to(A__ )
pndm.set_progress_bar_config(disable=A__ )
A__ : Dict = torch.manual_seed(0 )
A__ : List[Any] = pndm(generator=A__ , num_inference_steps=20 , output_type="""numpy""" ).images
A__ : List[str] = torch.manual_seed(0 )
A__ : str = pndm(generator=A__ , num_inference_steps=20 , output_type="""numpy""" , return_dict=A__ )[0]
A__ : Any = image[0, -3:, -3:, -1]
A__ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A__ : Dict = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _a (unittest.TestCase ):
'''simple docstring'''
def __A ( self ):
A__ : int = """google/ddpm-cifar10-32"""
A__ : Union[str, Any] = UNetaDModel.from_pretrained(A__ )
A__ : int = PNDMScheduler()
A__ : List[Any] = PNDMPipeline(unet=A__ , scheduler=A__ )
pndm.to(A__ )
pndm.set_progress_bar_config(disable=A__ )
A__ : Optional[int] = torch.manual_seed(0 )
A__ : Union[str, Any] = pndm(generator=A__ , output_type="""numpy""" ).images
A__ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A__ : int = np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 192
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = [
'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'IBertForMaskedLM',
'IBertForMultipleChoice',
'IBertForQuestionAnswering',
'IBertForSequenceClassification',
'IBertForTokenClassification',
'IBertModel',
'IBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
A_ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 192
| 1
|
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__a :List[str] = TypeVar('T')
class _a ( Generic[T] ):
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase : T ):
A_ = data
A_ = None
def __str__( self : Union[str, Any] ):
return f'''{self.data}'''
class _a ( Generic[T] ):
"""simple docstring"""
def __init__( self : Union[str, Any] ):
A_ = None
def __iter__( self : Dict ):
A_ = self.top
while node:
yield node.data
A_ = node.next
def __str__( self : Tuple ):
return "->".join([str(UpperCAmelCase ) for item in self] )
def __len__( self : Dict ):
return len(tuple(iter(self ) ) )
def __A ( self : Optional[int] ):
return self.top is None
def __A ( self : Dict , UpperCAmelCase : T ):
A_ = Node(UpperCAmelCase )
if not self.is_empty():
A_ = self.top
A_ = node
def __A ( self : Any ):
if self.is_empty():
raise IndexError("pop from empty stack" )
assert isinstance(self.top , UpperCAmelCase )
A_ = self.top
A_ = self.top.next
return pop_node.data
def __A ( self : Union[str, Any] ):
if self.is_empty():
raise IndexError("peek from empty stack" )
assert self.top is not None
return self.top.data
def __A ( self : List[str] ):
A_ = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 329
|
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : str ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Tuple , *UpperCAmelCase : Tuple , **UpperCAmelCase : Union[str, Any] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Dict , *UpperCAmelCase : List[Any] , **UpperCAmelCase : Tuple ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Tuple = ['torch', 'transformers', 'onnx']
def __init__( self : Optional[Any] , *UpperCAmelCase : List[Any] , **UpperCAmelCase : List[Any] ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : List[Any] , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : str ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Tuple , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Any = ['torch', 'transformers', 'onnx']
def __init__( self : Dict , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : Optional[int] ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Union[str, Any] , *UpperCAmelCase : Tuple , **UpperCAmelCase : Optional[int] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Tuple , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[str] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase : List[Any] , **UpperCAmelCase : int ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Any , *UpperCAmelCase : List[Any] , **UpperCAmelCase : str ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Optional[int] , *UpperCAmelCase : str , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Dict = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase : int , **UpperCAmelCase : Tuple ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Optional[int] , *UpperCAmelCase : str , **UpperCAmelCase : Dict ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : int , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : List[str] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase : str , **UpperCAmelCase : List[Any] ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : List[Any] , *UpperCAmelCase : Optional[Any] , **UpperCAmelCase : List[Any] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Optional[int] , *UpperCAmelCase : List[str] , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
| 329
| 1
|
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
if isinstance(lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : Any = np.full((len(lowercase__ ), sequence_length, 2) , lowercase__ )
else:
__SCREAMING_SNAKE_CASE : List[Any] = np.full((len(lowercase__ ), sequence_length) , lowercase__ )
for i, tensor in enumerate(lowercase__ ):
if padding_side == "right":
if isinstance(lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : Dict = tensor[:sequence_length]
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = tensor[:sequence_length]
else:
if isinstance(lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : List[Any] = tensor[:sequence_length]
else:
__SCREAMING_SNAKE_CASE : Optional[int] = tensor[:sequence_length]
return out_tensor.tolist()
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : Dict = ord(lowercase__ )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
__SCREAMING_SNAKE_CASE : List[str] = unicodedata.category(lowercase__ )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : PreTrainedTokenizerBase
SCREAMING_SNAKE_CASE__ : Union[bool, str, PaddingStrategy] = True
SCREAMING_SNAKE_CASE__ : Optional[int] = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
SCREAMING_SNAKE_CASE__ : int = -100
SCREAMING_SNAKE_CASE__ : str = "pt"
def __magic_name__( self :Tuple , lowerCAmelCase__ :Any ) -> List[Any]:
import torch
__SCREAMING_SNAKE_CASE : int = '''label''' if '''label''' in features[0].keys() else '''labels'''
__SCREAMING_SNAKE_CASE : List[str] = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__SCREAMING_SNAKE_CASE : List[str] = self.tokenizer.pad(
lowerCAmelCase__ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(batch['''entity_ids'''] ).shape[1]
__SCREAMING_SNAKE_CASE : List[str] = self.tokenizer.padding_side
if padding_side == "right":
__SCREAMING_SNAKE_CASE : Optional[int] = [
list(lowerCAmelCase__ ) + [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase__ )) for label in labels
]
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = [
[self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase__ )) + list(lowerCAmelCase__ ) for label in labels
]
__SCREAMING_SNAKE_CASE : Optional[Any] = [feature['''ner_tags'''] for feature in features]
__SCREAMING_SNAKE_CASE : Optional[int] = padding_tensor(lowerCAmelCase__ , -1 , lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[Any] = [feature['''original_entity_spans'''] for feature in features]
__SCREAMING_SNAKE_CASE : int = padding_tensor(lowerCAmelCase__ , (-1, -1) , lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = {k: torch.tensor(lowerCAmelCase__ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 9
|
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCAmelCase_ = logging.getLogger()
@unittest.skip("""Temporarily disable the doc tests.""" )
@require_torch
@require_tf
@slow
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Path , _UpperCAmelCase : Union[str, None] = None , _UpperCAmelCase : Union[List[str], None] = None , _UpperCAmelCase : Union[str, List[str], None] = None , _UpperCAmelCase : bool = True , ):
"""simple docstring"""
UpperCAmelCase__ = [file for file in os.listdir(_UpperCAmelCase ) if os.path.isfile(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) )]
if identifier is not None:
UpperCAmelCase__ = [file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
for n_ in n_identifier:
UpperCAmelCase__ = [file for file in files if n_ not in file]
else:
UpperCAmelCase__ = [file for file in files if n_identifier not in file]
UpperCAmelCase__ = ignore_files or []
ignore_files.append("""__init__.py""" )
UpperCAmelCase__ = [file for file in files if file not in ignore_files]
for file in files:
# Open all files
print("""Testing""" , _UpperCAmelCase )
if only_modules:
UpperCAmelCase__ = file.split(""".""" )[0]
try:
UpperCAmelCase__ = getattr(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = doctest.DocTestSuite(_UpperCAmelCase )
UpperCAmelCase__ = unittest.TextTestRunner().run(_UpperCAmelCase )
self.assertIs(len(result.failures ) , 0 )
except AttributeError:
logger.info(f'''{module_identifier} is not a module.''' )
else:
UpperCAmelCase__ = doctest.testfile(str("""..""" / directory / file ) , optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed , 0 )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = Path("""src/transformers""" )
UpperCAmelCase__ = """modeling"""
UpperCAmelCase__ = [
"""modeling_ctrl.py""",
"""modeling_tf_ctrl.py""",
]
self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase , ignore_files=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = Path("""src/transformers""" )
UpperCAmelCase__ = """tokenization"""
self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = Path("""src/transformers""" )
UpperCAmelCase__ = """configuration"""
self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = Path("""src/transformers""" )
UpperCAmelCase__ = ["""configuration""", """modeling""", """tokenization"""]
self.analyze_directory(_UpperCAmelCase , n_identifier=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = Path("""docs/source""" )
UpperCAmelCase__ = ["""favicon.ico"""]
self.analyze_directory(_UpperCAmelCase , ignore_files=_UpperCAmelCase , only_modules=_UpperCAmelCase )
| 346
| 0
|
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase: List[Any] = logging.get_logger()
@dataclass
class UpperCAmelCase :
_lowerCamelCase : nn.Module
_lowerCamelCase : List[nn.Module] = field(default_factory=SCREAMING_SNAKE_CASE__)
_lowerCamelCase : list = field(default_factory=SCREAMING_SNAKE_CASE__)
def lowercase_ ( self : Union[str, Any], a_ : List[Any], a_ : Tensor, a_ : Tensor ):
"""simple docstring"""
UpperCamelCase__ = len(list(m.modules() ) ) == 1 or isinstance(a_, nn.Convad ) or isinstance(a_, nn.BatchNormad )
if has_not_submodules:
self.traced.append(a_ )
def __call__( self : Union[str, Any], a_ : Tensor ):
"""simple docstring"""
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(a_ )
[x.remove() for x in self.handles]
return self
@property
def lowercase_ ( self : List[Any] ):
"""simple docstring"""
return list(filter(lambda a_ : len(list(x.state_dict().keys() ) ) > 0, self.traced ) )
@dataclass
class UpperCAmelCase :
_lowerCamelCase : nn.Module
_lowerCamelCase : nn.Module
_lowerCamelCase : int = 0
_lowerCamelCase : List = field(default_factory=SCREAMING_SNAKE_CASE__)
_lowerCamelCase : List = field(default_factory=SCREAMING_SNAKE_CASE__)
def __call__( self : str, a_ : Tensor ):
"""simple docstring"""
UpperCamelCase__ = Tracker(self.dest )(a_ ).parametrized
UpperCamelCase__ = Tracker(self.src )(a_ ).parametrized
UpperCamelCase__ = list(filter(lambda a_ : type(a_ ) not in self.src_skip, a_ ) )
UpperCamelCase__ = list(filter(lambda a_ : type(a_ ) not in self.dest_skip, a_ ) )
if len(a_ ) != len(a_ ):
raise Exception(
f'Numbers of operations are different. Source module has {len(a_ )} operations while'
f' destination module has {len(a_ )}.' )
for dest_m, src_m in zip(a_, a_ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : str , _UpperCamelCase : ResNetConfig , _UpperCamelCase : Path , _UpperCamelCase : bool = True ) -> Dict:
'''simple docstring'''
print(F'Converting {name}...' )
with torch.no_grad():
UpperCamelCase__ = timm.create_model(_UpperCamelCase , pretrained=_UpperCamelCase ).eval()
UpperCamelCase__ = ResNetForImageClassification(_UpperCamelCase ).eval()
UpperCamelCase__ = ModuleTransfer(src=_UpperCamelCase , dest=_UpperCamelCase )
UpperCamelCase__ = torch.randn((1, 3, 2_24, 2_24) )
module_transfer(_UpperCamelCase )
assert torch.allclose(from_model(_UpperCamelCase ) , our_model(_UpperCamelCase ).logits ), "The model logits don't match the original one."
UpperCamelCase__ = F'resnet{"-".join(name.split("resnet" ) )}'
print(_UpperCamelCase )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="Add model" , use_temp_dir=_UpperCamelCase , )
# we can use the convnext one
UpperCamelCase__ = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="Add image processor" , use_temp_dir=_UpperCamelCase , )
print(F'Pushed {checkpoint_name}' )
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : Path , _UpperCamelCase : str = None , _UpperCamelCase : bool = True ) -> int:
'''simple docstring'''
UpperCamelCase__ = "imagenet-1k-id2label.json"
UpperCamelCase__ = 10_00
UpperCamelCase__ = (1, num_labels)
UpperCamelCase__ = "huggingface/label-files"
UpperCamelCase__ = num_labels
UpperCamelCase__ = json.load(open(hf_hub_download(_UpperCamelCase , _UpperCamelCase , repo_type="dataset" ) , "r" ) )
UpperCamelCase__ = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
UpperCamelCase__ = idalabel
UpperCamelCase__ = {v: k for k, v in idalabel.items()}
UpperCamelCase__ = partial(_UpperCamelCase , num_labels=_UpperCamelCase , idalabel=_UpperCamelCase , labelaid=_UpperCamelCase )
UpperCamelCase__ = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[64, 1_28, 2_56, 5_12] , layer_type="basic" ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[2_56, 5_12, 10_24, 20_48] , layer_type="bottleneck" ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[64, 1_28, 2_56, 5_12] , layer_type="basic" ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[2_56, 5_12, 10_24, 20_48] , layer_type="bottleneck" ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] , hidden_sizes=[2_56, 5_12, 10_24, 20_48] , layer_type="bottleneck" ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] , hidden_sizes=[2_56, 5_12, 10_24, 20_48] , layer_type="bottleneck" ),
}
if model_name:
convert_weight_and_push(_UpperCamelCase , names_to_config[model_name] , _UpperCamelCase , _UpperCamelCase )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
return config, expected_shape
if __name__ == "__main__":
__lowercase: List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
__lowercase: Any = parser.parse_args()
__lowercase: Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 354
|
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
__lowercase: str = random.Random()
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : List[str] , _UpperCamelCase : Optional[int]=1.0 , _UpperCamelCase : Dict=None , _UpperCamelCase : List[str]=None ) -> Union[str, Any]:
'''simple docstring'''
if rng is None:
UpperCamelCase__ = global_rng
UpperCamelCase__ = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class UpperCAmelCase ( unittest.TestCase):
def __init__( self : List[Any], a_ : List[str], a_ : Any=7, a_ : Dict=400, a_ : str=2000, a_ : List[Any]=24, a_ : int=24, a_ : int=0.0, a_ : Union[str, Any]=1_6000, a_ : Union[str, Any]=True, a_ : Optional[Any]=True, ):
"""simple docstring"""
UpperCamelCase__ = parent
UpperCamelCase__ = batch_size
UpperCamelCase__ = min_seq_length
UpperCamelCase__ = max_seq_length
UpperCamelCase__ = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCamelCase__ = feature_size
UpperCamelCase__ = num_mel_bins
UpperCamelCase__ = padding_value
UpperCamelCase__ = sampling_rate
UpperCamelCase__ = return_attention_mask
UpperCamelCase__ = do_normalize
def lowercase_ ( self : Tuple ):
"""simple docstring"""
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowercase_ ( self : Optional[Any], a_ : Union[str, Any]=False, a_ : Optional[int]=False ):
"""simple docstring"""
def _flatten(a_ : Dict ):
return list(itertools.chain(*a_ ) )
if equal_length:
UpperCamelCase__ = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
UpperCamelCase__ = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff )
]
if numpify:
UpperCamelCase__ = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase):
_lowerCamelCase : Dict = SpeechaTextFeatureExtractor if is_speech_available() else None
def lowercase_ ( self : Any ):
"""simple docstring"""
UpperCamelCase__ = SpeechaTextFeatureExtractionTester(self )
def lowercase_ ( self : Optional[int], a_ : Tuple ):
"""simple docstring"""
self.assertTrue(np.all(np.mean(a_, axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(a_, axis=0 ) - 1 ) < 1e-3 ) )
def lowercase_ ( self : Any ):
"""simple docstring"""
UpperCamelCase__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase__ = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test feature size
UpperCamelCase__ = feature_extractor(a_, padding=a_, return_tensors="np" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
UpperCamelCase__ = feature_extractor(speech_inputs[0], return_tensors="np" ).input_features
UpperCamelCase__ = feature_extractor(np_speech_inputs[0], return_tensors="np" ).input_features
self.assertTrue(np.allclose(a_, a_, atol=1e-3 ) )
# Test batched
UpperCamelCase__ = feature_extractor(a_, return_tensors="np" ).input_features
UpperCamelCase__ = feature_extractor(a_, return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(a_, a_ ):
self.assertTrue(np.allclose(a_, a_, atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
UpperCamelCase__ = [floats_list((1, x) )[0] for x in (800, 800, 800)]
UpperCamelCase__ = np.asarray(a_ )
UpperCamelCase__ = feature_extractor(a_, return_tensors="np" ).input_features
UpperCamelCase__ = feature_extractor(a_, return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(a_, a_ ):
self.assertTrue(np.allclose(a_, a_, atol=1e-3 ) )
def lowercase_ ( self : List[str] ):
"""simple docstring"""
UpperCamelCase__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ = ["longest", "max_length", "do_not_pad"]
UpperCamelCase__ = [None, 16, None]
for max_length, padding in zip(a_, a_ ):
UpperCamelCase__ = feature_extractor(
a_, padding=a_, max_length=a_, return_attention_mask=a_ )
UpperCamelCase__ = inputs.input_features
UpperCamelCase__ = inputs.attention_mask
UpperCamelCase__ = [np.sum(a_ ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowercase_ ( self : Any ):
"""simple docstring"""
UpperCamelCase__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ = ["longest", "max_length", "do_not_pad"]
UpperCamelCase__ = [None, 16, None]
for max_length, padding in zip(a_, a_ ):
UpperCamelCase__ = feature_extractor(
a_, max_length=a_, padding=a_, return_tensors="np", return_attention_mask=a_ )
UpperCamelCase__ = inputs.input_features
UpperCamelCase__ = inputs.attention_mask
UpperCamelCase__ = [np.sum(a_ ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowercase_ ( self : str ):
"""simple docstring"""
UpperCamelCase__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ = feature_extractor(
a_, padding="max_length", max_length=4, truncation=a_, return_tensors="np", return_attention_mask=a_, )
UpperCamelCase__ = inputs.input_features
UpperCamelCase__ = inputs.attention_mask
UpperCamelCase__ = np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def lowercase_ ( self : Any ):
"""simple docstring"""
UpperCamelCase__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ = feature_extractor(
a_, padding="longest", max_length=4, truncation=a_, return_tensors="np", return_attention_mask=a_, )
UpperCamelCase__ = inputs.input_features
UpperCamelCase__ = inputs.attention_mask
UpperCamelCase__ = np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 4, 24) )
UpperCamelCase__ = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ = feature_extractor(
a_, padding="longest", max_length=16, truncation=a_, return_tensors="np", return_attention_mask=a_, )
UpperCamelCase__ = inputs.input_features
UpperCamelCase__ = inputs.attention_mask
UpperCamelCase__ = np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 6, 24) )
def lowercase_ ( self : Optional[Any] ):
"""simple docstring"""
import torch
UpperCamelCase__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ = np.random.rand(100, 32 ).astype(np.floataa )
UpperCamelCase__ = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCamelCase__ = feature_extractor.pad([{"input_features": inputs}], return_tensors="np" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
UpperCamelCase__ = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowercase_ ( self : List[str], a_ : int ):
"""simple docstring"""
from datasets import load_dataset
UpperCamelCase__ = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation" )
# automatic decoding with librispeech
UpperCamelCase__ = ds.sort("id" ).select(range(a_ ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def lowercase_ ( self : int ):
"""simple docstring"""
UpperCamelCase__ = np.array([
-1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
-1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
-1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
] )
# fmt: on
UpperCamelCase__ = self._load_datasamples(1 )
UpperCamelCase__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ = feature_extractor(a_, return_tensors="pt" ).input_features
self.assertEquals(input_features.shape, (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30], a_, atol=1e-4 ) )
| 31
| 0
|
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowercase ):
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__UpperCamelCase ) / len(__UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 66
|
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 249
| 0
|
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=13 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=99 , _UpperCAmelCase=64 , _UpperCAmelCase=5 , _UpperCAmelCase=4 , _UpperCAmelCase=37 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=512 , _UpperCAmelCase=16 , _UpperCAmelCase=2 , _UpperCAmelCase=0.02 , _UpperCAmelCase=3 , _UpperCAmelCase=4 , _UpperCAmelCase=None , ):
'''simple docstring'''
__A : Tuple = parent
__A : Optional[Any] = batch_size
__A : List[Any] = seq_length
__A : int = is_training
__A : int = use_input_mask
__A : Tuple = use_token_type_ids
__A : Dict = use_labels
__A : Optional[Any] = vocab_size
__A : List[str] = hidden_size
__A : Union[str, Any] = num_hidden_layers
__A : Tuple = num_attention_heads
__A : List[str] = intermediate_size
__A : str = hidden_act
__A : str = hidden_dropout_prob
__A : List[Any] = attention_probs_dropout_prob
__A : Dict = max_position_embeddings
__A : Any = type_vocab_size
__A : Union[str, Any] = type_sequence_label_size
__A : Any = initializer_range
__A : List[str] = num_labels
__A : Optional[int] = num_choices
__A : Tuple = scope
__A : Union[str, Any] = vocab_size - 1
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__A : str = None
if self.use_input_mask:
__A : int = random_attention_mask([self.batch_size, self.seq_length])
__A : Any = None
if self.use_labels:
__A : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
__A : str = self.get_config()
return config, input_ids, input_mask, token_labels
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
return GPTNeoXConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , )
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A ,__A ,__A ,__A : Dict = self.prepare_config_and_inputs()
__A : List[Any] = True
return config, input_ids, input_mask, token_labels
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : int = GPTNeoXModel(config=_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
__A : List[str] = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase)
__A : List[str] = model(_UpperCAmelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : Any = True
__A : Any = GPTNeoXModel(_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
__A : List[str] = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : Tuple = GPTNeoXForCausalLM(config=_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
__A : int = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : Any = self.num_labels
__A : List[str] = GPTNeoXForQuestionAnswering(_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
__A : int = model(_UpperCAmelCase , attention_mask=_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 SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : Optional[Any] = self.num_labels
__A : Tuple = GPTNeoXForSequenceClassification(_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
__A : int = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__A : Optional[Any] = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : Any = self.num_labels
__A : Tuple = GPTNeoXForTokenClassification(_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
__A : Any = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : Optional[Any] = True
__A : List[Any] = GPTNeoXForCausalLM(config=_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
# first forward pass
__A : List[Any] = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , use_cache=_UpperCAmelCase)
__A : Any = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__A : List[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size)
__A : List[str] = ids_tensor((self.batch_size, 3) , vocab_size=2)
# append to next input_ids and
__A : str = torch.cat([input_ids, next_tokens] , dim=-1)
__A : Union[str, Any] = torch.cat([input_mask, next_mask] , dim=-1)
__A : List[Any] = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , output_hidden_states=_UpperCAmelCase)
__A : Dict = output_from_no_past['hidden_states'][0]
__A : List[str] = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , past_key_values=_UpperCAmelCase , output_hidden_states=_UpperCAmelCase , )['hidden_states'][0]
# select random slice
__A : Optional[Any] = ids_tensor((1,) , output_from_past.shape[-1]).item()
__A : int = output_from_no_past[:, -3:, random_slice_idx].detach()
__A : Tuple = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-3))
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[str] = self.prepare_config_and_inputs()
__A ,__A ,__A ,__A : Union[str, Any] = config_and_inputs
__A : Dict = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE (a__ , a__ , a__ , unittest.TestCase ):
lowerCAmelCase = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase = (GPTNeoXForCausalLM,) if is_torch_available() else ()
lowerCAmelCase = (
{
'''feature-extraction''': GPTNeoXModel,
'''question-answering''': GPTNeoXForQuestionAnswering,
'''text-classification''': GPTNeoXForSequenceClassification,
'''text-generation''': GPTNeoXForCausalLM,
'''token-classification''': GPTNeoXForTokenClassification,
'''zero-shot''': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Union[str, Any] = GPTNeoXModelTester(self)
__A : Tuple = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=64 , num_attention_heads=8)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A ,__A ,__A ,__A : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A ,__A ,__A ,__A : Any = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A ,__A ,__A ,__A : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
__A : List[Any] = None
self.model_tester.create_and_check_model_as_decoder(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A ,__A ,__A ,__A : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase)
@unittest.skip(reason='Feed forward chunking is not implemented')
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
pass
@parameterized.expand([('linear',), ('dynamic',)])
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
__A ,__A : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
__A : Tuple = ids_tensor([1, 10] , config.vocab_size)
__A : Union[str, Any] = ids_tensor([1, int(config.max_position_embeddings * 1.5)] , config.vocab_size)
set_seed(42) # Fixed seed at init time so the two models get the same random weights
__A : List[Any] = GPTNeoXModel(_UpperCAmelCase)
original_model.to(_UpperCAmelCase)
original_model.eval()
__A : str = original_model(_UpperCAmelCase).last_hidden_state
__A : Dict = original_model(_UpperCAmelCase).last_hidden_state
set_seed(42) # Fixed seed at init time so the two models get the same random weights
__A : Union[str, Any] = {'type': scaling_type, 'factor': 10.0}
__A : Dict = GPTNeoXModel(_UpperCAmelCase)
scaled_model.to(_UpperCAmelCase)
scaled_model.eval()
__A : Dict = scaled_model(_UpperCAmelCase).last_hidden_state
__A : Dict = scaled_model(_UpperCAmelCase).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-5))
else:
self.assertFalse(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-5))
# The output should be different for long inputs
self.assertFalse(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-5))
@require_torch
class SCREAMING_SNAKE_CASE (unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : str = AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped')
for checkpointing in [True, False]:
__A : List[Any] = GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped')
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(_UpperCAmelCase)
__A : Any = tokenizer('My favorite food is' , return_tensors='pt').to(_UpperCAmelCase)
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
__A : int = 'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'
__A : str = model.generate(**_UpperCAmelCase , do_sample=_UpperCAmelCase , max_new_tokens=20)
__A : int = tokenizer.batch_decode(_UpperCAmelCase)[0]
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase)
| 190
|
'''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowercase__ : Optional[int] = HfApi()
lowercase__ : Dict = {}
# fmt: off
lowercase__ : List[str] = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
lowercase__ : Tuple = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
lowercase__ : Optional[Any] = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
lowercase__ : List[Any] = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
lowercase__ : Dict = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
lowercase__ : Optional[int] = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
lowercase__ : List[Any] = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
lowercase__ : List[str] = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
lowercase__ : Dict = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
lowercase__ : Optional[int] = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
lowercase__ : List[str] = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
lowercase__ : Optional[int] = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
lowercase__ : int = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
lowercase__ : int = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
lowercase__ : List[Any] = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
lowercase__ : str = api.list_models(filter='''diffusers''')
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
lowercase__ : int = '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]
print(f"""Started running {mod.modelId}!!!""")
if mod.modelId.startswith('''CompVis'''):
lowercase__ : Optional[Any] = UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
else:
lowercase__ : Tuple = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
lowercase__ : List[str] = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
lowercase__ : int = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
lowercase__ : Tuple = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1e-3
)
print(f"""{mod.modelId} has passed successfully!!!""")
| 190
| 1
|
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowerCAmelCase__ ( UpperCamelCase__ ):
def lowercase ( self : Dict , _lowerCamelCase : str ):
with open(UpperCamelCase_ , encoding='''utf-8''' ) as input_file:
_snake_case = re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)''' )
_snake_case = input_file.read()
_snake_case = regexp.search(UpperCamelCase_ )
return match
def lowercase ( self : List[str] , _lowerCamelCase : str ):
with open(UpperCamelCase_ , encoding='''utf-8''' ) as input_file:
_snake_case = re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()''' , re.DOTALL )
_snake_case = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
_snake_case = regexp.finditer(UpperCamelCase_ )
_snake_case = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def lowercase ( self : List[Any] ):
_snake_case = Path('''./datasets''' )
_snake_case = list(dataset_paths.absolute().glob('''**/*.py''' ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(UpperCamelCase_ ) ):
raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' )
def lowercase ( self : Any ):
_snake_case = Path('''./datasets''' )
_snake_case = list(dataset_paths.absolute().glob('''**/*.py''' ) )
for dataset in dataset_files:
if self._no_print_statements(str(UpperCamelCase_ ) ):
raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 288
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase = ['DPTFeatureExtractor']
lowerCAmelCase = ['DPTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase = [
'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DPTForDepthEstimation',
'DPTForSemanticSegmentation',
'DPTModel',
'DPTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 110
| 0
|
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
A : List[str] = [8, 5, 9, 7]
A : Tuple = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A : Union[str, Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , __a , __a , __a , ):
__lowerCAmelCase = claim_vector
__lowerCAmelCase = allocated_resources_table
__lowerCAmelCase = maximum_claim_table
def snake_case ( self ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def snake_case ( self ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def snake_case ( self ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def snake_case ( self ):
return {self.__need().index(__a ): i for i in self.__need()}
def snake_case ( self , **__a ):
__lowerCAmelCase = self.__need()
__lowerCAmelCase = self.__allocated_resources_table
__lowerCAmelCase = self.__available_resources()
__lowerCAmelCase = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("_" * 50 + "\n" )
while need_list:
__lowerCAmelCase = False
for each_need in need_list:
__lowerCAmelCase = True
for index, need in enumerate(__a ):
if need > available_resources[index]:
__lowerCAmelCase = False
break
if execution:
__lowerCAmelCase = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__lowerCAmelCase = original_need_index
print(f"Process {process_number + 1} is executing." )
# remove the process run from stack
need_list.remove(__a )
# update available/freed resources stack
__lowerCAmelCase = np.array(__a ) + np.array(
alloc_resources_table[process_number] )
print(
"Updated available resource stack for processes: "
+ " ".join([str(__a ) for x in available_resources] ) )
break
if safe:
print("The process is in a safe state.\n" )
else:
print("System in unsafe state. Aborting...\n" )
break
def snake_case ( self ):
print(" " * 9 + "Allocated Resource Table" )
for item in self.__allocated_resources_table:
print(
f"P{self.__allocated_resources_table.index(__a ) + 1}"
+ " ".join(f"{it:>8}" for it in item )
+ "\n" )
print(" " * 9 + "System Resource Table" )
for item in self.__maximum_claim_table:
print(
f"P{self.__maximum_claim_table.index(__a ) + 1}"
+ " ".join(f"{it:>8}" for it in item )
+ "\n" )
print(
"Current Usage by Active Processes: "
+ " ".join(str(__a ) for x in self.__claim_vector ) )
print(
"Initial Available Resources: "
+ " ".join(str(__a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 259
|
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
A : Any = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : int ="""AutoTokenizer"""
__UpperCAmelCase : Union[str, Any] =["""tokenizer"""]
__UpperCAmelCase : Tuple ={
"""semantic_prompt""": 1,
"""coarse_prompt""": 2,
"""fine_prompt""": 2,
}
def __init__( self , __a , __a=None ):
super().__init__(__a )
__lowerCAmelCase = speaker_embeddings
@classmethod
def snake_case ( cls , __a , __a="speaker_embeddings_path.json" , **__a ):
if speaker_embeddings_dict_path is not None:
__lowerCAmelCase = get_file_from_repo(
__a , __a , subfolder=kwargs.pop("subfolder" , __a ) , cache_dir=kwargs.pop("cache_dir" , __a ) , force_download=kwargs.pop("force_download" , __a ) , proxies=kwargs.pop("proxies" , __a ) , resume_download=kwargs.pop("resume_download" , __a ) , local_files_only=kwargs.pop("local_files_only" , __a ) , use_auth_token=kwargs.pop("use_auth_token" , __a ) , revision=kwargs.pop("revision" , __a ) , )
if speaker_embeddings_path is None:
logger.warning(
f"`{os.path.join(__a , __a )}` does not exists\n , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`." )
__lowerCAmelCase = None
else:
with open(__a ) as speaker_embeddings_json:
__lowerCAmelCase = json.load(__a )
else:
__lowerCAmelCase = None
__lowerCAmelCase = AutoTokenizer.from_pretrained(__a , **__a )
return cls(tokenizer=__a , speaker_embeddings=__a )
def snake_case ( self , __a , __a="speaker_embeddings_path.json" , __a="speaker_embeddings" , __a = False , **__a , ):
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(__a , __a , "v2" ) , exist_ok=__a )
__lowerCAmelCase = {}
__lowerCAmelCase = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
__lowerCAmelCase = self._load_voice_preset(__a )
__lowerCAmelCase = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["repo_or_path"] , __a , f"{prompt_key}_{key}" ) , voice_preset[key] , allow_pickle=__a , )
__lowerCAmelCase = os.path.join(__a , f"{prompt_key}_{key}.npy" )
__lowerCAmelCase = tmp_dict
with open(os.path.join(__a , __a ) , "w" ) as fp:
json.dump(__a , __a )
super().save_pretrained(__a , __a , **__a )
def snake_case ( self , __a = None , **__a ):
__lowerCAmelCase = self.speaker_embeddings[voice_preset]
__lowerCAmelCase = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]." )
__lowerCAmelCase = get_file_from_repo(
self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] , subfolder=kwargs.pop("subfolder" , __a ) , cache_dir=kwargs.pop("cache_dir" , __a ) , force_download=kwargs.pop("force_download" , __a ) , proxies=kwargs.pop("proxies" , __a ) , resume_download=kwargs.pop("resume_download" , __a ) , local_files_only=kwargs.pop("local_files_only" , __a ) , use_auth_token=kwargs.pop("use_auth_token" , __a ) , revision=kwargs.pop("revision" , __a ) , )
if path is None:
raise ValueError(
f"`{os.path.join(self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] )}` does not exists\n , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n embeddings." )
__lowerCAmelCase = np.load(__a )
return voice_preset_dict
def snake_case ( self , __a = None ):
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f"Voice preset unrecognized, missing {key} as a key." )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(f"{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray." )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f"{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray." )
def __call__( self , __a=None , __a=None , __a="pt" , __a=2_56 , __a=False , __a=True , __a=False , **__a , ):
if voice_preset is not None and not isinstance(__a , __a ):
if (
isinstance(__a , __a )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
__lowerCAmelCase = self._load_voice_preset(__a )
else:
if isinstance(__a , __a ) and not voice_preset.endswith(".npz" ):
__lowerCAmelCase = voice_preset + ".npz"
__lowerCAmelCase = np.load(__a )
if voice_preset is not None:
self._validate_voice_preset_dict(__a , **__a )
__lowerCAmelCase = BatchFeature(data=__a , tensor_type=__a )
__lowerCAmelCase = self.tokenizer(
__a , return_tensors=__a , padding="max_length" , max_length=__a , return_attention_mask=__a , return_token_type_ids=__a , add_special_tokens=__a , **__a , )
if voice_preset is not None:
__lowerCAmelCase = voice_preset
return encoded_text
| 259
| 1
|
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowerCAmelCase__ :Union[str, Any] = TypeVar('''T''')
class __a ( Generic[T] ):
def __init__( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = data
_UpperCAmelCase = None
def __str__( self ) -> str:
"""simple docstring"""
return f'''{self.data}'''
class __a ( Generic[T] ):
def __init__( self ) -> None:
"""simple docstring"""
_UpperCAmelCase = None
def __iter__( self ) -> Iterator[T]:
"""simple docstring"""
_UpperCAmelCase = self.top
while node:
yield node.data
_UpperCAmelCase = node.next
def __str__( self ) -> str:
"""simple docstring"""
return "->".join([str(_SCREAMING_SNAKE_CASE ) for item in self] )
def __len__( self ) -> int:
"""simple docstring"""
return len(tuple(iter(self ) ) )
def UpperCAmelCase__ ( self ) -> bool:
"""simple docstring"""
return self.top is None
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
_UpperCAmelCase = Node(_SCREAMING_SNAKE_CASE )
if not self.is_empty():
_UpperCAmelCase = self.top
_UpperCAmelCase = node
def UpperCAmelCase__ ( self ) -> T:
"""simple docstring"""
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.top
_UpperCAmelCase = self.top.next
return pop_node.data
def UpperCAmelCase__ ( self ) -> T:
"""simple docstring"""
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def UpperCAmelCase__ ( self ) -> None:
"""simple docstring"""
_UpperCAmelCase = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 329
|
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ :Any = logging.get_logger(__name__)
def lowerCAmelCase__ ( a__: List[Any] , a__: Union[str, Any] , a__: Dict , a__: Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = original_name.split('.' )[0]
_UpperCAmelCase = key.split('.' )
_UpperCAmelCase = int(key_list[key_list.index(a__ ) - 2] )
_UpperCAmelCase = int(key_list[key_list.index(a__ ) - 1] )
_UpperCAmelCase = orig_block_num - offset
_UpperCAmelCase = key.replace(F'''{orig_block_num}.{layer_num}.{original_name}''' , F'''block.{new_block_num}.{layer_num}.{new_name}''' )
return key
def lowerCAmelCase__ ( a__: Tuple ) -> int:
'''simple docstring'''
_UpperCAmelCase = OrderedDict()
_UpperCAmelCase , _UpperCAmelCase = 0, 0
for key, value in state_dict.items():
if key.startswith('network' ):
_UpperCAmelCase = key.replace('network' , 'poolformer.encoder' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('bias' ) and "patch_embed" not in key:
patch_emb_offset += 1
_UpperCAmelCase = key[: key.find('proj' )]
_UpperCAmelCase = key.replace(a__ , F'''patch_embeddings.{total_embed_found}.''' )
_UpperCAmelCase = key.replace('proj' , 'projection' )
if key.endswith('bias' ):
total_embed_found += 1
if "patch_embeddings" in key:
_UpperCAmelCase = 'poolformer.encoder.' + key
if "mlp.fc1" in key:
_UpperCAmelCase = replace_key_with_offset(a__ , a__ , 'mlp.fc1' , 'output.conv1' )
if "mlp.fc2" in key:
_UpperCAmelCase = replace_key_with_offset(a__ , a__ , 'mlp.fc2' , 'output.conv2' )
if "norm1" in key:
_UpperCAmelCase = replace_key_with_offset(a__ , a__ , 'norm1' , 'before_norm' )
if "norm2" in key:
_UpperCAmelCase = replace_key_with_offset(a__ , a__ , 'norm2' , 'after_norm' )
if "layer_scale_1" in key:
_UpperCAmelCase = replace_key_with_offset(a__ , a__ , 'layer_scale_1' , 'layer_scale_1' )
if "layer_scale_2" in key:
_UpperCAmelCase = replace_key_with_offset(a__ , a__ , 'layer_scale_2' , 'layer_scale_2' )
if "head" in key:
_UpperCAmelCase = key.replace('head' , 'classifier' )
_UpperCAmelCase = value
return new_state_dict
def lowerCAmelCase__ ( ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_UpperCAmelCase = Image.open(requests.get(a__ , stream=a__ ).raw )
return image
@torch.no_grad()
def lowerCAmelCase__ ( a__: Optional[int] , a__: Dict , a__: Any ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = PoolFormerConfig()
# set attributes based on model_name
_UpperCAmelCase = 'huggingface/label-files'
_UpperCAmelCase = model_name[-3:]
_UpperCAmelCase = 1_0_0_0
_UpperCAmelCase = 'imagenet-1k-id2label.json'
_UpperCAmelCase = (1, 1_0_0_0)
# set config attributes
_UpperCAmelCase = json.load(open(hf_hub_download(a__ , a__ , repo_type='dataset' ) , 'r' ) )
_UpperCAmelCase = {int(a__ ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
if size == "s12":
_UpperCAmelCase = [2, 2, 6, 2]
_UpperCAmelCase = [6_4, 1_2_8, 3_2_0, 5_1_2]
_UpperCAmelCase = 4.0
_UpperCAmelCase = 0.9
elif size == "s24":
_UpperCAmelCase = [4, 4, 1_2, 4]
_UpperCAmelCase = [6_4, 1_2_8, 3_2_0, 5_1_2]
_UpperCAmelCase = 4.0
_UpperCAmelCase = 0.9
elif size == "s36":
_UpperCAmelCase = [6, 6, 1_8, 6]
_UpperCAmelCase = [6_4, 1_2_8, 3_2_0, 5_1_2]
_UpperCAmelCase = 4.0
_UpperCAmelCase = 1e-6
_UpperCAmelCase = 0.9
elif size == "m36":
_UpperCAmelCase = [6, 6, 1_8, 6]
_UpperCAmelCase = [9_6, 1_9_2, 3_8_4, 7_6_8]
_UpperCAmelCase = 4.0
_UpperCAmelCase = 1e-6
_UpperCAmelCase = 0.95
elif size == "m48":
_UpperCAmelCase = [8, 8, 2_4, 8]
_UpperCAmelCase = [9_6, 1_9_2, 3_8_4, 7_6_8]
_UpperCAmelCase = 4.0
_UpperCAmelCase = 1e-6
_UpperCAmelCase = 0.95
else:
raise ValueError(F'''Size {size} not supported''' )
# load image processor
_UpperCAmelCase = PoolFormerImageProcessor(crop_pct=a__ )
# Prepare image
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=a__ , return_tensors='pt' ).pixel_values
logger.info(F'''Converting model {model_name}...''' )
# load original state dict
_UpperCAmelCase = torch.load(a__ , map_location=torch.device('cpu' ) )
# rename keys
_UpperCAmelCase = rename_keys(a__ )
# create HuggingFace model and load state dict
_UpperCAmelCase = PoolFormerForImageClassification(a__ )
model.load_state_dict(a__ )
model.eval()
# Define image processor
_UpperCAmelCase = PoolFormerImageProcessor(crop_pct=a__ )
_UpperCAmelCase = image_processor(images=prepare_img() , return_tensors='pt' ).pixel_values
# forward pass
_UpperCAmelCase = model(a__ )
_UpperCAmelCase = outputs.logits
# define expected logit slices for different models
if size == "s12":
_UpperCAmelCase = torch.tensor([-0.3_045, -0.6_758, -0.4_869] )
elif size == "s24":
_UpperCAmelCase = torch.tensor([0.4_402, -0.1_374, -0.8_045] )
elif size == "s36":
_UpperCAmelCase = torch.tensor([-0.6_080, -0.5_133, -0.5_898] )
elif size == "m36":
_UpperCAmelCase = torch.tensor([0.3_952, 0.2_263, -1.2_668] )
elif size == "m48":
_UpperCAmelCase = torch.tensor([0.1_167, -0.0_656, -0.3_423] )
else:
raise ValueError(F'''Size {size} not supported''' )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , a__ , atol=1e-2 )
# finally, save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(a__ ).mkdir(exist_ok=a__ )
model.save_pretrained(a__ )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(a__ )
if __name__ == "__main__":
lowerCAmelCase__ :str = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''poolformer_s12''',
type=str,
help='''Name of the model you\'d like to convert.''',
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
lowerCAmelCase__ :Dict = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 329
| 1
|
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase__ = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
UpperCAmelCase__ = [0, 2_5, 5_0]
UpperCAmelCase__ = [2_5, 5_0, 7_5]
UpperCAmelCase__ = fuzz.membership.trimf(X, abca)
UpperCAmelCase__ = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
UpperCAmelCase__ = np.ones(7_5)
UpperCAmelCase__ = np.zeros((7_5,))
# 1. Union = max(µA(x), µB(x))
UpperCAmelCase__ = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
UpperCAmelCase__ = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
UpperCAmelCase__ = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
UpperCAmelCase__ = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
UpperCAmelCase__ = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
UpperCAmelCase__ = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
UpperCAmelCase__ = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
UpperCAmelCase__ = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("""Young""")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("""Middle aged""")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("""union""")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("""intersection""")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("""complement_a""")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("""difference a/b""")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("""alg_sum""")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("""alg_product""")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("""bdd_sum""")
plt.grid(True)
plt.subplot(4, 3, 1_0)
plt.plot(X, bdd_difference)
plt.title("""bdd_difference""")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 30
|
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
UpperCAmelCase__ = logging.get_logger(__name__)
class a ( lowerCAmelCase_ ):
_snake_case : List[Any] = 'vision-encoder-decoder'
_snake_case : Optional[int] = True
def __init__( self : int , **__lowerCAmelCase : Any ):
super().__init__(**__lowerCAmelCase )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f'''A configuraton of type {self.model_type} cannot be instantiated because '''
f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}''' )
_UpperCAmelCase = kwargs.pop("""encoder""" )
_UpperCAmelCase = encoder_config.pop("""model_type""" )
_UpperCAmelCase = kwargs.pop("""decoder""" )
_UpperCAmelCase = decoder_config.pop("""model_type""" )
_UpperCAmelCase = AutoConfig.for_model(__lowerCAmelCase , **__lowerCAmelCase )
_UpperCAmelCase = AutoConfig.for_model(__lowerCAmelCase , **__lowerCAmelCase )
_UpperCAmelCase = True
@classmethod
def lowerCAmelCase_ ( cls : int , __lowerCAmelCase : PretrainedConfig , __lowerCAmelCase : PretrainedConfig , **__lowerCAmelCase : str ):
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
_UpperCAmelCase = True
_UpperCAmelCase = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__lowerCAmelCase )
def lowerCAmelCase_ ( self : int ):
_UpperCAmelCase = copy.deepcopy(self.__dict__ )
_UpperCAmelCase = self.encoder.to_dict()
_UpperCAmelCase = self.decoder.to_dict()
_UpperCAmelCase = self.__class__.model_type
return output
class a ( lowerCAmelCase_ ):
_snake_case : Union[str, Any] = version.parse('1.11' )
@property
def lowerCAmelCase_ ( self : int ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCAmelCase_ ( self : Tuple ):
return 1e-4
@property
def lowerCAmelCase_ ( self : Dict ):
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class a ( lowerCAmelCase_ ):
@property
def lowerCAmelCase_ ( self : Any ):
_UpperCAmelCase = OrderedDict()
_UpperCAmelCase = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
_UpperCAmelCase = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
_UpperCAmelCase = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def lowerCAmelCase_ ( self : Union[str, Any] , __lowerCAmelCase : "PreTrainedTokenizerBase" , __lowerCAmelCase : int = -1 , __lowerCAmelCase : int = -1 , __lowerCAmelCase : bool = False , __lowerCAmelCase : Optional["TensorType"] = None , ):
import torch
_UpperCAmelCase = OrderedDict()
_UpperCAmelCase = super().generate_dummy_inputs(
__lowerCAmelCase , batch_size=__lowerCAmelCase , seq_length=__lowerCAmelCase , is_pair=__lowerCAmelCase , framework=__lowerCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = dummy_input["""input_ids"""].shape
_UpperCAmelCase = (batch, encoder_sequence, self._config.encoder_hidden_size)
_UpperCAmelCase = dummy_input.pop("""input_ids""" )
_UpperCAmelCase = dummy_input.pop("""attention_mask""" )
_UpperCAmelCase = torch.zeros(__lowerCAmelCase )
return common_inputs
class a ( lowerCAmelCase_ ):
@property
def lowerCAmelCase_ ( self : Tuple ):
pass
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : PretrainedConfig ):
return VisionEncoderDecoderEncoderOnnxConfig(__lowerCAmelCase )
def lowerCAmelCase_ ( self : int , __lowerCAmelCase : PretrainedConfig , __lowerCAmelCase : PretrainedConfig , __lowerCAmelCase : str = "default" ):
_UpperCAmelCase = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(__lowerCAmelCase , __lowerCAmelCase )
| 30
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
__UpperCamelCase = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 1_0563, 1_0786,
1_1420, 1_1709, 1_1907, 1_3163, 1_3697, 1_3700, 1_4808, 1_5306, 1_6410, 1_6791,
1_7992, 1_9203, 1_9510, 2_0724, 2_2305, 2_2935, 2_7007, 3_0109, 3_0420, 3_3409,
3_4949, 4_0283, 4_0493, 4_0549, 4_7282, 4_9146, 5_0257, 5_0359, 5_0360, 5_0361
]
__UpperCamelCase = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
7273, 9061, 9383, 1_0428, 1_0929, 1_1938, 1_2033, 1_2331, 1_2562, 1_3793,
1_4157, 1_4635, 1_5265, 1_5618, 1_6553, 1_6604, 1_8362, 1_8956, 2_0075, 2_1675,
2_2520, 2_6130, 2_6161, 2_6435, 2_8279, 2_9464, 3_1650, 3_2302, 3_2470, 3_6865,
4_2863, 4_7425, 4_9870, 5_0254, 5_0258, 5_0360, 5_0361, 5_0362
]
class UpperCamelCase ( snake_case__ ):
SCREAMING_SNAKE_CASE_ = "whisper"
SCREAMING_SNAKE_CASE_ = ["past_key_values"]
SCREAMING_SNAKE_CASE_ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self, lowerCAmelCase__=5_1865, lowerCAmelCase__=80, lowerCAmelCase__=6, lowerCAmelCase__=4, lowerCAmelCase__=6, lowerCAmelCase__=4, lowerCAmelCase__=1536, lowerCAmelCase__=1536, lowerCAmelCase__=0.0, lowerCAmelCase__=0.0, lowerCAmelCase__=5_0257, lowerCAmelCase__=True, lowerCAmelCase__=True, lowerCAmelCase__="gelu", lowerCAmelCase__=256, lowerCAmelCase__=0.0, lowerCAmelCase__=0.0, lowerCAmelCase__=0.0, lowerCAmelCase__=0.02, lowerCAmelCase__=False, lowerCAmelCase__=1500, lowerCAmelCase__=448, lowerCAmelCase__=5_0256, lowerCAmelCase__=5_0256, lowerCAmelCase__=5_0256, lowerCAmelCase__=None, lowerCAmelCase__=[220, 5_0256], lowerCAmelCase__=False, lowerCAmelCase__=256, lowerCAmelCase__=False, lowerCAmelCase__=0.05, lowerCAmelCase__=10, lowerCAmelCase__=2, lowerCAmelCase__=0.0, lowerCAmelCase__=10, lowerCAmelCase__=0, lowerCAmelCase__=7, **lowerCAmelCase__, ) -> Union[str, Any]:
snake_case_ = vocab_size
snake_case_ = num_mel_bins
snake_case_ = d_model
snake_case_ = encoder_layers
snake_case_ = encoder_attention_heads
snake_case_ = decoder_layers
snake_case_ = decoder_attention_heads
snake_case_ = decoder_ffn_dim
snake_case_ = encoder_ffn_dim
snake_case_ = dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = activation_function
snake_case_ = init_std
snake_case_ = encoder_layerdrop
snake_case_ = decoder_layerdrop
snake_case_ = use_cache
snake_case_ = encoder_layers
snake_case_ = scale_embedding # scale factor will be sqrt(d_model) if True
snake_case_ = max_source_positions
snake_case_ = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
snake_case_ = classifier_proj_size
snake_case_ = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case_ = apply_spec_augment
snake_case_ = mask_time_prob
snake_case_ = mask_time_length
snake_case_ = mask_time_min_masks
snake_case_ = mask_feature_prob
snake_case_ = mask_feature_length
snake_case_ = mask_feature_min_masks
snake_case_ = median_filter_width
super().__init__(
pad_token_id=lowerCAmelCase__, bos_token_id=lowerCAmelCase__, eos_token_id=lowerCAmelCase__, is_encoder_decoder=lowerCAmelCase__, decoder_start_token_id=lowerCAmelCase__, suppress_tokens=lowerCAmelCase__, begin_suppress_tokens=lowerCAmelCase__, **lowerCAmelCase__, )
class UpperCamelCase ( snake_case__ ):
@property
def a_ ( self) -> str:
snake_case_ = OrderedDict(
[
('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
])
if self.use_past:
snake_case_ = {0: "batch"}
else:
snake_case_ = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase__, direction='inputs')
return common_inputs
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__ = -1, lowerCAmelCase__ = -1, lowerCAmelCase__ = False, lowerCAmelCase__ = None, lowerCAmelCase__ = 2_2050, lowerCAmelCase__ = 5.0, lowerCAmelCase__ = 220, ) -> Optional[int]:
snake_case_ = OrderedDict()
snake_case_ = OnnxConfig.generate_dummy_inputs(
self, preprocessor=preprocessor.feature_extractor, batch_size=lowerCAmelCase__, framework=lowerCAmelCase__, sampling_rate=lowerCAmelCase__, time_duration=lowerCAmelCase__, frequency=lowerCAmelCase__, )
snake_case_ = encoder_inputs["input_features"].shape[2]
snake_case_ = encoder_sequence_length // 2 if self.use_past else seq_length
snake_case_ = super().generate_dummy_inputs(
preprocessor.tokenizer, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__)
snake_case_ = encoder_inputs.pop('input_features')
snake_case_ = decoder_inputs.pop('decoder_input_ids')
if "past_key_values" in decoder_inputs:
snake_case_ = decoder_inputs.pop('past_key_values')
return dummy_inputs
@property
def a_ ( self) -> Tuple:
return 1e-3
| 69
|
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 31
| 0
|
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class lowerCAmelCase__ ( lowerCamelCase_ ):
@slow
@require_torch
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[int] = EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' , '''prajjwal1/bert-tiny''' )
lowercase_ : Union[str, Any] = BertTokenizer.from_pretrained('''bert-base-uncased''' )
lowercase_ : Tuple = bertabert.config.encoder.vocab_size
lowercase_ : Any = tokenizer.sep_token_id
lowercase_ : Optional[Any] = tokenizer.cls_token_id
lowercase_ : str = 1_28
lowercase_ : str = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''train[:1%]''' )
lowercase_ : Optional[Any] = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''validation[:1%]''' )
lowercase_ : str = train_dataset.select(range(32 ) )
lowercase_ : Any = val_dataset.select(range(16 ) )
lowercase_ : int = 4
def _map_to_encoder_decoder_inputs(__SCREAMING_SNAKE_CASE ):
# Tokenizer will automatically set [BOS] <text> [EOS]
lowercase_ : List[Any] = tokenizer(batch['''article'''] , padding='''max_length''' , truncation=__SCREAMING_SNAKE_CASE , max_length=5_12 )
lowercase_ : Optional[Any] = tokenizer(batch['''highlights'''] , padding='''max_length''' , truncation=__SCREAMING_SNAKE_CASE , max_length=1_28 )
lowercase_ : Tuple = inputs.input_ids
lowercase_ : str = inputs.attention_mask
lowercase_ : str = outputs.input_ids
lowercase_ : Dict = outputs.input_ids.copy()
lowercase_ : List[str] = [
[-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
]
lowercase_ : List[Any] = outputs.attention_mask
assert all(len(__SCREAMING_SNAKE_CASE ) == 5_12 for x in inputs.input_ids )
assert all(len(__SCREAMING_SNAKE_CASE ) == 1_28 for x in outputs.input_ids )
return batch
def _compute_metrics(__SCREAMING_SNAKE_CASE ):
lowercase_ : Any = pred.label_ids
lowercase_ : Any = pred.predictions
# all unnecessary tokens are removed
lowercase_ : List[str] = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE )
lowercase_ : Dict = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE )
lowercase_ : Dict = sum([int(pred_str[i] == label_str[i] ) for i in range(len(__SCREAMING_SNAKE_CASE ) )] ) / len(__SCREAMING_SNAKE_CASE )
return {"accuracy": accuracy}
# map train dataset
lowercase_ : List[Any] = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE , remove_columns=['''article''', '''highlights'''] , )
train_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
# same for validation dataset
lowercase_ : Dict = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE , remove_columns=['''article''', '''highlights'''] , )
val_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
lowercase_ : Tuple = self.get_auto_remove_tmp_dir()
lowercase_ : int = SeqaSeqTrainingArguments(
output_dir=__SCREAMING_SNAKE_CASE , per_device_train_batch_size=__SCREAMING_SNAKE_CASE , per_device_eval_batch_size=__SCREAMING_SNAKE_CASE , predict_with_generate=__SCREAMING_SNAKE_CASE , evaluation_strategy='''steps''' , do_train=__SCREAMING_SNAKE_CASE , do_eval=__SCREAMING_SNAKE_CASE , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
lowercase_ : Union[str, Any] = SeqaSeqTrainer(
model=__SCREAMING_SNAKE_CASE , args=__SCREAMING_SNAKE_CASE , compute_metrics=_compute_metrics , train_dataset=__SCREAMING_SNAKE_CASE , eval_dataset=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE , )
# start training
trainer.train()
| 264
|
'''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
| 1
|
'''simple docstring'''
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __snake_case : int ) -> int:
__A : Any = tf.convert_to_tensor(__snake_case )
__A : int = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def _lowerCAmelCase ( __snake_case : Optional[int] ) -> Any:
__A : Union[str, Any] = tf.convert_to_tensor(__snake_case )
__A : Tuple = tf.cast(math.pi , x.dtype )
__A : Tuple = tf.cast(0.044_715 , x.dtype )
__A : int = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__snake_case , 3 )) ))
return x * cdf
def _lowerCAmelCase ( __snake_case : List[str] ) -> Tuple:
__A : str = tf.convert_to_tensor(__snake_case )
return x * tf.tanh(tf.math.softplus(__snake_case ) )
def _lowerCAmelCase ( __snake_case : Any ) -> List[str]:
__A : Any = tf.convert_to_tensor(__snake_case )
__A : Optional[Any] = tf.cast(0.044_715 , x.dtype )
__A : int = tf.cast(0.7_978_845_608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def _lowerCAmelCase ( __snake_case : Any ) -> Tuple:
__A : List[str] = tf.convert_to_tensor(__snake_case )
__A : Optional[int] = tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def _lowerCAmelCase ( __snake_case : List[str] ) -> Tuple:
return tf.clip_by_value(_gelu(__snake_case ) , -10 , 10 )
def _lowerCAmelCase ( __snake_case : Dict , __snake_case : Union[str, Any]=-1 ) -> List[str]:
__A ,__A : Dict = tf.split(__snake_case , 2 , axis=__snake_case )
return a * tf.math.sigmoid(__snake_case )
if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
def _lowerCAmelCase ( __snake_case : List[str] ) -> List[Any]:
return tf.keras.activations.gelu(__snake_case , approximate=__snake_case )
lowercase__ : List[Any] = tf.keras.activations.gelu
lowercase__ : Any = approximate_gelu_wrap
else:
lowercase__ : Dict = _gelu
lowercase__ : List[Any] = _gelu_new
lowercase__ : Union[str, Any] = {
'''gelu''': gelu,
'''gelu_10''': gelu_aa,
'''gelu_fast''': gelu_fast,
'''gelu_new''': gelu_new,
'''glu''': glu,
'''mish''': mish,
'''quick_gelu''': quick_gelu,
'''relu''': tf.keras.activations.relu,
'''sigmoid''': tf.keras.activations.sigmoid,
'''silu''': tf.keras.activations.swish,
'''swish''': tf.keras.activations.swish,
'''tanh''': tf.keras.activations.tanh,
}
def _lowerCAmelCase ( __snake_case : Optional[Any] ) -> Optional[int]:
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(f'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}' )
| 190
|
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = ['''image_processor''', '''tokenizer''']
lowerCAmelCase = '''BlipImageProcessor'''
lowerCAmelCase = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : int = False
super().__init__(_UpperCAmelCase , _UpperCAmelCase)
__A : Optional[int] = self.image_processor
def __call__( self , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = True , _UpperCAmelCase = False , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = 0 , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = False , _UpperCAmelCase = False , _UpperCAmelCase = False , _UpperCAmelCase = False , _UpperCAmelCase = False , _UpperCAmelCase = True , _UpperCAmelCase = None , **_UpperCAmelCase , ):
'''simple docstring'''
if images is None and text is None:
raise ValueError('You have to specify either images or text.')
# Get only text
if images is None:
__A : int = self.tokenizer
__A : Optional[Any] = self.tokenizer(
text=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase , stride=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , return_overflowing_tokens=_UpperCAmelCase , return_special_tokens_mask=_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , return_token_type_ids=_UpperCAmelCase , return_length=_UpperCAmelCase , verbose=_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase , )
return text_encoding
# add pixel_values
__A : List[Any] = self.image_processor(_UpperCAmelCase , return_tensors=_UpperCAmelCase)
if text is not None:
__A : Optional[Any] = self.tokenizer(
text=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase , stride=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , return_overflowing_tokens=_UpperCAmelCase , return_special_tokens_mask=_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , return_token_type_ids=_UpperCAmelCase , return_length=_UpperCAmelCase , verbose=_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase , )
else:
__A : int = None
if text_encoding is not None:
encoding_image_processor.update(_UpperCAmelCase)
return encoding_image_processor
def SCREAMING_SNAKE_CASE ( self , *_UpperCAmelCase , **_UpperCAmelCase):
'''simple docstring'''
return self.tokenizer.batch_decode(*_UpperCAmelCase , **_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self , *_UpperCAmelCase , **_UpperCAmelCase):
'''simple docstring'''
return self.tokenizer.decode(*_UpperCAmelCase , **_UpperCAmelCase)
@property
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[str] = self.tokenizer.model_input_names
__A : Tuple = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
| 190
| 1
|
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
if any(not isinstance(UpperCamelCase , UpperCamelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(UpperCamelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(UpperCamelCase , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 184
|
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCAmelCase_:
'''simple docstring'''
__lowercase : Optional[Union[str, Path]] = None
__lowercase : bool = False
__lowercase : bool = False
__lowercase : bool = False
__lowercase : Optional[Dict] = None
__lowercase : Optional[str] = None
__lowercase : bool = False
__lowercase : bool = False
__lowercase : bool = False
__lowercase : bool = True
__lowercase : Optional[int] = None
__lowercase : int = 1
__lowercase : Optional[Union[str, bool]] = None
__lowercase : bool = False
__lowercase : Optional[Dict] = None
__lowercase : Optional[str] = None
def UpperCAmelCase_ ( self ) -> "DownloadConfig":
return self.__class__(**{k: copy.deepcopy(__UpperCAmelCase ) for k, v in self.__dict__.items()} )
| 184
| 1
|
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 UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ ) -> Any:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['''bs'''] , model_result['''ss'''] ):
UpperCamelCase :Tuple = model_result['''result'''][batch_size][sequence_length]
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase ( self ) -> Dict:
UpperCamelCase :List[Any] = '''sshleifer/tiny-gpt2'''
UpperCamelCase :int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase :Optional[int] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Optional[Any] = 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]:
UpperCamelCase :str = '''sgugger/tiny-distilbert-classification'''
UpperCamelCase :List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , only_pretrain_model=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase :List[str] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Tuple = 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 ) -> Tuple:
UpperCamelCase :Optional[int] = '''sshleifer/tiny-gpt2'''
UpperCamelCase :Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase :str = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCAmelCase ( self ) -> Tuple:
UpperCamelCase :List[Any] = '''sshleifer/tiny-gpt2'''
UpperCamelCase :Any = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase :str = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , [config] )
UpperCamelCase :List[Any] = 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 ) -> Dict:
UpperCamelCase :List[str] = '''sshleifer/tiny-gpt2'''
UpperCamelCase :str = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase :str = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , [config] )
UpperCamelCase :int = 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:
UpperCamelCase :Dict = '''sshleifer/tiny-gpt2'''
UpperCamelCase :List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase :int = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Tuple = 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 ) -> Dict:
UpperCamelCase :List[str] = '''sshleifer/tiny-gpt2'''
UpperCamelCase :str = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase :Optional[Any] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , [config] )
UpperCamelCase :Dict = 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 ) -> Optional[int]:
UpperCamelCase :List[Any] = '''patrickvonplaten/t5-tiny-random'''
UpperCamelCase :int = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase :List[Any] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , configs=[config] )
UpperCamelCase :Tuple = 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 UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCamelCase :Optional[Any] = '''sshleifer/tiny-gpt2'''
UpperCamelCase :str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , use_xla=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase :Union[str, Any] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :str = 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]:
UpperCamelCase :Any = '''sshleifer/tiny-gpt2'''
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase :Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=SCREAMING_SNAKE_CASE_ , save_to_csv=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ , '''inf_time.csv''' ) , inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ , '''inf_mem.csv''' ) , env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ , '''env.csv''' ) , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase :Optional[Any] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
benchmark.run()
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , '''inf_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , '''inf_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , '''env.csv''' ) ).exists() )
def UpperCAmelCase ( self ) -> List[Any]:
UpperCamelCase :Union[str, Any] = '''sshleifer/tiny-gpt2'''
def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE_ ):
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''sequential''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''cumulative''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''current''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''total''' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase :Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(SCREAMING_SNAKE_CASE_ , '''log.txt''' ) , log_print=SCREAMING_SNAKE_CASE_ , trace_memory_line_by_line=SCREAMING_SNAKE_CASE_ , eager_mode=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase :str = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Dict = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , '''log.txt''' ) ).exists() )
| 259
|
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 UpperCAmelCase_ :
"""simple docstring"""
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_=512 , 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 , ) -> Dict:
UpperCamelCase :Any = parent
UpperCamelCase :Dict = 13
UpperCamelCase :List[Any] = 7
UpperCamelCase :List[Any] = True
UpperCamelCase :Dict = True
UpperCamelCase :Union[str, Any] = True
UpperCamelCase :List[str] = True
UpperCamelCase :Dict = 99
UpperCamelCase :Any = 32
UpperCamelCase :Tuple = 2
UpperCamelCase :Union[str, Any] = 4
UpperCamelCase :List[str] = 37
UpperCamelCase :Dict = '''gelu'''
UpperCamelCase :Dict = 0.1
UpperCamelCase :Tuple = 0.1
UpperCamelCase :Dict = 512
UpperCamelCase :str = 16
UpperCamelCase :Optional[Any] = 2
UpperCamelCase :Dict = 0.02
UpperCamelCase :Optional[int] = 3
UpperCamelCase :int = 4
UpperCamelCase :Dict = None
def UpperCAmelCase ( self ) -> Tuple:
UpperCamelCase :Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase :Optional[int] = None
if self.use_input_mask:
UpperCamelCase :Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase :Dict = None
if self.use_token_type_ids:
UpperCamelCase :List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase :Union[str, Any] = None
UpperCamelCase :Optional[int] = None
UpperCamelCase :Any = None
if self.use_labels:
UpperCamelCase :Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase :Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase :int = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase :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 UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCamelCase :Optional[Any] = TFRoFormerModel(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Dict = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
UpperCamelCase :int = [input_ids, input_mask]
UpperCamelCase :List[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :int = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
UpperCamelCase :List[Any] = True
UpperCamelCase :Union[str, Any] = TFRoFormerForCausalLM(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase :str = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCamelCase :Any = model(SCREAMING_SNAKE_CASE_ )['''logits''']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
UpperCamelCase :str = TFRoFormerForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Optional[Any] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCamelCase :List[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
UpperCamelCase :List[Any] = self.num_labels
UpperCamelCase :int = TFRoFormerForSequenceClassification(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase :str = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCamelCase :Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
UpperCamelCase :List[Any] = self.num_choices
UpperCamelCase :Any = TFRoFormerForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Dict = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase :int = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase :Any = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase :List[Any] = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
UpperCamelCase :Dict = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
UpperCamelCase :Union[str, Any] = self.num_labels
UpperCamelCase :Dict = TFRoFormerForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase :str = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCamelCase :Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
UpperCamelCase :Union[str, Any] = TFRoFormerForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Dict = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCamelCase :List[Any] = 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 UpperCAmelCase ( self ) -> Tuple:
UpperCamelCase :Optional[int] = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) :Union[str, Any] = config_and_inputs
UpperCamelCase :Any = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class UpperCAmelCase_ ( lowercase, lowercase, unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : str =(
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
UpperCamelCase_ : Tuple =(
{
'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 {}
)
UpperCamelCase_ : Tuple =False
UpperCamelCase_ : Optional[Any] =False
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCamelCase :Any = TFRoFormerModelTester(self )
UpperCamelCase :Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def UpperCAmelCase ( self ) -> List[str]:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCamelCase :List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase ( self ) -> List[Any]:
UpperCamelCase :Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase ( self ) -> str:
UpperCamelCase :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase ( self ) -> Optional[Any]:
UpperCamelCase :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCamelCase :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCamelCase :Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase ( self ) -> Dict:
UpperCamelCase :List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCAmelCase ( self ) -> Dict:
UpperCamelCase :Dict = TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''' )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_tf
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase ( self ) -> Dict:
UpperCamelCase :Tuple = TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
UpperCamelCase :Union[str, Any] = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase :str = model(SCREAMING_SNAKE_CASE_ )[0]
# TODO Replace vocab size
UpperCamelCase :Tuple = 5_0000
UpperCamelCase :Optional[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.
UpperCamelCase :int = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 )
@require_tf
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] =1E-4
def UpperCAmelCase ( self ) -> Dict:
UpperCamelCase :str = tf.constant([[4, 10]] )
UpperCamelCase :List[Any] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
UpperCamelCase :str = emba(input_ids.shape )
UpperCamelCase :List[str] = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=self.tolerance )
def UpperCAmelCase ( self ) -> Optional[Any]:
UpperCamelCase :Dict = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
UpperCamelCase :Dict = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 , embedding_dim=512 )
emba([2, 16, 512] )
UpperCamelCase :Any = emba.weight[:3, :5]
tf.debugging.assert_near(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=self.tolerance )
@require_tf
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : List[Any] =1E-4
def UpperCAmelCase ( self ) -> List[str]:
# 2,12,16,64
UpperCamelCase :List[Any] = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 100
UpperCamelCase :List[Any] = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 100
UpperCamelCase :List[Any] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
UpperCamelCase :int = embed_positions([2, 16, 768] )[None, None, :, :]
UpperCamelCase , UpperCamelCase :List[str] = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase :str = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
UpperCamelCase :Optional[int] = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
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 )
| 259
| 1
|
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__a: List[Any] = collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__a: List[Any] = "https://storage.googleapis.com/cvdf-datasets/mnist/"
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : str = numpy.dtype(numpy.uintaa ).newbyteorder('''>''' )
return numpy.frombuffer(bytestream.read(4 ) , dtype=UpperCAmelCase )[0]
@deprecated(UpperCAmelCase , '''Please use tf.data to implement this functionality.''' )
def __UpperCamelCase ( UpperCAmelCase ):
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=UpperCAmelCase ) as bytestream:
lowercase__ : int = _readaa(UpperCAmelCase )
if magic != 2051:
raise ValueError(
'''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name) )
lowercase__ : Tuple = _readaa(UpperCAmelCase )
lowercase__ : Optional[Any] = _readaa(UpperCAmelCase )
lowercase__ : int = _readaa(UpperCAmelCase )
lowercase__ : List[str] = bytestream.read(rows * cols * num_images )
lowercase__ : Optional[int] = numpy.frombuffer(UpperCAmelCase , dtype=numpy.uinta )
lowercase__ : Optional[Any] = data.reshape(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , 1 )
return data
@deprecated(UpperCAmelCase , '''Please use tf.one_hot on tensors.''' )
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ):
lowercase__ : int = labels_dense.shape[0]
lowercase__ : Any = numpy.arange(UpperCAmelCase ) * num_classes
lowercase__ : List[str] = numpy.zeros((num_labels, num_classes) )
lowercase__ : Dict = 1
return labels_one_hot
@deprecated(UpperCAmelCase , '''Please use tf.data to implement this functionality.''' )
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase=False , UpperCAmelCase=10 ):
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=UpperCAmelCase ) as bytestream:
lowercase__ : List[Any] = _readaa(UpperCAmelCase )
if magic != 2049:
raise ValueError(
'''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name) )
lowercase__ : List[str] = _readaa(UpperCAmelCase )
lowercase__ : Optional[int] = bytestream.read(UpperCAmelCase )
lowercase__ : Dict = numpy.frombuffer(UpperCAmelCase , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(UpperCAmelCase , UpperCAmelCase )
return labels
class UpperCAmelCase :
'''simple docstring'''
@deprecated(
__A , '''Please use alternatives such as official/mnist/_DataSet.py'''
''' from tensorflow/models.''' , )
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=False , __lowerCAmelCase=False , __lowerCAmelCase=dtypes.floataa , __lowerCAmelCase=True , __lowerCAmelCase=None , ) -> List[Any]:
lowercase__ : Optional[Any] = random_seed.get_seed(__A )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
lowercase__ : Optional[Any] = dtypes.as_dtype(__A ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype )
if fake_data:
lowercase__ : Any = 10000
lowercase__ : str = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
lowercase__ : Tuple = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
lowercase__ : Optional[Any] = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
lowercase__ : Tuple = images.astype(numpy.floataa )
lowercase__ : Tuple = numpy.multiply(__A , 1.0 / 255.0 )
lowercase__ : str = images
lowercase__ : Union[str, Any] = labels
lowercase__ : int = 0
lowercase__ : Any = 0
@property
def _lowerCAmelCase( self ) -> Tuple:
return self._images
@property
def _lowerCAmelCase( self ) -> List[str]:
return self._labels
@property
def _lowerCAmelCase( self ) -> List[str]:
return self._num_examples
@property
def _lowerCAmelCase( self ) -> Any:
return self._epochs_completed
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase=False , __lowerCAmelCase=True ) -> Union[str, Any]:
if fake_data:
lowercase__ : int = [1] * 784
lowercase__ : Optional[int] = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(__A )],
[fake_label for _ in range(__A )],
)
lowercase__ : Any = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
lowercase__ : Dict = numpy.arange(self._num_examples )
numpy.random.shuffle(__A )
lowercase__ : Optional[Any] = self.images[perma]
lowercase__ : str = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
lowercase__ : List[str] = self._num_examples - start
lowercase__ : Tuple = self._images[start : self._num_examples]
lowercase__ : List[str] = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
lowercase__ : Optional[Any] = numpy.arange(self._num_examples )
numpy.random.shuffle(__A )
lowercase__ : Any = self.images[perm]
lowercase__ : Any = self.labels[perm]
# Start next epoch
lowercase__ : Any = 0
lowercase__ : List[Any] = batch_size - rest_num_examples
lowercase__ : str = self._index_in_epoch
lowercase__ : List[Any] = self._images[start:end]
lowercase__ : Dict = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
lowercase__ : List[Any] = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(UpperCAmelCase , '''Please write your own downloading logic.''' )
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
if not gfile.Exists(UpperCAmelCase ):
gfile.MakeDirs(UpperCAmelCase )
lowercase__ : Tuple = os.path.join(UpperCAmelCase , UpperCAmelCase )
if not gfile.Exists(UpperCAmelCase ):
urllib.request.urlretrieve(UpperCAmelCase , UpperCAmelCase ) # noqa: S310
with gfile.GFile(UpperCAmelCase ) as f:
lowercase__ : List[str] = f.size()
print('''Successfully downloaded''' , UpperCAmelCase , UpperCAmelCase , '''bytes.''' )
return filepath
@deprecated(
UpperCAmelCase , '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')''' )
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase=False , UpperCAmelCase=False , UpperCAmelCase=dtypes.floataa , UpperCAmelCase=True , UpperCAmelCase=5000 , UpperCAmelCase=None , UpperCAmelCase=DEFAULT_SOURCE_URL , ):
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=UpperCAmelCase , one_hot=UpperCAmelCase , dtype=UpperCAmelCase , seed=UpperCAmelCase )
lowercase__ : Tuple = fake()
lowercase__ : int = fake()
lowercase__ : Union[str, Any] = fake()
return _Datasets(train=UpperCAmelCase , validation=UpperCAmelCase , test=UpperCAmelCase )
if not source_url: # empty string check
lowercase__ : Union[str, Any] = DEFAULT_SOURCE_URL
lowercase__ : Optional[int] = '''train-images-idx3-ubyte.gz'''
lowercase__ : str = '''train-labels-idx1-ubyte.gz'''
lowercase__ : List[Any] = '''t10k-images-idx3-ubyte.gz'''
lowercase__ : Optional[int] = '''t10k-labels-idx1-ubyte.gz'''
lowercase__ : Any = _maybe_download(
UpperCAmelCase , UpperCAmelCase , source_url + train_images_file )
with gfile.Open(UpperCAmelCase , '''rb''' ) as f:
lowercase__ : Any = _extract_images(UpperCAmelCase )
lowercase__ : List[Any] = _maybe_download(
UpperCAmelCase , UpperCAmelCase , source_url + train_labels_file )
with gfile.Open(UpperCAmelCase , '''rb''' ) as f:
lowercase__ : List[str] = _extract_labels(UpperCAmelCase , one_hot=UpperCAmelCase )
lowercase__ : Optional[int] = _maybe_download(
UpperCAmelCase , UpperCAmelCase , source_url + test_images_file )
with gfile.Open(UpperCAmelCase , '''rb''' ) as f:
lowercase__ : Optional[int] = _extract_images(UpperCAmelCase )
lowercase__ : Dict = _maybe_download(
UpperCAmelCase , UpperCAmelCase , source_url + test_labels_file )
with gfile.Open(UpperCAmelCase , '''rb''' ) as f:
lowercase__ : Optional[int] = _extract_labels(UpperCAmelCase , one_hot=UpperCAmelCase )
if not 0 <= validation_size <= len(UpperCAmelCase ):
lowercase__ : List[str] = (
'''Validation size should be between 0 and '''
F"""{len(UpperCAmelCase )}. Received: {validation_size}."""
)
raise ValueError(UpperCAmelCase )
lowercase__ : Dict = train_images[:validation_size]
lowercase__ : Optional[Any] = train_labels[:validation_size]
lowercase__ : Union[str, Any] = train_images[validation_size:]
lowercase__ : Optional[int] = train_labels[validation_size:]
lowercase__ : Tuple = {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}
lowercase__ : int = _DataSet(UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
lowercase__ : List[str] = _DataSet(UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
lowercase__ : Dict = _DataSet(UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
return _Datasets(train=UpperCAmelCase , validation=UpperCAmelCase , test=UpperCAmelCase )
| 361
|
'''simple docstring'''
class UpperCAmelCase :
'''simple docstring'''
def __init__( self ) -> List[str]:
lowercase__ : Dict = {}
def _lowerCAmelCase( self ) -> None:
print(self.vertex )
for i in self.vertex:
print(__lowerCAmelCase , ''' -> ''' , ''' -> '''.join([str(__lowerCAmelCase ) for j in self.vertex[i]] ) )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase ) -> None:
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(__lowerCAmelCase )
else:
# else make a new vertex
lowercase__ : Union[str, Any] = [to_vertex]
def _lowerCAmelCase( self ) -> None:
# visited array for storing already visited nodes
lowercase__ : str = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(__lowerCAmelCase , __lowerCAmelCase )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase ) -> None:
# mark start vertex as visited
lowercase__ : List[str] = True
print(__lowerCAmelCase , end=''' ''' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
__a: Optional[Any] = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("""DFS:""")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 214
| 0
|
def a ( snake_case__: int ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise TypeError('''Input value must be an \'int\' type''' )
lowercase_ = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30
|
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__a = re.compile(r'\s*\(\s*"(\S[^"]+)"')
def a ( snake_case__: str , snake_case__: bool = False ):
'''simple docstring'''
with open(snake_case__ , '''r''' , encoding='''utf-8''' ) as f:
lowercase_ = f.read()
lowercase_ = content.split('''\n''' )
lowercase_ = []
lowercase_ = 0
while line_idx < len(snake_case__ ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
lowercase_ = len(re.search(r'''^(\s*)\S''' , lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(''' ''' * indent + '''(''' ):
new_lines.append(lines[line_idx] )
line_idx += 1
lowercase_ = []
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
lowercase_ = line_idx
while not lines[line_idx].startswith(''' ''' * indent + ''')''' ):
line_idx += 1
blocks.append('''\n'''.join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
lowercase_ = sorted(snake_case__ , key=lambda snake_case__ : _re_identifier.search(snake_case__ ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write('''\n'''.join(snake_case__ ) )
elif "\n".join(snake_case__ ) != content:
return True
def a ( snake_case__: bool = False ):
'''simple docstring'''
lowercase_ = [os.path.join(snake_case__ , snake_case__ ) for f in os.listdir(snake_case__ ) if f.endswith('''.py''' )]
lowercase_ = [sort_auto_mapping(snake_case__ , overwrite=snake_case__ ) for fname in fnames]
if not overwrite and any(snake_case__ ):
lowercase_ = [f for f, d in zip(snake_case__ , snake_case__ ) if d]
raise ValueError(
F'''The following files have auto mappings that need sorting: {', '.join(snake_case__ )}. Run `make style` to fix'''
''' this.''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
__a = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 30
| 1
|
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class __UpperCamelCase :
def __init__( self, lowerCAmelCase = "cpu", lowerCAmelCase = "openai/clip-vit-large-patch14" ):
"""simple docstring"""
lowerCamelCase_ =device
lowerCamelCase_ =CLIPTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =[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]
lowerCamelCase_ =[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]
lowerCamelCase_ =torchvision.transforms.Normalize(self.image_mean, self.image_std )
lowerCamelCase_ =torchvision.transforms.Resize(224 )
lowerCamelCase_ =torchvision.transforms.CenterCrop(224 )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.resize(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =self.center_crop(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =self.normalize(_SCREAMING_SNAKE_CASE )
return images
def __call__( self, lowerCAmelCase=None, lowerCAmelCase=None, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.tokenizer(text=_SCREAMING_SNAKE_CASE, **_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =self.preprocess_img(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ ={key: value.to(self.device ) for (key, value) in encoding.items()}
return encoding
class __UpperCamelCase ( nn.Module ):
def __init__( self, lowerCAmelCase=10, lowerCAmelCase=0.0_1, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=False, lowerCAmelCase=True, lowerCAmelCase="image", lowerCAmelCase=True, lowerCAmelCase=False, lowerCAmelCase=False, lowerCAmelCase=False, ):
"""simple docstring"""
super().__init__()
lowerCamelCase_ =None
lowerCamelCase_ =device if device else get_device()
if vqgan:
lowerCamelCase_ =vqgan
else:
lowerCamelCase_ =load_vqgan(self.device, conf_path=_SCREAMING_SNAKE_CASE, ckpt_path=_SCREAMING_SNAKE_CASE )
self.vqgan.eval()
if clip:
lowerCamelCase_ =clip
else:
lowerCamelCase_ =CLIPModel.from_pretrained('''openai/clip-vit-base-patch32''' )
self.clip.to(self.device )
lowerCamelCase_ =ProcessorGradientFlow(device=self.device )
lowerCamelCase_ =iterations
lowerCamelCase_ =lr
lowerCamelCase_ =log
lowerCamelCase_ =make_grid
lowerCamelCase_ =return_val
lowerCamelCase_ =quantize
lowerCamelCase_ =self.vqgan.decoder.z_shape
def lowercase__ ( self, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=5, lowerCAmelCase=True ):
"""simple docstring"""
lowerCamelCase_ =[]
if output_path is None:
lowerCamelCase_ ="./animation.gif"
if input_path is None:
lowerCamelCase_ =self.save_path
lowerCamelCase_ =sorted(glob(input_path + '''/*''' ) )
if not len(_SCREAMING_SNAKE_CASE ):
raise ValueError(
'''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
''' function?)''' )
if len(_SCREAMING_SNAKE_CASE ) == 1:
print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)''' )
lowerCamelCase_ =total_duration / len(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =[frame_duration] * len(_SCREAMING_SNAKE_CASE )
if extend_frames:
lowerCamelCase_ =1.5
lowerCamelCase_ =3
for file_name in paths:
if file_name.endswith('''.png''' ):
images.append(imageio.imread(_SCREAMING_SNAKE_CASE ) )
imageio.mimsave(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, duration=_SCREAMING_SNAKE_CASE )
print(f'''gif saved to {output_path}''' )
def lowercase__ ( self, lowerCAmelCase=None, lowerCAmelCase=None ):
"""simple docstring"""
if not (path or img):
raise ValueError('''Input either path or tensor''' )
if img is not None:
raise NotImplementedError
lowerCamelCase_ =preprocess(Image.open(_SCREAMING_SNAKE_CASE ), target_image_size=256 ).to(self.device )
lowerCamelCase_ =preprocess_vqgan(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =self.vqgan.encode(_SCREAMING_SNAKE_CASE )
return z
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.latent.detach().requires_grad_()
lowerCamelCase_ =base_latent + transform_vector
if self.quantize:
lowerCamelCase_ =self.vqgan.quantize(_SCREAMING_SNAKE_CASE )
else:
lowerCamelCase_ =trans_latent
return self.vqgan.decode(_SCREAMING_SNAKE_CASE )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase=None ):
"""simple docstring"""
lowerCamelCase_ =self.clip_preprocessor(text=_SCREAMING_SNAKE_CASE, images=_SCREAMING_SNAKE_CASE, return_tensors='''pt''', padding=_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =self.clip(**_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =clip_outputs.logits_per_image
if weights is not None:
lowerCamelCase_ =similarity_logits * weights
return similarity_logits.sum()
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self._get_clip_similarity(pos_prompts['''prompts'''], _SCREAMING_SNAKE_CASE, weights=(1 / pos_prompts['''weights''']) )
if neg_prompts:
lowerCamelCase_ =self._get_clip_similarity(neg_prompts['''prompts'''], _SCREAMING_SNAKE_CASE, weights=neg_prompts['''weights'''] )
else:
lowerCamelCase_ =torch.tensor([1], device=self.device )
lowerCamelCase_ =-torch.log(_SCREAMING_SNAKE_CASE ) + torch.log(_SCREAMING_SNAKE_CASE )
return loss
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =torch.randn_like(self.latent, requires_grad=_SCREAMING_SNAKE_CASE, device=self.device )
lowerCamelCase_ =torch.optim.Adam([vector], lr=self.lr )
for i in range(self.iterations ):
optim.zero_grad()
lowerCamelCase_ =self._add_vector(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =loop_post_process(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =self._get_CLIP_loss(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE )
print('''CLIP loss''', _SCREAMING_SNAKE_CASE )
if self.log:
wandb.log({'''CLIP Loss''': clip_loss} )
clip_loss.backward(retain_graph=_SCREAMING_SNAKE_CASE )
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0] )
else:
yield vector
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
wandb.init(reinit=_SCREAMING_SNAKE_CASE, project='''face-editor''' )
wandb.config.update({'''Positive Prompts''': positive_prompts} )
wandb.config.update({'''Negative Prompts''': negative_prompts} )
wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations} )
if image_path:
lowerCamelCase_ =Image.open(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =image.resize((256, 256) )
wandb.log('''Original Image''', wandb.Image(_SCREAMING_SNAKE_CASE ) )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if not prompts:
return []
lowerCamelCase_ =[]
lowerCamelCase_ =[]
if isinstance(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE ):
lowerCamelCase_ =[prompt.strip() for prompt in prompts.split('''|''' )]
for prompt in prompts:
if isinstance(_SCREAMING_SNAKE_CASE, (tuple, list) ):
lowerCamelCase_ =prompt[0]
lowerCamelCase_ =float(prompt[1] )
elif ":" in prompt:
lowerCamelCase_ =prompt.split(''':''' )
lowerCamelCase_ =float(_SCREAMING_SNAKE_CASE )
else:
lowerCamelCase_ =prompt
lowerCamelCase_ =1.0
processed_prompts.append(_SCREAMING_SNAKE_CASE )
weights.append(_SCREAMING_SNAKE_CASE )
return {
"prompts": processed_prompts,
"weights": torch.tensor(_SCREAMING_SNAKE_CASE, device=self.device ),
}
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=True, lowerCAmelCase=False, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=None, ):
"""simple docstring"""
if image_path:
lowerCamelCase_ =self._get_latent(_SCREAMING_SNAKE_CASE )
else:
lowerCamelCase_ =torch.randn(self.latent_dim, device=self.device )
if self.log:
self._init_logging(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE )
assert pos_prompts, "You must provide at least one positive prompt."
lowerCamelCase_ =self.process_prompts(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =self.process_prompts(_SCREAMING_SNAKE_CASE )
if save_final and save_path is None:
lowerCamelCase_ =os.path.join('''./outputs/''', '''_'''.join(pos_prompts['''prompts'''] ) )
if not os.path.exists(_SCREAMING_SNAKE_CASE ):
os.makedirs(_SCREAMING_SNAKE_CASE )
else:
lowerCamelCase_ =save_path + "_" + get_timestamp()
os.makedirs(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =save_path
lowerCamelCase_ =self.vqgan.decode(self.latent )[0]
if show_intermediate:
print('''Original Image''' )
show_pil(custom_to_pil(_SCREAMING_SNAKE_CASE ) )
lowerCamelCase_ =loop_post_process(_SCREAMING_SNAKE_CASE )
for iter, transformed_img in enumerate(self._optimize_CLIP(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE ) ):
if show_intermediate:
show_pil(_SCREAMING_SNAKE_CASE )
if save_intermediate:
transformed_img.save(os.path.join(self.save_path, f'''iter_{iter:03d}.png''' ) )
if self.log:
wandb.log({'''Image''': wandb.Image(_SCREAMING_SNAKE_CASE )} )
if show_final:
show_pil(_SCREAMING_SNAKE_CASE )
if save_final:
transformed_img.save(os.path.join(self.save_path, f'''iter_{iter:03d}_final.png''' ) )
| 363
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=lowerCamelCase__ ):
lowercase : str =['speech']
def __init__( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(self, ['''speech'''] )
class __UpperCamelCase ( metaclass=lowerCamelCase__ ):
lowercase : Any =['speech']
def __init__( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(self, ['''speech'''] )
| 6
| 0
|
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self : int , lowercase_ : Optional[int] , lowercase_ : Any , lowercase_ : Union[str, Any] ):
snake_case_ : str = name
snake_case_ : List[Any] = value
snake_case_ : Union[str, Any] = weight
def __repr__( self : Union[str, Any] ):
return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"
def _snake_case ( self : Tuple ):
return self.value
def _snake_case ( self : Any ):
return self.name
def _snake_case ( self : Any ):
return self.weight
def _snake_case ( self : List[Any] ):
return self.value / self.weight
def __lowercase ( _a , _a , _a ):
snake_case_ : Union[str, Any] = []
for i in range(len(_a ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def __lowercase ( _a , _a , _a ):
snake_case_ : Union[str, Any] = sorted(_a , key=_a , reverse=_a )
snake_case_ : List[Any] = []
snake_case_, snake_case_ : Any = 0.0, 0.0
for i in range(len(_a ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def __lowercase ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264
|
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def __lowercase ( _a="" ):
snake_case_ : List[str] = tempfile.mkdtemp()
return os.path.join(_a , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : str ):
snake_case_ : int = torch.rand(12 , dtype=torch.floataa ) - 0.5
snake_case_ : Optional[int] = AgentAudio(lowercase_ )
snake_case_ : List[str] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1E-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(lowercase_ ) )
# Ensure that the file contains the same value as the original tensor
snake_case_, snake_case_ : int = sf.read(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , torch.tensor(lowercase_ ) , atol=1E-4 ) )
def _snake_case ( self : Optional[int] ):
snake_case_ : Any = torch.rand(12 , dtype=torch.floataa ) - 0.5
snake_case_ : List[str] = get_new_path(suffix='''.wav''' )
sf.write(lowercase_ , lowercase_ , 16000 )
snake_case_ : Tuple = AgentAudio(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1E-4 ) )
self.assertEqual(agent_type.to_string() , lowercase_ )
@require_vision
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Tuple ):
snake_case_ : List[Any] = torch.randint(0 , 256 , (64, 64, 3) )
snake_case_ : str = AgentImage(lowercase_ )
snake_case_ : Union[str, Any] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type._tensor , atol=1E-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def _snake_case ( self : str ):
snake_case_ : Any = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
snake_case_ : Optional[int] = Image.open(lowercase_ )
snake_case_ : Tuple = AgentImage(lowercase_ )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def _snake_case ( self : str ):
snake_case_ : int = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
snake_case_ : Dict = Image.open(lowercase_ )
snake_case_ : List[str] = AgentImage(lowercase_ )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Any ):
snake_case_ : Tuple = '''Hey!'''
snake_case_ : Optional[Any] = AgentText(lowercase_ )
self.assertEqual(lowercase_ , agent_type.to_string() )
self.assertEqual(lowercase_ , agent_type.to_raw() )
self.assertEqual(lowercase_ , lowercase_ )
| 264
| 1
|
"""simple docstring"""
def lowerCamelCase_ (UpperCamelCase__ : list[int] ):
if not numbers:
return 0
if not isinstance(UpperCamelCase__ , (list, tuple) ) or not all(
isinstance(UpperCamelCase__ , UpperCamelCase__ ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
_UpperCAmelCase : Union[str, Any] = numbers[0]
for i in range(1 , len(UpperCamelCase__ ) ):
# update the maximum and minimum subarray products
_UpperCAmelCase : Tuple = numbers[i]
if number < 0:
_UpperCAmelCase , _UpperCAmelCase : Tuple = min_till_now, max_till_now
_UpperCAmelCase : Union[str, Any] = max(UpperCamelCase__ , max_till_now * number )
_UpperCAmelCase : Dict = min(UpperCamelCase__ , min_till_now * number )
# update the maximum product found till now
_UpperCAmelCase : Any = max(UpperCamelCase__ , UpperCamelCase__ )
return max_prod
| 68
|
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ (UpperCamelCase__ : list[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
_UpperCAmelCase , _UpperCAmelCase : int = array[indexa], array[indexa]
def lowerCamelCase_ (UpperCamelCase__ : list[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if length > 1:
_UpperCAmelCase : str = int(length / 2 )
for i in range(UpperCamelCase__ , low + middle ):
comp_and_swap(UpperCamelCase__ , UpperCamelCase__ , i + middle , UpperCamelCase__ )
bitonic_merge(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
bitonic_merge(UpperCamelCase__ , low + middle , UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ (UpperCamelCase__ : list[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if length > 1:
_UpperCAmelCase : str = int(length / 2 )
bitonic_sort(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , 1 )
bitonic_sort(UpperCamelCase__ , low + middle , UpperCamelCase__ , 0 )
bitonic_merge(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
_lowerCAmelCase :Any = input('Enter numbers separated by a comma:\n').strip()
_lowerCAmelCase :Tuple = [int(item.strip()) for item in user_input.split(',')]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print('\nSorted array in ascending order is: ', end='')
print(*unsorted, sep=', ')
bitonic_merge(unsorted, 0, len(unsorted), 0)
print('Sorted array in descending order is: ', end='')
print(*unsorted, sep=', ')
| 68
| 1
|
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _lowercase ( ctypes.Structure):
"""simple docstring"""
A__ = [("size", ctypes.c_int), ("visible", ctypes.c_byte)]
def lowercase_ ( ):
"""simple docstring"""
if os.name == "nt":
lowerCamelCase__ : Any = CursorInfo()
lowerCamelCase__ : Any = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_A , ctypes.byref(_A ) )
lowerCamelCase__ : int = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(_A , ctypes.byref(_A ) )
elif os.name == "posix":
sys.stdout.write("\033[?25l" )
sys.stdout.flush()
def lowercase_ ( ):
"""simple docstring"""
if os.name == "nt":
lowerCamelCase__ : List[Any] = CursorInfo()
lowerCamelCase__ : Dict = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_A , ctypes.byref(_A ) )
lowerCamelCase__ : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(_A , ctypes.byref(_A ) )
elif os.name == "posix":
sys.stdout.write("\033[?25h" )
sys.stdout.flush()
@contextmanager
def lowercase_ ( ):
"""simple docstring"""
try:
hide_cursor()
yield
finally:
show_cursor()
| 184
|
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
A : Optional[int] = [
"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.",
]
A : List[Any] = [
"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_ ( ):
"""simple docstring"""
lowerCamelCase__ : Dict = calculate_rouge(_A , _A , bootstrap_aggregation=_A , rouge_keys=["rouge2", "rougeL"] )
assert isinstance(_A , _A )
lowerCamelCase__ : List[Any] = 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_ ( ):
"""simple docstring"""
lowerCamelCase__ : Any = "rougeLsum"
lowerCamelCase__ : List[str] = calculate_rouge(_A , _A , newline_sep=_A , rouge_keys=[k] )[k]
lowerCamelCase__ : str = calculate_rouge(_A , _A , newline_sep=_A , rouge_keys=[k] )[k]
assert score > score_no_sep
def lowercase_ ( ):
"""simple docstring"""
lowerCamelCase__ : int = ["rouge1", "rouge2", "rougeL"]
lowerCamelCase__ : Union[str, Any] = calculate_rouge(_A , _A , newline_sep=_A , rouge_keys=_A )
lowerCamelCase__ : Any = calculate_rouge(_A , _A , newline_sep=_A , rouge_keys=_A )
assert score_sep == score_no_sep
def lowercase_ ( ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] = [
"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 .",
]
lowerCamelCase__ : Tuple = [
"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_ ( ):
"""simple docstring"""
lowerCamelCase__ : List[str] = [
"\" \"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\" "
]
lowerCamelCase__ : str = [
" 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 ."
]
lowerCamelCase__ : Union[str, Any] = calculate_rouge(_A , _A , rouge_keys=["rougeLsum"] , newline_sep=_A )["rougeLsum"]
lowerCamelCase__ : List[str] = calculate_rouge(_A , _A , rouge_keys=["rougeLsum"] )["rougeLsum"]
assert new_score > prev_score
def lowercase_ ( ):
"""simple docstring"""
lowerCamelCase__ : Tuple = Path("examples/seq2seq/test_data/wmt_en_ro" )
lowerCamelCase__ : Any = calculate_rouge_path(data_dir.joinpath("test.source" ) , data_dir.joinpath("test.target" ) )
assert isinstance(_A , _A )
lowerCamelCase__ : str = calculate_rouge_path(
data_dir.joinpath("test.source" ) , data_dir.joinpath("test.target" ) , bootstrap_aggregation=_A )
assert isinstance(_A , _A )
| 184
| 1
|
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def __A ( self ) -> List[str]:
'''simple docstring'''
super().tearDown()
gc.collect()
def __A ( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase , lowerCamelCase = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-canny""" , from_pt=A , dtype=jnp.bfloataa )
lowerCamelCase , lowerCamelCase = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=A , from_pt=A , dtype=jnp.bfloataa )
lowerCamelCase = controlnet_params
lowerCamelCase = """bird"""
lowerCamelCase = jax.device_count()
lowerCamelCase = pipe.prepare_text_inputs([prompts] * num_samples )
lowerCamelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" )
lowerCamelCase = pipe.prepare_image_inputs([canny_image] * num_samples )
lowerCamelCase = jax.random.PRNGKey(0 )
lowerCamelCase = jax.random.split(A , jax.device_count() )
lowerCamelCase = replicate(A )
lowerCamelCase = shard(A )
lowerCamelCase = shard(A )
lowerCamelCase = pipe(
prompt_ids=A , image=A , params=A , prng_seed=A , num_inference_steps=50 , jit=A , ).images
assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)
lowerCamelCase = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCamelCase = images[0, 2_53:2_56, 2_53:2_56, -1]
lowerCamelCase = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCamelCase = jnp.array(
[0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def __A ( self ) -> Dict:
'''simple docstring'''
lowerCamelCase , lowerCamelCase = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-openpose""" , from_pt=A , dtype=jnp.bfloataa )
lowerCamelCase , lowerCamelCase = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=A , from_pt=A , dtype=jnp.bfloataa )
lowerCamelCase = controlnet_params
lowerCamelCase = """Chef in the kitchen"""
lowerCamelCase = jax.device_count()
lowerCamelCase = pipe.prepare_text_inputs([prompts] * num_samples )
lowerCamelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png""" )
lowerCamelCase = pipe.prepare_image_inputs([pose_image] * num_samples )
lowerCamelCase = jax.random.PRNGKey(0 )
lowerCamelCase = jax.random.split(A , jax.device_count() )
lowerCamelCase = replicate(A )
lowerCamelCase = shard(A )
lowerCamelCase = shard(A )
lowerCamelCase = pipe(
prompt_ids=A , image=A , params=A , prng_seed=A , num_inference_steps=50 , jit=A , ).images
assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)
lowerCamelCase = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCamelCase = images[0, 2_53:2_56, 2_53:2_56, -1]
lowerCamelCase = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCamelCase = jnp.array(
[[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 66
|
import math
import tensorflow as tf
from packaging import version
def __lowerCamelCase ( lowerCamelCase__ : Optional[Any] ):
'''simple docstring'''
lowerCamelCase = tf.convert_to_tensor(lowerCamelCase__ )
lowerCamelCase = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def __lowerCamelCase ( lowerCamelCase__ : Dict ):
'''simple docstring'''
lowerCamelCase = tf.convert_to_tensor(lowerCamelCase__ )
lowerCamelCase = tf.cast(math.pi , x.dtype )
lowerCamelCase = tf.cast(0.0_4_4_7_1_5 , x.dtype )
lowerCamelCase = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase__ , 3 )) ))
return x * cdf
def __lowerCamelCase ( lowerCamelCase__ : Any ):
'''simple docstring'''
lowerCamelCase = tf.convert_to_tensor(lowerCamelCase__ )
return x * tf.tanh(tf.math.softplus(lowerCamelCase__ ) )
def __lowerCamelCase ( lowerCamelCase__ : List[Any] ):
'''simple docstring'''
lowerCamelCase = tf.convert_to_tensor(lowerCamelCase__ )
lowerCamelCase = tf.cast(0.0_4_4_7_1_5 , x.dtype )
lowerCamelCase = tf.cast(0.7_9_7_8_8_4_5_6_0_8 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def __lowerCamelCase ( lowerCamelCase__ : str ):
'''simple docstring'''
lowerCamelCase = tf.convert_to_tensor(lowerCamelCase__ )
lowerCamelCase = tf.cast(1.7_0_2 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def __lowerCamelCase ( lowerCamelCase__ : List[str] ):
'''simple docstring'''
return tf.clip_by_value(_gelu(lowerCamelCase__ ) , -10 , 10 )
def __lowerCamelCase ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int]=-1 ):
'''simple docstring'''
lowerCamelCase , lowerCamelCase = tf.split(lowerCamelCase__ , 2 , axis=lowerCamelCase__ )
return a * tf.math.sigmoid(lowerCamelCase__ )
if version.parse(tf.version.VERSION) >= version.parse("2.4"):
def __lowerCamelCase ( lowerCamelCase__ : List[str] ):
'''simple docstring'''
return tf.keras.activations.gelu(lowerCamelCase__ , approximate=lowerCamelCase__ )
UpperCAmelCase : Union[str, Any] = tf.keras.activations.gelu
UpperCAmelCase : Optional[Any] = approximate_gelu_wrap
else:
UpperCAmelCase : List[Any] = _gelu
UpperCAmelCase : str = _gelu_new
UpperCAmelCase : Union[str, Any] = {
"gelu": gelu,
"gelu_10": gelu_aa,
"gelu_fast": gelu_fast,
"gelu_new": gelu_new,
"glu": glu,
"mish": mish,
"quick_gelu": quick_gelu,
"relu": tf.keras.activations.relu,
"sigmoid": tf.keras.activations.sigmoid,
"silu": tf.keras.activations.swish,
"swish": tf.keras.activations.swish,
"tanh": tf.keras.activations.tanh,
}
def __lowerCamelCase ( lowerCamelCase__ : List[Any] ):
'''simple docstring'''
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(f'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}' )
| 66
| 1
|
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 _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ):
a :Optional[Any] = tempfile.mkdtemp()
a :List[str] = BlipImageProcessor()
a :List[Any] = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''' )
a :int = BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
a :str = InstructBlipProcessor(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self , **_lowerCamelCase ):
return AutoProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase ).tokenizer
def SCREAMING_SNAKE_CASE__ ( self , **_lowerCamelCase ):
return AutoProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase ).image_processor
def SCREAMING_SNAKE_CASE__ ( self , **_lowerCamelCase ):
return AutoProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase ).qformer_tokenizer
def SCREAMING_SNAKE_CASE__ ( self ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self ):
a :List[str] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
a :Tuple = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE__ ( self ):
a :Union[str, Any] = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
a :List[str] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
a :Tuple = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
a :Union[str, Any] = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
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 )
self.assertIsInstance(processor.qformer_tokenizer , _lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self ):
a :Tuple = self.get_image_processor()
a :Tuple = self.get_tokenizer()
a :Union[str, Any] = self.get_qformer_tokenizer()
a :Optional[Any] = InstructBlipProcessor(
tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase , qformer_tokenizer=_lowerCamelCase )
a :Optional[int] = self.prepare_image_inputs()
a :Optional[int] = image_processor(_lowerCamelCase , return_tensors='''np''' )
a :int = processor(images=_lowerCamelCase , 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 SCREAMING_SNAKE_CASE__ ( self ):
a :Dict = self.get_image_processor()
a :Any = self.get_tokenizer()
a :Dict = self.get_qformer_tokenizer()
a :Optional[Any] = InstructBlipProcessor(
tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase , qformer_tokenizer=_lowerCamelCase )
a :str = '''lower newer'''
a :Any = processor(text=_lowerCamelCase )
a :List[Any] = tokenizer(_lowerCamelCase , return_token_type_ids=_lowerCamelCase )
a :Optional[int] = qformer_tokenizer(_lowerCamelCase , return_token_type_ids=_lowerCamelCase )
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 SCREAMING_SNAKE_CASE__ ( self ):
a :List[Any] = self.get_image_processor()
a :str = self.get_tokenizer()
a :Union[str, Any] = self.get_qformer_tokenizer()
a :Tuple = InstructBlipProcessor(
tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase , qformer_tokenizer=_lowerCamelCase )
a :List[Any] = '''lower newer'''
a :Union[str, Any] = self.prepare_image_inputs()
a :Tuple = processor(text=_lowerCamelCase , images=_lowerCamelCase )
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(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE__ ( self ):
a :List[str] = self.get_image_processor()
a :Union[str, Any] = self.get_tokenizer()
a :int = self.get_qformer_tokenizer()
a :Optional[Any] = InstructBlipProcessor(
tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase , qformer_tokenizer=_lowerCamelCase )
a :Tuple = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
a :Optional[Any] = processor.batch_decode(_lowerCamelCase )
a :List[str] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self ):
a :Dict = self.get_image_processor()
a :List[Any] = self.get_tokenizer()
a :str = self.get_qformer_tokenizer()
a :List[str] = InstructBlipProcessor(
tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase , qformer_tokenizer=_lowerCamelCase )
a :int = '''lower newer'''
a :Tuple = self.prepare_image_inputs()
a :Dict = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
| 94
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 214
| 0
|
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class a :
def __init__( self : Optional[int] , lowercase_ : str , lowercase_ : int=13 , lowercase_ : List[str]=7 , lowercase_ : Union[str, Any]=True , lowercase_ : Any=True , lowercase_ : int=99 , lowercase_ : str=32 , lowercase_ : str=5 , lowercase_ : List[Any]=4 , lowercase_ : Dict=37 , lowercase_ : List[str]="gelu" , lowercase_ : int=0.1 , lowercase_ : Tuple=0.1 , lowercase_ : int=50 , lowercase_ : Any=0.02 , lowercase_ : str=True , lowercase_ : int=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = initializer_range
snake_case_ = use_labels
snake_case_ = scope
def A_ ( self : Dict ):
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = self.get_config()
return config, input_ids, input_mask, token_labels
def A_ ( self : Optional[int] ):
return BertGenerationConfig(
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 , is_decoder=snake_case_ , initializer_range=self.initializer_range , )
def A_ ( self : Dict ):
(
snake_case_
) = self.prepare_config_and_inputs()
snake_case_ = True
snake_case_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def A_ ( self : Optional[int] , lowercase_ : Tuple , lowercase_ : str , lowercase_ : str , lowercase_ : Optional[int] , **lowercase_ : int , ):
snake_case_ = BertGenerationEncoder(config=snake_case_ )
model.to(snake_case_ )
model.eval()
snake_case_ = model(snake_case_ , attention_mask=snake_case_ )
snake_case_ = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A_ ( self : Dict , lowercase_ : str , lowercase_ : str , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : Dict , **lowercase_ : Optional[Any] , ):
snake_case_ = True
snake_case_ = BertGenerationEncoder(config=snake_case_ )
model.to(snake_case_ )
model.eval()
snake_case_ = model(
snake_case_ , attention_mask=snake_case_ , encoder_hidden_states=snake_case_ , encoder_attention_mask=snake_case_ , )
snake_case_ = model(
snake_case_ , attention_mask=snake_case_ , encoder_hidden_states=snake_case_ , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A_ ( self : str , lowercase_ : str , lowercase_ : Tuple , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : Any , **lowercase_ : Optional[int] , ):
snake_case_ = True
snake_case_ = True
snake_case_ = BertGenerationDecoder(config=snake_case_ ).to(snake_case_ ).eval()
# first forward pass
snake_case_ = model(
snake_case_ , attention_mask=snake_case_ , encoder_hidden_states=snake_case_ , encoder_attention_mask=snake_case_ , use_cache=snake_case_ , )
snake_case_ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
snake_case_ = torch.cat([input_ids, next_tokens] , dim=-1 )
snake_case_ = torch.cat([input_mask, next_mask] , dim=-1 )
snake_case_ = model(
snake_case_ , attention_mask=snake_case_ , encoder_hidden_states=snake_case_ , encoder_attention_mask=snake_case_ , output_hidden_states=snake_case_ , )["""hidden_states"""][0]
snake_case_ = model(
snake_case_ , attention_mask=snake_case_ , encoder_hidden_states=snake_case_ , encoder_attention_mask=snake_case_ , past_key_values=snake_case_ , output_hidden_states=snake_case_ , )["""hidden_states"""][0]
# select random slice
snake_case_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
snake_case_ = output_from_no_past[:, -3:, random_slice_idx].detach()
snake_case_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case_ , snake_case_ , atol=1e-3 ) )
def A_ ( self : List[str] , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : Optional[Any] , lowercase_ : Any , *lowercase_ : Union[str, Any] , ):
snake_case_ = BertGenerationDecoder(snake_case_ )
model.to(snake_case_ )
model.eval()
snake_case_ = model(snake_case_ , attention_mask=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A_ ( self : List[str] ):
snake_case_ = self.prepare_config_and_inputs()
snake_case_ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class a ( _a , _a , _a , unittest.TestCase ):
snake_case_ = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
snake_case_ = (BertGenerationDecoder,) if is_torch_available() else ()
snake_case_ = (
{"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
if is_torch_available()
else {}
)
def A_ ( self : Tuple ):
snake_case_ = BertGenerationEncoderTester(self )
snake_case_ = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def A_ ( self : List[Any] ):
self.config_tester.run_common_tests()
def A_ ( self : List[str] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def A_ ( self : Dict ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
snake_case_ = """bert"""
self.model_tester.create_and_check_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*snake_case_ )
def A_ ( self : Any ):
snake_case_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case_ )
def A_ ( self : Optional[int] ):
# This regression test was failing with PyTorch < 1.3
(
snake_case_
) = self.model_tester.prepare_config_and_inputs_for_decoder()
snake_case_ = None
self.model_tester.create_and_check_model_as_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*snake_case_ )
@slow
def A_ ( self : int ):
snake_case_ = BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
self.assertIsNotNone(snake_case_ )
@require_torch
class a ( unittest.TestCase ):
@slow
def A_ ( self : Any ):
snake_case_ = BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
snake_case_ = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]] )
with torch.no_grad():
snake_case_ = model(snake_case_ )[0]
snake_case_ = torch.Size([1, 8, 1024] )
self.assertEqual(output.shape , snake_case_ )
snake_case_ = torch.tensor(
[[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case_ , atol=1e-4 ) )
@require_torch
class a ( unittest.TestCase ):
@slow
def A_ ( self : Any ):
snake_case_ = BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
snake_case_ = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]] )
with torch.no_grad():
snake_case_ = model(snake_case_ )[0]
snake_case_ = torch.Size([1, 8, 5_0358] )
self.assertEqual(output.shape , snake_case_ )
snake_case_ = torch.tensor(
[[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case_ , atol=1e-4 ) )
| 371
|
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class a :
def __init__( self : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any]=13 , lowercase_ : int=64 , lowercase_ : Tuple=2 , lowercase_ : List[str]=3 , lowercase_ : str=True , lowercase_ : Dict=True , lowercase_ : int=32 , lowercase_ : int=5 , lowercase_ : Optional[Any]=4 , lowercase_ : Optional[Any]=37 , lowercase_ : List[Any]="gelu" , lowercase_ : Tuple=0.1 , lowercase_ : str=0.1 , lowercase_ : Any=10 , lowercase_ : List[str]=0.02 , lowercase_ : Tuple=[1, 16, 4, 4] , lowercase_ : Tuple=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = scope
snake_case_ = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
snake_case_ = (self.image_size // 32) ** 2
snake_case_ = num_patches + 1
def A_ ( self : List[Any] ):
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = self.get_config()
return config, pixel_values, labels
def A_ ( self : Any ):
snake_case_ = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 16, 32],
'''num_groups''': 2,
}
return ViTHybridConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase_ , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=lowercase_ , )
def A_ ( self : Optional[int] , lowercase_ : List[Any] , lowercase_ : int , lowercase_ : int ):
snake_case_ = ViTHybridModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A_ ( self : List[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Optional[int] ):
snake_case_ = self.type_sequence_label_size
snake_case_ = ViTHybridForImageClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A_ ( self : List[Any] ):
snake_case_ = self.prepare_config_and_inputs()
snake_case_ ,snake_case_ ,snake_case_ = config_and_inputs
snake_case_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
snake_case_ = (
{"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
def A_ ( self : Optional[Any] ):
snake_case_ = ViTHybridModelTester(self )
snake_case_ = ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ , hidden_size=37 )
def A_ ( self : Optional[int] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def A_ ( self : Any ):
pass
def A_ ( self : Dict ):
snake_case_ ,snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(lowercase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase_ , nn.Linear ) )
def A_ ( self : Dict ):
snake_case_ ,snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(lowercase_ )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowercase_ )
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_ )
def A_ ( self : Optional[Any] ):
snake_case_ ,snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = _config_zero_init(lowercase_ )
for model_class in self.all_model_classes:
snake_case_ = model_class(config=lowercase_ )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
snake_case_ = [F"{name}.{key}" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , )
@slow
def A_ ( self : Tuple ):
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = ViTHybridModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def __magic_name__ ( ) -> List[Any]:
'''simple docstring'''
snake_case_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class a ( unittest.TestCase ):
@cached_property
def A_ ( self : Any ):
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def A_ ( self : List[str] ):
snake_case_ = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
lowercase_ )
snake_case_ = self.default_image_processor
snake_case_ = prepare_img()
snake_case_ = image_processor(images=lowercase_ , return_tensors='''pt''' ).to(lowercase_ )
# forward pass
with torch.no_grad():
snake_case_ = model(**lowercase_ )
# verify the logits
snake_case_ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , lowercase_ )
snake_case_ = torch.tensor([-1.9090, -0.4993, -0.2389] ).to(lowercase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase_ , atol=1e-4 ) )
@slow
@require_accelerate
def A_ ( self : Dict ):
snake_case_ = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''' )
snake_case_ = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' , device_map='''auto''' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=lowercase_ , return_tensors='''pt''' )
snake_case_ = model(**lowercase_ )
snake_case_ = outputs.logits
# model predicts one of the 1000 ImageNet classes
snake_case_ = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , '''tabby, tabby cat''' )
| 72
| 0
|
import requests
from bsa import BeautifulSoup
def A ( _lowerCamelCase = "AAPL" ):
'''simple docstring'''
_lowerCAmelCase : str = F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
_lowerCAmelCase : Optional[int] = BeautifulSoup(requests.get(_lowerCamelCase ).text , "html.parser" )
_lowerCAmelCase : List[Any] = "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}''')
| 36
|
from __future__ import annotations
import typing
from collections import Counter
def __lowerCAmelCase ( a__ ) -> typing.Counter[int]:
__a = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(a__ , max_perimeter + 1 ):
__a = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(a__ ):
__a = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def __lowerCAmelCase ( a__ = 1000 ) -> int:
__a = pythagorean_triple(a__ )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(F"Perimeter {solution()} has maximum solutions")
| 6
| 0
|
"""simple docstring"""
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase_ : Union[str, Any] = 1_6
lowerCAmelCase_ : Optional[Any] = 3_2
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase = 16 ) -> Any:
'''simple docstring'''
UpperCAmelCase = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCAmelCase = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowerCAmelCase ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCAmelCase , max_length=lowerCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCAmelCase = datasets.map(
lowerCAmelCase , batched=lowerCAmelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCAmelCase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCAmelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCAmelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCAmelCase = 16
elif accelerator.mixed_precision != "no":
UpperCAmelCase = 8
else:
UpperCAmelCase = None
return tokenizer.pad(
lowerCAmelCase , padding="""longest""" , max_length=lowerCAmelCase , pad_to_multiple_of=lowerCAmelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
UpperCAmelCase = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCAmelCase , collate_fn=lowerCAmelCase , batch_size=lowerCAmelCase , drop_last=lowerCAmelCase )
UpperCAmelCase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCAmelCase , collate_fn=lowerCAmelCase , batch_size=lowerCAmelCase , drop_last=(accelerator.mixed_precision == """fp8""") , )
return train_dataloader, eval_dataloader
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase ) -> List[str]:
'''simple docstring'''
UpperCAmelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase = config["""lr"""]
UpperCAmelCase = int(config["""num_epochs"""] )
UpperCAmelCase = int(config["""seed"""] )
UpperCAmelCase = int(config["""batch_size"""] )
UpperCAmelCase = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
UpperCAmelCase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
UpperCAmelCase = batch_size // MAX_GPU_BATCH_SIZE
UpperCAmelCase = MAX_GPU_BATCH_SIZE
set_seed(lowerCAmelCase )
UpperCAmelCase , UpperCAmelCase = get_dataloaders(lowerCAmelCase , lowerCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowerCAmelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
UpperCAmelCase = AdamW(params=model.parameters() , lr=lowerCAmelCase )
# Instantiate scheduler
UpperCAmelCase = get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase , num_warmup_steps=100 , num_training_steps=(len(lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = accelerator.prepare(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Now we train the model
for epoch in range(lowerCAmelCase ):
model.train()
for step, batch in enumerate(lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
UpperCAmelCase = model(**lowerCAmelCase )
UpperCAmelCase = outputs.loss
UpperCAmelCase = loss / gradient_accumulation_steps
accelerator.backward(lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCAmelCase = model(**lowerCAmelCase )
UpperCAmelCase = outputs.logits.argmax(dim=-1 )
UpperCAmelCase , UpperCAmelCase = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowerCAmelCase , references=lowerCAmelCase , )
UpperCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowerCAmelCase )
def _lowerCAmelCase ( ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowerCAmelCase , default=lowerCAmelCase , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
UpperCAmelCase = parser.parse_args()
UpperCAmelCase = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowerCAmelCase , lowerCAmelCase )
if __name__ == "__main__":
main()
| 359
|
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=a_ )
class UpperCamelCase_ ( a_ ):
_A : str = field(default='image-classification' , metadata={'include_in_asdict_even_if_is_default': True} )
_A : ClassVar[Features] = Features({'image': Image()} )
_A : ClassVar[Features] = Features({'labels': ClassLabel} )
_A : str = "image"
_A : str = "labels"
def UpperCamelCase_ ( self , snake_case__ ) -> List[str]:
"""simple docstring"""
if self.label_column not in features:
raise ValueError(f'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , snake_case__ ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
UpperCAmelCase = copy.deepcopy(self )
UpperCAmelCase = self.label_schema.copy()
UpperCAmelCase = features[self.label_column]
UpperCAmelCase = label_schema
return task_template
@property
def UpperCamelCase_ ( self ) -> Dict[str, str]:
"""simple docstring"""
return {
self.image_column: "image",
self.label_column: "labels",
}
| 248
| 0
|
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase , lowercase , lowercase=1024 , lowercase=1024 , lowercase=3.6 ) -> Tuple:
'''simple docstring'''
A__ = tokenizer
A__ = tokenizer.bos_token_id
A__ = dataset
A__ = seq_length
A__ = seq_length * chars_per_token * num_of_sequences
def __iter__( self ) -> Tuple:
'''simple docstring'''
A__ = iter(self.dataset )
A__ = True
while more_examples:
A__ , A__ = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(lowercase )["content"] )
buffer_len += len(buffer[-1] )
except StopIteration:
A__ = False
break
A__ = tokenizer(lowercase , truncation=lowercase )["input_ids"]
A__ = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(lowercase ) , self.seq_length ):
A__ = all_token_ids[i : i + self.seq_length]
if len(lowercase ) == self.seq_length:
yield torch.tensor(lowercase )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] ) -> List[str]:
'''simple docstring'''
A__ = {"streaming": True}
A__ = load_dataset(args.dataset_name , split="train" , **SCREAMING_SNAKE_CASE_ )
A__ = ConstantLengthDataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , seq_length=args.seq_length )
A__ = DataLoader(SCREAMING_SNAKE_CASE_ , batch_size=args.batch_size )
return eval_dataloader
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[Any] ) -> int:
'''simple docstring'''
model.eval()
A__ = []
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
with torch.no_grad():
A__ = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
A__ = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(SCREAMING_SNAKE_CASE_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
A__ = torch.mean(torch.cat(SCREAMING_SNAKE_CASE_ ) )
try:
A__ = torch.exp(SCREAMING_SNAKE_CASE_ )
except OverflowError:
A__ = float("inf" )
return loss.item(), perplexity.item()
# Setup Accelerator
lowerCAmelCase__ = Accelerator()
# Parse configuration
lowerCAmelCase__ = HfArgumentParser(EvaluationArguments)
lowerCAmelCase__ = parser.parse_args()
set_seed(args.seed)
# Logging
lowerCAmelCase__ = logging.getLogger(__name__)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
# Load model and tokenizer
lowerCAmelCase__ = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowerCAmelCase__ = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
lowerCAmelCase__ = create_dataloader(args)
# Prepare everything with our `accelerator`.
lowerCAmelCase__ , lowerCAmelCase__ = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
lowerCAmelCase__ , lowerCAmelCase__ = evaluate(args)
logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")
| 68
|
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 68
| 1
|
def _a ( SCREAMING_SNAKE_CASE_ : list[list[int]] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : set ):
__lowerCAmelCase , __lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ ), len(grid[0] )
if (
min(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
__lowerCAmelCase = 0
count += depth_first_search(SCREAMING_SNAKE_CASE_ , row + 1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
count += depth_first_search(SCREAMING_SNAKE_CASE_ , row - 1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
count += depth_first_search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , col + 1 , SCREAMING_SNAKE_CASE_ )
count += depth_first_search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , col - 1 , SCREAMING_SNAKE_CASE_ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 356
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
UpperCamelCase__ = 16
UpperCamelCase__ = 32
def _a ( SCREAMING_SNAKE_CASE_ : Accelerator , SCREAMING_SNAKE_CASE_ : int = 16 ):
__lowerCAmelCase = AutoTokenizer.from_pretrained("bert-base-cased" )
__lowerCAmelCase = load_dataset("glue" , "mrpc" )
def tokenize_function(SCREAMING_SNAKE_CASE_ : str ):
# max_length=None => use the model max length (it's actually the default)
__lowerCAmelCase = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowerCAmelCase = datasets.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCAmelCase = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(SCREAMING_SNAKE_CASE_ : int ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowerCAmelCase = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowerCAmelCase = 16
elif accelerator.mixed_precision != "no":
__lowerCAmelCase = 8
else:
__lowerCAmelCase = None
return tokenizer.pad(
SCREAMING_SNAKE_CASE_ , padding="longest" , max_length=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_tensors="pt" , )
# Instantiate dataloaders.
__lowerCAmelCase = DataLoader(
tokenized_datasets["train"] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = DataLoader(
tokenized_datasets["validation"] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
UpperCamelCase__ = mocked_dataloaders # noqa: F811
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS" , SCREAMING_SNAKE_CASE_ ) == "1":
__lowerCAmelCase = 2
# New Code #
__lowerCAmelCase = int(args.gradient_accumulation_steps )
# Initialize accelerator
__lowerCAmelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=SCREAMING_SNAKE_CASE_ )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
"Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCAmelCase = config["lr"]
__lowerCAmelCase = int(config["num_epochs"] )
__lowerCAmelCase = int(config["seed"] )
__lowerCAmelCase = int(config["batch_size"] )
__lowerCAmelCase = evaluate.load("glue" , "mrpc" )
set_seed(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase , __lowerCAmelCase = get_dataloaders(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=SCREAMING_SNAKE_CASE_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowerCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
__lowerCAmelCase = AdamW(params=model.parameters() , lr=SCREAMING_SNAKE_CASE_ )
# Instantiate scheduler
__lowerCAmelCase = get_linear_schedule_with_warmup(
optimizer=SCREAMING_SNAKE_CASE_ , num_warmup_steps=1_00 , num_training_steps=(len(SCREAMING_SNAKE_CASE_ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = accelerator.prepare(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Now we train the model
for epoch in range(SCREAMING_SNAKE_CASE_ ):
model.train()
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(SCREAMING_SNAKE_CASE_ ):
__lowerCAmelCase = model(**SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = output.loss
accelerator.backward(SCREAMING_SNAKE_CASE_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowerCAmelCase = model(**SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = outputs.logits.argmax(dim=-1 )
__lowerCAmelCase , __lowerCAmelCase = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ , )
__lowerCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , SCREAMING_SNAKE_CASE_ )
def _a ( ):
__lowerCAmelCase = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
# New Code #
parser.add_argument(
"--gradient_accumulation_steps" , type=SCREAMING_SNAKE_CASE_ , default=1 , help="The number of minibatches to be ran before gradients are accumulated." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main()
| 102
| 0
|
"""simple docstring"""
import 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 lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self: int ) -> Optional[int]:
snake_case_ :str = inspect.getfile(accelerate.test_utils )
snake_case_ :List[str] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_script.py"""] )
snake_case_ :Optional[Any] = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_distributed_data_loop.py"""] )
snake_case_ :str = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_ops.py"""] )
@require_multi_gpu
def lowerCAmelCase_ ( self: Optional[Any] ) -> List[str]:
print(f"""Found {torch.cuda.device_count()} devices.""" )
snake_case_ :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 lowerCAmelCase_ ( self: Optional[Any] ) -> str:
print(f"""Found {torch.cuda.device_count()} devices.""" )
snake_case_ :str = ["""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 lowerCAmelCase_ ( self: Union[str, Any] ) -> List[str]:
snake_case_ :List[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 lowerCAmelCase_ ( self: str ) -> List[str]:
print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only""" )
snake_case_ :Optional[int] = ["""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 = Accelerator()
__a = (accelerator.state.process_index + 2, 10)
__a = torch.randint(0, 10, shape).to(accelerator.device)
__a = ""
__a = 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 = 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 = 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)
| 66
|
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class lowerCamelCase :
'''simple docstring'''
def __init__( self: Dict , snake_case: Optional[Any] , snake_case: Tuple=13 , snake_case: Any=32 , snake_case: Union[str, Any]=2 , snake_case: Tuple=3 , snake_case: Union[str, Any]=16 , snake_case: Union[str, Any]=[1, 2, 1] , snake_case: Optional[Any]=[2, 2, 4] , snake_case: str=2 , snake_case: List[str]=2.0 , snake_case: Optional[int]=True , snake_case: Union[str, Any]=0.0 , snake_case: Optional[int]=0.0 , snake_case: Optional[Any]=0.1 , snake_case: List[str]="gelu" , snake_case: Any=False , snake_case: Optional[Any]=True , snake_case: Optional[int]=0.0_2 , snake_case: Any=1E-5 , snake_case: Optional[int]=True , snake_case: int=None , snake_case: Any=True , snake_case: str=10 , snake_case: Optional[Any]=8 , snake_case: Union[str, Any]=["stage1", "stage2", "stage3"] , snake_case: Tuple=[1, 2, 3] , ) -> Dict:
snake_case_ :Dict = parent
snake_case_ :List[Any] = batch_size
snake_case_ :Dict = image_size
snake_case_ :Dict = patch_size
snake_case_ :Tuple = num_channels
snake_case_ :List[Any] = embed_dim
snake_case_ :List[str] = depths
snake_case_ :str = num_heads
snake_case_ :Tuple = window_size
snake_case_ :Tuple = mlp_ratio
snake_case_ :int = qkv_bias
snake_case_ :Tuple = hidden_dropout_prob
snake_case_ :Optional[Any] = attention_probs_dropout_prob
snake_case_ :Dict = drop_path_rate
snake_case_ :Any = hidden_act
snake_case_ :Any = use_absolute_embeddings
snake_case_ :int = patch_norm
snake_case_ :List[Any] = layer_norm_eps
snake_case_ :Tuple = initializer_range
snake_case_ :str = is_training
snake_case_ :int = scope
snake_case_ :Tuple = use_labels
snake_case_ :Tuple = type_sequence_label_size
snake_case_ :str = encoder_stride
snake_case_ :List[Any] = out_features
snake_case_ :str = out_indices
def lowerCAmelCase_ ( self: Tuple ) -> Dict:
snake_case_ :Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ :str = None
if self.use_labels:
snake_case_ :Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ :Union[str, Any] = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase_ ( self: int ) -> Optional[Any]:
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def lowerCAmelCase_ ( self: List[Any] , snake_case: str , snake_case: int , snake_case: List[str] ) -> Any:
snake_case_ :Dict = MaskFormerSwinModel(config=snake_case )
model.to(snake_case )
model.eval()
snake_case_ :Tuple = model(snake_case )
snake_case_ :Dict = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case_ :Any = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def lowerCAmelCase_ ( self: Optional[Any] , snake_case: int , snake_case: List[str] , snake_case: Tuple ) -> Union[str, Any]:
snake_case_ :Any = MaskFormerSwinBackbone(config=snake_case )
model.to(snake_case )
model.eval()
snake_case_ :Optional[Any] = model(snake_case )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(snake_case ):
snake_case_ :Optional[Any] = ["""stem"""]
snake_case_ :str = MaskFormerSwinBackbone(config=snake_case )
def lowerCAmelCase_ ( self: List[str] ) -> Optional[Any]:
snake_case_ :Optional[int] = self.prepare_config_and_inputs()
snake_case_, snake_case_, snake_case_ :str = config_and_inputs
snake_case_ :Tuple = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
_A : Union[str, Any] = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
_A : str = {"""feature-extraction""": MaskFormerSwinModel} if is_torch_available() else {}
_A : List[str] = False
_A : Any = False
_A : Dict = False
_A : List[Any] = False
_A : Optional[int] = False
def lowerCAmelCase_ ( self: Dict ) -> Any:
snake_case_ :str = MaskFormerSwinModelTester(self )
snake_case_ :Optional[Any] = ConfigTester(self , config_class=snake_case , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"""`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
""" `nn.DataParallel`"""
) )
def lowerCAmelCase_ ( self: List[str] ) -> Optional[int]:
pass
def lowerCAmelCase_ ( self: Union[str, Any] ) -> Dict:
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 lowerCAmelCase_ ( self: Any ) -> Tuple:
return
def lowerCAmelCase_ ( self: Any ) -> Any:
snake_case_ :List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCAmelCase_ ( self: Union[str, Any] ) -> int:
snake_case_ :Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*snake_case )
@unittest.skip("""Swin does not use inputs_embeds""" )
def lowerCAmelCase_ ( self: str ) -> List[str]:
pass
@unittest.skip("""Swin does not support feedforward chunking""" )
def lowerCAmelCase_ ( self: int ) -> Optional[int]:
pass
def lowerCAmelCase_ ( self: List[str] ) -> List[Any]:
snake_case_, snake_case_ :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ :str = model_class(snake_case )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ :Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case , nn.Linear ) )
def lowerCAmelCase_ ( self: Tuple ) -> Dict:
snake_case_, snake_case_ :int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ :Optional[int] = model_class(snake_case )
snake_case_ :str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ :str = [*signature.parameters.keys()]
snake_case_ :str = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , snake_case )
@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions""" )
def lowerCAmelCase_ ( self: List[Any] ) -> List[Any]:
pass
@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone""" )
def lowerCAmelCase_ ( self: Dict ) -> List[Any]:
pass
def lowerCAmelCase_ ( self: Union[str, Any] , snake_case: Union[str, Any] , snake_case: int , snake_case: Any , snake_case: List[str] ) -> str:
snake_case_ :List[str] = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
snake_case_ :List[Any] = model(**self._prepare_for_class(snake_case , snake_case ) )
snake_case_ :Any = outputs.hidden_states
snake_case_ :Optional[int] = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(snake_case ) , snake_case )
# Swin has a different seq_length
snake_case_ :str = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ :int = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def lowerCAmelCase_ ( self: List[Any] ) -> Optional[int]:
snake_case_, snake_case_ :Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ :List[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
snake_case_ :Tuple = True
self.check_hidden_states_output(snake_case , snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ :List[Any] = True
self.check_hidden_states_output(snake_case , snake_case , snake_case , snake_case )
def lowerCAmelCase_ ( self: Optional[Any] ) -> Tuple:
snake_case_, snake_case_ :int = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ :List[Any] = 3
snake_case_ :List[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case_ :Any = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ :Tuple = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case_ :List[str] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
snake_case_ :str = True
self.check_hidden_states_output(snake_case , snake_case , snake_case , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ :Any = True
self.check_hidden_states_output(snake_case , snake_case , snake_case , (padded_height, padded_width) )
@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints""" )
def lowerCAmelCase_ ( self: Union[str, Any] ) -> List[str]:
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def lowerCAmelCase_ ( self: List[str] ) -> str:
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def lowerCAmelCase_ ( self: str ) -> List[Any]:
pass
def lowerCAmelCase_ ( self: Union[str, Any] ) -> Optional[Any]:
snake_case_, snake_case_ :Dict = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(snake_case: str ):
snake_case_ :Optional[int] = 0
return t
def check_equivalence(snake_case: List[Any] , snake_case: Union[str, Any] , snake_case: int , snake_case: Tuple={} ):
with torch.no_grad():
snake_case_ :List[Any] = model(**snake_case , return_dict=snake_case , **snake_case )
snake_case_ :Any = model(**snake_case , return_dict=snake_case , **snake_case ).to_tuple()
def recursive_check(snake_case: List[Any] , snake_case: int ):
if isinstance(snake_case , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(snake_case , snake_case ):
recursive_check(snake_case , snake_case )
elif isinstance(snake_case , snake_case ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(snake_case , snake_case )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(snake_case ) , set_nan_tensor_to_zero(snake_case ) , atol=1E-5 ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
f""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
f""" {torch.isnan(snake_case ).any()} and `inf`: {torch.isinf(snake_case )}. Dict has"""
f""" `nan`: {torch.isnan(snake_case ).any()} and `inf`: {torch.isinf(snake_case )}."""
) , )
recursive_check(snake_case , snake_case )
for model_class in self.all_model_classes:
snake_case_ :int = model_class(snake_case )
model.to(snake_case )
model.eval()
snake_case_ :Any = self._prepare_for_class(snake_case , snake_case )
snake_case_ :List[Any] = self._prepare_for_class(snake_case , snake_case )
check_equivalence(snake_case , snake_case , snake_case )
snake_case_ :Tuple = self._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
snake_case_ :Dict = self._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
check_equivalence(snake_case , snake_case , snake_case )
snake_case_ :Tuple = self._prepare_for_class(snake_case , snake_case )
snake_case_ :Any = self._prepare_for_class(snake_case , snake_case )
check_equivalence(snake_case , snake_case , snake_case , {"""output_hidden_states""": True} )
snake_case_ :Dict = self._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
snake_case_ :List[str] = self._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
check_equivalence(snake_case , snake_case , snake_case , {"""output_hidden_states""": True} )
@require_torch
class lowerCamelCase ( unittest.TestCase , _lowerCAmelCase ):
'''simple docstring'''
_A : int = (MaskFormerSwinBackbone,) if is_torch_available() else ()
_A : Tuple = MaskFormerSwinConfig
def lowerCAmelCase_ ( self: List[str] ) -> Optional[int]:
snake_case_ :Optional[Any] = MaskFormerSwinModelTester(self )
def lowerCAmelCase_ ( self: int ) -> Optional[int]:
snake_case_, snake_case_ :Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ :Tuple = inputs_dict["""pixel_values"""].shape[0]
for backbone_class in self.all_model_classes:
snake_case_ :List[str] = backbone_class(snake_case )
backbone.to(snake_case )
backbone.eval()
snake_case_ :List[Any] = backbone(**snake_case )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , snake_case )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
snake_case_ :Union[str, Any] = backbone(**snake_case , output_hidden_states=snake_case )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
snake_case_, snake_case_, snake_case_ :List[Any] = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
snake_case_ :List[Any] = backbone(**snake_case , output_attentions=snake_case )
self.assertIsNotNone(outputs.attentions )
| 66
| 1
|
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __UpperCAmelCase ( __a : int ) -> bool:
"""simple docstring"""
_a : int = int(number**0.5 )
return number == sq * sq
def __UpperCAmelCase ( __a : int ,__a : int ,__a : int ,__a : int ,__a : int ,__a : int ) -> tuple[int, int]:
"""simple docstring"""
_a : int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
_a : int = x_den * y_den * z_den
_a : int = gcd(__a ,__a )
top //= hcf
bottom //= hcf
return top, bottom
def __UpperCAmelCase ( __a : int = 35 ) -> int:
"""simple docstring"""
_a : set = set()
_a : int
_a : Fraction = Fraction(0 )
_a : tuple[int, int]
for x_num in range(1 ,order + 1 ):
for x_den in range(x_num + 1 ,order + 1 ):
for y_num in range(1 ,order + 1 ):
for y_den in range(y_num + 1 ,order + 1 ):
# n=1
_a : Optional[Any] = x_num * y_den + x_den * y_num
_a : Any = x_den * y_den
_a : List[str] = gcd(__a ,__a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_a : List[Any] = add_three(
__a ,__a ,__a ,__a ,__a ,__a )
unique_s.add(__a )
# n=2
_a : Dict = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
_a : Tuple = x_den * x_den * y_den * y_den
if is_sq(__a ) and is_sq(__a ):
_a : List[str] = int(sqrt(__a ) )
_a : List[str] = int(sqrt(__a ) )
_a : List[Any] = gcd(__a ,__a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_a : List[Any] = add_three(
__a ,__a ,__a ,__a ,__a ,__a )
unique_s.add(__a )
# n=-1
_a : Optional[Any] = x_num * y_num
_a : List[Any] = x_den * y_num + x_num * y_den
_a : List[Any] = gcd(__a ,__a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_a : Any = add_three(
__a ,__a ,__a ,__a ,__a ,__a )
unique_s.add(__a )
# n=2
_a : int = x_num * x_num * y_num * y_num
_a : Tuple = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(__a ) and is_sq(__a ):
_a : str = int(sqrt(__a ) )
_a : Optional[int] = int(sqrt(__a ) )
_a : Optional[int] = gcd(__a ,__a )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
_a : Optional[int] = add_three(
__a ,__a ,__a ,__a ,__a ,__a )
unique_s.add(__a )
for num, den in unique_s:
total += Fraction(__a ,__a )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f'''{solution() = }''')
| 366
|
from __future__ import annotations
def __UpperCAmelCase ( __a : list ) -> float:
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(__a ) / len(__a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 15
| 0
|
'''simple docstring'''
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
_A : Tuple =logging.get_logger(__name__)
_A : str ={
'''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
'''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
'''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class _lowercase ( _lowercase ):
a = """mobilenet_v2"""
def __init__( self: List[Any] , UpperCamelCase__: List[Any]=3 , UpperCamelCase__: Dict=224 , UpperCamelCase__: Optional[Any]=1.0 , UpperCamelCase__: Tuple=8 , UpperCamelCase__: Dict=8 , UpperCamelCase__: List[str]=6 , UpperCamelCase__: Tuple=32 , UpperCamelCase__: List[Any]=True , UpperCamelCase__: str=True , UpperCamelCase__: Dict="relu6" , UpperCamelCase__: Dict=True , UpperCamelCase__: Union[str, Any]=0.8 , UpperCamelCase__: List[Any]=0.02 , UpperCamelCase__: str=0.001 , UpperCamelCase__: Union[str, Any]=255 , **UpperCamelCase__: Dict , ):
super().__init__(**UpperCamelCase__ )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
lowerCamelCase__ : str = num_channels
lowerCamelCase__ : Tuple = image_size
lowerCamelCase__ : str = depth_multiplier
lowerCamelCase__ : Optional[Any] = depth_divisible_by
lowerCamelCase__ : List[str] = min_depth
lowerCamelCase__ : Tuple = expand_ratio
lowerCamelCase__ : Union[str, Any] = output_stride
lowerCamelCase__ : str = first_layer_is_expansion
lowerCamelCase__ : List[Any] = finegrained_output
lowerCamelCase__ : Tuple = hidden_act
lowerCamelCase__ : int = tf_padding
lowerCamelCase__ : List[str] = classifier_dropout_prob
lowerCamelCase__ : Optional[Any] = initializer_range
lowerCamelCase__ : Union[str, Any] = layer_norm_eps
lowerCamelCase__ : List[str] = semantic_loss_ignore_index
class _lowercase ( _lowercase ):
a = version.parse("""1.11""" )
@property
def lowerCamelCase_ ( self: Tuple ):
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def lowerCamelCase_ ( self: Optional[Any] ):
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def lowerCamelCase_ ( self: str ):
return 1e-4
| 41
|
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class __snake_case ( unittest.TestCase):
def SCREAMING_SNAKE_CASE ( self : List[str] ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
_lowerCamelCase : Union[str, Any] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(__lowerCAmelCase )
_lowerCamelCase : Tuple = -1
_lowerCamelCase : List[Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(__lowerCAmelCase )
_lowerCamelCase : List[Any] = model.generate(__lowerCAmelCase , max_new_tokens=1_0 , do_sample=__lowerCAmelCase )
_lowerCamelCase : Optional[Any] = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
_lowerCamelCase : Union[str, Any] = TextStreamer(__lowerCAmelCase )
model.generate(__lowerCAmelCase , max_new_tokens=1_0 , do_sample=__lowerCAmelCase , streamer=__lowerCAmelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_lowerCamelCase : int = cs.out[:-1]
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
_lowerCamelCase : Optional[int] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(__lowerCAmelCase )
_lowerCamelCase : Tuple = -1
_lowerCamelCase : List[Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(__lowerCAmelCase )
_lowerCamelCase : Optional[int] = model.generate(__lowerCAmelCase , max_new_tokens=1_0 , do_sample=__lowerCAmelCase )
_lowerCamelCase : List[str] = tokenizer.decode(greedy_ids[0] )
_lowerCamelCase : Tuple = TextIteratorStreamer(__lowerCAmelCase )
_lowerCamelCase : Tuple = {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
_lowerCamelCase : List[Any] = Thread(target=model.generate , kwargs=__lowerCAmelCase )
thread.start()
_lowerCamelCase : int = ''''''
for new_text in streamer:
streamer_text += new_text
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
_lowerCamelCase : Dict = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
_lowerCamelCase : str = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(__lowerCAmelCase )
_lowerCamelCase : Tuple = -1
_lowerCamelCase : Optional[Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(__lowerCAmelCase )
_lowerCamelCase : int = model.generate(__lowerCAmelCase , max_new_tokens=1_0 , do_sample=__lowerCAmelCase )
_lowerCamelCase : Optional[Any] = greedy_ids[:, input_ids.shape[1] :]
_lowerCamelCase : int = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
_lowerCamelCase : Any = TextStreamer(__lowerCAmelCase , skip_prompt=__lowerCAmelCase )
model.generate(__lowerCAmelCase , max_new_tokens=1_0 , do_sample=__lowerCAmelCase , streamer=__lowerCAmelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_lowerCamelCase : Union[str, Any] = cs.out[:-1]
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained('''distilgpt2''' )
_lowerCamelCase : Optional[Any] = AutoModelForCausalLM.from_pretrained('''distilgpt2''' ).to(__lowerCAmelCase )
_lowerCamelCase : str = -1
_lowerCamelCase : Any = torch.ones((1, 5) , device=__lowerCAmelCase ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
_lowerCamelCase : List[Any] = TextStreamer(__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase )
model.generate(__lowerCAmelCase , max_new_tokens=1 , do_sample=__lowerCAmelCase , streamer=__lowerCAmelCase )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
_lowerCamelCase : Any = cs.out[:-1] # Remove the final "\n"
_lowerCamelCase : int = tokenizer(__lowerCAmelCase , return_tensors='''pt''' )
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1) )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
"""simple docstring"""
_lowerCamelCase : List[str] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
_lowerCamelCase : Dict = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = -1
_lowerCamelCase : Any = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(__lowerCAmelCase )
_lowerCamelCase : List[str] = TextIteratorStreamer(__lowerCAmelCase , timeout=0.0_01 )
_lowerCamelCase : str = {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
_lowerCamelCase : List[Any] = Thread(target=model.generate , kwargs=__lowerCAmelCase )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(__lowerCAmelCase ):
_lowerCamelCase : Optional[Any] = ''''''
for new_text in streamer:
streamer_text += new_text
| 72
| 0
|
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,
set_seed,
)
def _snake_case ( ) -> Dict:
lowerCamelCase_ : int =argparse.ArgumentParser()
parser.add_argument("--model_ckpt" , type=lowerCamelCase__ , default="microsoft/unixcoder-base-nine" )
parser.add_argument("--num_epochs" , type=lowerCamelCase__ , default=5 )
parser.add_argument("--batch_size" , type=lowerCamelCase__ , default=6 )
parser.add_argument("--gradient_accumulation_steps" , type=lowerCamelCase__ , default=1 )
parser.add_argument("--freeze" , type=lowerCamelCase__ , default=lowerCamelCase__ )
parser.add_argument("--learning_rate" , type=lowerCamelCase__ , default=5e-4 )
parser.add_argument("--seed" , type=lowerCamelCase__ , default=0 )
parser.add_argument("--lr_scheduler_type" , type=lowerCamelCase__ , default="cosine" )
parser.add_argument("--num_warmup_steps" , type=lowerCamelCase__ , default=10 )
parser.add_argument("--weight_decay" , type=lowerCamelCase__ , default=0.01 )
parser.add_argument("--output_dir" , type=lowerCamelCase__ , default="./results" )
return parser.parse_args()
A__ : List[Any] = load('accuracy')
def _snake_case ( lowerCamelCase__ : Tuple ) -> Optional[Any]:
lowerCamelCase_ : Dict =eval_pred
lowerCamelCase_ : Tuple =np.argmax(lowerCamelCase__ , axis=1 )
return metric.compute(predictions=lowerCamelCase__ , references=lowerCamelCase__ )
class lowercase__ ( snake_case__ ):
def __init__( self : int , snake_case__ : int ):
super().__init__()
lowerCamelCase_ : Optional[int] =trainer
def UpperCAmelCase__ ( self : str , snake_case__ : Dict , snake_case__ : List[Any] , snake_case__ : List[str] , **snake_case__ : List[str] ):
if control.should_evaluate:
lowerCamelCase_ : List[str] =deepcopy(snake_case__ )
self._trainer.evaluate(eval_dataset=self._trainer.train_dataset , metric_key_prefix="train" )
return control_copy
def _snake_case ( ) -> Any:
lowerCamelCase_ : int =get_args()
set_seed(args.seed )
lowerCamelCase_ : List[Any] =load_dataset("codeparrot/codecomplex" , split="train" )
lowerCamelCase_ : List[Any] =dataset.train_test_split(test_size=0.2 )
lowerCamelCase_ : Union[str, Any] =train_test["test"].train_test_split(test_size=0.5 )
lowerCamelCase_ : Dict =DatasetDict(
{
"train": train_test["train"],
"test": test_validation["train"],
"valid": test_validation["test"],
} )
print("Loading tokenizer and model" )
lowerCamelCase_ : Optional[Any] =AutoTokenizer.from_pretrained(args.model_ckpt )
lowerCamelCase_ : List[Any] =tokenizer.eos_token
lowerCamelCase_ : Union[str, Any] =AutoModelForSequenceClassification.from_pretrained(args.model_ckpt , num_labels=7 )
lowerCamelCase_ : Optional[Any] =model.config.eos_token_id
if args.freeze:
for param in model.roberta.parameters():
lowerCamelCase_ : Dict =False
lowerCamelCase_ : Dict =ClassLabel(num_classes=7 , names=list(set(train_test_validation["train"]["complexity"] ) ) )
def tokenize(lowerCamelCase__ : List[Any] ):
lowerCamelCase_ : Any =tokenizer(example["src"] , truncation=lowerCamelCase__ , max_length=1_024 )
lowerCamelCase_ : Dict =labels.straint(example["complexity"] )
return {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"label": label,
}
lowerCamelCase_ : Union[str, Any] =train_test_validation.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=train_test_validation["train"].column_names , )
lowerCamelCase_ : Optional[Any] =DataCollatorWithPadding(tokenizer=lowerCamelCase__ )
lowerCamelCase_ : int =TrainingArguments(
output_dir=args.output_dir , learning_rate=args.learning_rate , lr_scheduler_type=args.lr_scheduler_type , evaluation_strategy="epoch" , save_strategy="epoch" , logging_strategy="epoch" , per_device_train_batch_size=args.batch_size , per_device_eval_batch_size=args.batch_size , num_train_epochs=args.num_epochs , gradient_accumulation_steps=args.gradient_accumulation_steps , weight_decay=0.01 , metric_for_best_model="accuracy" , run_name="complexity-java" , report_to="wandb" , )
lowerCamelCase_ : Any =Trainer(
model=lowerCamelCase__ , args=lowerCamelCase__ , train_dataset=tokenized_datasets["train"] , eval_dataset=tokenized_datasets["valid"] , tokenizer=lowerCamelCase__ , data_collator=lowerCamelCase__ , compute_metrics=lowerCamelCase__ , )
print("Training..." )
trainer.add_callback(CustomCallback(lowerCamelCase__ ) )
trainer.train()
if __name__ == "__main__":
main()
| 361
|
"""simple docstring"""
def _snake_case ( lowerCamelCase__ : Optional[Any] ) -> Optional[int]:
if not head:
return True
# split the list to two parts
lowerCamelCase_ , lowerCamelCase_ : Union[str, Any] =head.next, head
while fast and fast.next:
lowerCamelCase_ : Optional[Any] =fast.next.next
lowerCamelCase_ : str =slow.next
lowerCamelCase_ : Tuple =slow.next
lowerCamelCase_ : Any =None # Don't forget here! But forget still works!
# reverse the second part
lowerCamelCase_ : List[str] =None
while second:
lowerCamelCase_ : Any =second.next
lowerCamelCase_ : Union[str, Any] =node
lowerCamelCase_ : Union[str, Any] =second
lowerCamelCase_ : Optional[Any] =nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
lowerCamelCase_ : List[str] =node.next
lowerCamelCase_ : Optional[Any] =head.next
return True
def _snake_case ( lowerCamelCase__ : str ) -> Optional[int]:
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
lowerCamelCase_ : List[str] =head
while fast and fast.next:
lowerCamelCase_ , lowerCamelCase_ : Union[str, Any] =fast.next.next, slow.next
# 2. Push the second half into the stack
lowerCamelCase_ : List[Any] =[slow.val]
while slow.next:
lowerCamelCase_ : List[Any] =slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
lowerCamelCase_ : Union[str, Any] =cur.next
return True
def _snake_case ( lowerCamelCase__ : Dict ) -> Optional[Any]:
if not head or not head.next:
return True
lowerCamelCase_ : Union[str, Any] ={}
lowerCamelCase_ : List[Any] =0
while head:
if head.val in d:
d[head.val].append(lowerCamelCase__ )
else:
lowerCamelCase_ : List[str] =[pos]
lowerCamelCase_ : Optional[int] =head.next
pos += 1
lowerCamelCase_ : Union[str, Any] =pos - 1
lowerCamelCase_ : Optional[int] =0
for v in d.values():
if len(lowerCamelCase__ ) % 2 != 0:
middle += 1
else:
lowerCamelCase_ : Optional[Any] =0
for i in range(0 , len(lowerCamelCase__ ) ):
if v[i] + v[len(lowerCamelCase__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 209
| 0
|
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__lowerCAmelCase = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : List[Any] = TextaTextGenerationPipeline(model=_lowercase , tokenizer=_lowercase )
return generator, ["Something to write", "Something else"]
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase ):
__a : Any = generator('''Something there''' )
self.assertEqual(_lowercase , [{'''generated_text''': ANY(_lowercase )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there''' ) )
__a : Any = generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=_lowercase )
self.assertEqual(
_lowercase , [
[{'''generated_text''': ANY(_lowercase )}, {'''generated_text''': ANY(_lowercase )}],
[{'''generated_text''': ANY(_lowercase )}, {'''generated_text''': ANY(_lowercase )}],
] , )
__a : str = generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=_lowercase )
self.assertEqual(
_lowercase , [
[{'''generated_text''': ANY(_lowercase )}, {'''generated_text''': ANY(_lowercase )}],
[{'''generated_text''': ANY(_lowercase )}, {'''generated_text''': ANY(_lowercase )}],
] , )
with self.assertRaises(_lowercase ):
generator(4 )
@require_torch
def _lowerCamelCase ( self ):
__a : Union[str, Any] = pipeline('''text2text-generation''' , model='''patrickvonplaten/t5-tiny-random''' , framework='''pt''' )
# do_sample=False necessary for reproducibility
__a : List[str] = generator('''Something there''' , do_sample=_lowercase )
self.assertEqual(_lowercase , [{'''generated_text''': ''''''}] )
__a : Dict = 3
__a : Optional[Any] = generator(
'''Something there''' , num_return_sequences=_lowercase , num_beams=_lowercase , )
__a : Dict = [
{"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
{"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
{"""generated_text""": """"""},
]
self.assertEqual(_lowercase , _lowercase )
__a : Optional[Any] = generator('''This is a test''' , do_sample=_lowercase , num_return_sequences=2 , return_tensors=_lowercase )
self.assertEqual(
_lowercase , [
{'''generated_token_ids''': ANY(torch.Tensor )},
{'''generated_token_ids''': ANY(torch.Tensor )},
] , )
__a : List[Any] = generator.model.config.eos_token_id
__a : Optional[Any] = """<pad>"""
__a : Optional[int] = generator(
['''This is a test''', '''This is a second test'''] , do_sample=_lowercase , num_return_sequences=2 , batch_size=2 , return_tensors=_lowercase , )
self.assertEqual(
_lowercase , [
[
{'''generated_token_ids''': ANY(torch.Tensor )},
{'''generated_token_ids''': ANY(torch.Tensor )},
],
[
{'''generated_token_ids''': ANY(torch.Tensor )},
{'''generated_token_ids''': ANY(torch.Tensor )},
],
] , )
@require_tf
def _lowerCamelCase ( self ):
__a : Tuple = pipeline('''text2text-generation''' , model='''patrickvonplaten/t5-tiny-random''' , framework='''tf''' )
# do_sample=False necessary for reproducibility
__a : List[Any] = generator('''Something there''' , do_sample=_lowercase )
self.assertEqual(_lowercase , [{'''generated_text''': ''''''}] )
| 160
|
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
__snake_case : Optional[int] = """sshleifer/mar_enro_6_3_student"""
class A__(a_ ):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Tuple:
super().setUp()
a_ : Union[str, Any] = cached_path(
"""https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz""" , extract_compressed_file=_lowercase , )
a_ : Union[str, Any] = F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Tuple:
MarianMTModel.from_pretrained(_lowercase )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> int:
a_ : Any = {
"""$MAX_LEN""": 64,
"""$BS""": 64,
"""$GAS""": 1,
"""$ENRO_DIR""": self.data_dir,
"""facebook/mbart-large-cc25""": MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
"""--learning_rate=3e-5""": """--learning_rate 3e-4""",
"""--num_train_epochs 6""": """--num_train_epochs 1""",
}
# Clean up bash script
a_ : List[str] = (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py""" )[1].strip()
a_ : Dict = bash_script.replace("""\\\n""" , """""" ).strip().replace("""\"$@\"""" , """""" )
for k, v in env_vars_to_replace.items():
a_ : Optional[int] = bash_script.replace(_lowercase , str(_lowercase ) )
a_ : int = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
a_ : Dict = F'''
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
'''.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
a_ : Union[str, Any] = ["""finetune.py"""] + bash_script.split() + args
with patch.object(_lowercase , """argv""" , _lowercase ):
a_ : Optional[Any] = argparse.ArgumentParser()
a_ : Tuple = pl.Trainer.add_argparse_args(_lowercase )
a_ : Any = SummarizationModule.add_model_specific_args(_lowercase , os.getcwd() )
a_ : str = parser.parse_args()
a_ : Union[str, Any] = main(_lowercase )
# Check metrics
a_ : Any = load_json(model.metrics_save_path )
a_ : List[Any] = metrics["""val"""][0]
a_ : Union[str, Any] = metrics["""val"""][-1]
self.assertEqual(len(metrics["""val"""] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , _lowercase )
self.assertGreater(last_step_stats["""val_avg_gen_time"""] , 0.0_1 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats["""val_avg_gen_time"""] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats["""val_avg_bleu"""] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
a_ : Optional[Any] = os.listdir(_lowercase )
a_ : Dict = [x for x in contents if x.endswith(""".ckpt""" )][0]
a_ : str = os.path.join(args.output_dir , _lowercase )
a_ : Any = torch.load(_lowercase , map_location="""cpu""" )
a_ : Union[str, Any] = """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
a_ : List[Any] = {os.path.basename(_lowercase ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["""test"""] ) == 1
class A__(a_ ):
"""simple docstring"""
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Optional[Any]:
a_ : Tuple = F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
a_ : str = {
"""--fp16_opt_level=O1""": """""",
"""$MAX_LEN""": 128,
"""$BS""": 16,
"""$GAS""": 1,
"""$ENRO_DIR""": data_dir,
"""$m""": """sshleifer/student_marian_en_ro_6_1""",
"""val_check_interval=0.25""": """val_check_interval=1.0""",
}
# Clean up bash script
a_ : Union[str, Any] = (
(self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py""" )[1].strip()
)
a_ : Union[str, Any] = bash_script.replace("""\\\n""" , """""" ).strip().replace("""\"$@\"""" , """""" )
a_ : Any = bash_script.replace("""--fp16 """ , """ """ )
for k, v in env_vars_to_replace.items():
a_ : Dict = bash_script.replace(_lowercase , str(_lowercase ) )
a_ : int = self.get_auto_remove_tmp_dir()
a_ : Optional[Any] = bash_script.replace("""--fp16""" , """""" )
a_ : List[str] = 6
a_ : str = (
["""distillation.py"""]
+ bash_script.split()
+ [
F'''--output_dir={output_dir}''',
"""--gpus=1""",
"""--learning_rate=1e-3""",
F'''--num_train_epochs={epochs}''',
"""--warmup_steps=10""",
"""--val_check_interval=1.0""",
"""--do_predict""",
]
)
with patch.object(_lowercase , """argv""" , _lowercase ):
a_ : int = argparse.ArgumentParser()
a_ : Any = pl.Trainer.add_argparse_args(_lowercase )
a_ : str = SummarizationDistiller.add_model_specific_args(_lowercase , os.getcwd() )
a_ : Any = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
a_ : Dict = distill_main(_lowercase )
# Check metrics
a_ : Any = load_json(model.metrics_save_path )
a_ : int = metrics["""val"""][0]
a_ : Union[str, Any] = metrics["""val"""][-1]
assert len(metrics["""val"""] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.0_1
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , _lowercase )
# check lightning ckpt can be loaded and has a reasonable statedict
a_ : Dict = os.listdir(_lowercase )
a_ : List[Any] = [x for x in contents if x.endswith(""".ckpt""" )][0]
a_ : int = os.path.join(args.output_dir , _lowercase )
a_ : Union[str, Any] = torch.load(_lowercase , map_location="""cpu""" )
a_ : List[str] = """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
a_ : List[str] = {os.path.basename(_lowercase ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["""test"""] ) == 1
| 248
| 0
|
from __future__ import annotations
def UpperCamelCase__( UpperCamelCase__ : list )->float:
if not nums:
raise ValueError('''List is empty''' )
return sum(UpperCamelCase__ ) / len(UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 39
|
# 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 SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__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,*__lowerCamelCase,**__lowerCamelCase ):
requires_backends(self,['''vision'''] )
super().__init__(*__lowerCamelCase,**__lowerCamelCase )
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase ):
return self.pre_processor(text=[label],images=[image],padding=__lowerCamelCase,return_tensors='''pt''' )
def UpperCamelCase ( self,__lowerCamelCase ):
with torch.no_grad():
A__ = self.model(**__lowerCamelCase ).logits
return logits
def UpperCamelCase ( self,__lowerCamelCase ):
A__ = outputs.cpu().detach().numpy()
A__ = 0
A__ = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 39
| 1
|
'''simple docstring'''
import sys
from collections import defaultdict
class __A :
def __init__(self : Optional[Any] ):
UpperCAmelCase_ = []
def _lowercase (self : Union[str, Any] , __a : List[Any] ):
return self.node_position[vertex]
def _lowercase (self : Union[str, Any] , __a : Optional[Any] , __a : Dict ):
UpperCAmelCase_ = pos
def _lowercase (self : str , __a : str , __a : Dict , __a : Optional[Any] , __a : str ):
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
UpperCAmelCase_ = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
UpperCAmelCase_ = 2 * start + 1
else:
UpperCAmelCase_ = 2 * start + 2
if heap[smallest_child] < heap[start]:
UpperCAmelCase_ = heap[smallest_child], positions[smallest_child]
UpperCAmelCase_ = (
heap[start],
positions[start],
)
UpperCAmelCase_ = temp, tempa
UpperCAmelCase_ = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , a_ )
self.top_to_bottom(a_ , a_ , a_ , a_ )
def _lowercase (self : Any , __a : Dict , __a : Union[str, Any] , __a : Optional[Any] , __a : List[str] ):
UpperCAmelCase_ = position[index]
while index != 0:
UpperCAmelCase_ = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
UpperCAmelCase_ = heap[parent]
UpperCAmelCase_ = position[parent]
self.set_position(position[parent] , a_ )
else:
UpperCAmelCase_ = val
UpperCAmelCase_ = temp
self.set_position(a_ , a_ )
break
UpperCAmelCase_ = parent
else:
UpperCAmelCase_ = val
UpperCAmelCase_ = temp
self.set_position(a_ , 0 )
def _lowercase (self : List[str] , __a : Optional[int] , __a : int ):
UpperCAmelCase_ = len(a_ ) // 2 - 1
for i in range(a_ , -1 , -1 ):
self.top_to_bottom(a_ , a_ , len(a_ ) , a_ )
def _lowercase (self : List[str] , __a : Dict , __a : str ):
UpperCAmelCase_ = positions[0]
UpperCAmelCase_ = sys.maxsize
self.top_to_bottom(a_ , 0 , len(a_ ) , a_ )
return temp
def lowerCAmelCase_ ( snake_case_ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = Heap()
UpperCAmelCase_ = [0] * len(_snake_case )
UpperCAmelCase_ = [-1] * len(_snake_case ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
UpperCAmelCase_ = [] # Heap of Distance of vertices from their neighboring vertex
UpperCAmelCase_ = []
for vertex in range(len(_snake_case ) ):
distance_tv.append(sys.maxsize )
positions.append(_snake_case )
heap.node_position.append(_snake_case )
UpperCAmelCase_ = []
UpperCAmelCase_ = 1
UpperCAmelCase_ = sys.maxsize
for neighbor, distance in adjacency_list[0]:
UpperCAmelCase_ = 0
UpperCAmelCase_ = distance
heap.heapify(_snake_case , _snake_case )
for _ in range(1 , len(_snake_case ) ):
UpperCAmelCase_ = heap.delete_minimum(_snake_case , _snake_case )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
UpperCAmelCase_ = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_snake_case )]
):
UpperCAmelCase_ = distance
heap.bottom_to_top(
_snake_case , heap.get_position(_snake_case ) , _snake_case , _snake_case )
UpperCAmelCase_ = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
SCREAMING_SNAKE_CASE_: Tuple =int(input('Enter number of edges: ').strip())
SCREAMING_SNAKE_CASE_: List[Any] =defaultdict(list)
for _ in range(edges_number):
SCREAMING_SNAKE_CASE_: Optional[Any] =[int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 1
|
"""simple docstring"""
import math
def lowercase ( _snake_case : int ) ->bool:
"""simple docstring"""
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(_snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowercase ( _snake_case : float = 0.1 ) ->int:
"""simple docstring"""
__snake_case : Tuple = 3
__snake_case : Any = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(_snake_case )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 102
| 0
|
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase__ )
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ):
"""simple docstring"""
lowercase : str = field(default='text-classification' , metadata={'include_in_asdict_even_if_is_default': True} )
lowercase : ClassVar[Features] = Features({'text': Value('string' )} )
lowercase : ClassVar[Features] = Features({'labels': ClassLabel} )
lowercase : str = "text"
lowercase : str = "labels"
def __lowerCamelCase ( self , __UpperCamelCase ) -> List[str]:
'''simple docstring'''
if self.label_column not in features:
raise ValueError(f'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , __UpperCamelCase ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
__UpperCamelCase : int = copy.deepcopy(self )
__UpperCamelCase : List[Any] = self.label_schema.copy()
__UpperCamelCase : Union[str, Any] = features[self.label_column]
__UpperCamelCase : Optional[Any] = label_schema
return task_template
@property
def __lowerCamelCase ( self ) -> Dict[str, str]:
'''simple docstring'''
return {
self.text_column: "text",
self.label_column: "labels",
}
| 171
|
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Union[str, Any] = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureExtractor"],
"processing_mctct": ["MCTCTProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Tuple = [
"MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MCTCTForCTC",
"MCTCTModel",
"MCTCTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
lowercase : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 171
| 1
|
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> list:
if len(a_ ) <= 1:
return lst
lowerCamelCase__ : Dict = 1
while i < len(a_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
lowerCamelCase__ , lowerCamelCase__ : Dict = lst[i], lst[i - 1]
i -= 1
if i == 0:
lowerCamelCase__ : Tuple = 1
return lst
if __name__ == "__main__":
_UpperCAmelCase : List[Any] = input("""Enter numbers separated by a comma:\n""").strip()
_UpperCAmelCase : List[Any] = [int(item) for item in user_input.split(""",""")]
print(gnome_sort(unsorted))
| 50
|
SCREAMING_SNAKE_CASE :Any = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :Union[str, Any] = 100_0003
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = len(a_ )
__A = len(a_ )
if p_len > t_len:
return False
__A = 0
__A = 0
__A = 1
# Calculating the hash of pattern and substring of text
for i in range(a_ ):
__A = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
__A = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
__A = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
__A = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = "abc1abc12"
__A = "alskfjaldsabc1abc1abc12k23adsfabcabc"
__A = "alskfjaldsk23adsfabcabc"
assert rabin_karp(a_ , a_ ) and not rabin_karp(a_ , a_ )
# Test 2)
__A = "ABABX"
__A = "ABABZABABYABABX"
assert rabin_karp(a_ , a_ )
# Test 3)
__A = "AAAB"
__A = "ABAAAAAB"
assert rabin_karp(a_ , a_ )
# Test 4)
__A = "abcdabcy"
__A = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(a_ , a_ )
# Test 5)
__A = "Lü"
__A = "Lüsai"
assert rabin_karp(a_ , a_ )
__A = "Lue"
assert not rabin_karp(a_ , a_ )
print("Success." )
if __name__ == "__main__":
test_rabin_karp()
| 15
| 0
|
"""simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _lowerCAmelCase :
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
if dst_width < 0 or dst_height < 0:
raise ValueError("Destination width/height should be > 0" )
snake_case : List[Any] = img
snake_case : Dict = img.shape[1]
snake_case : Union[str, Any] = img.shape[0]
snake_case : Optional[Any] = dst_width
snake_case : List[Any] = dst_height
snake_case : int = self.src_w / self.dst_w
snake_case : Union[str, Any] = self.src_h / self.dst_h
snake_case : Dict = (
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 255
)
def lowerCamelCase ( self ) -> Dict:
'''simple docstring'''
for i in range(self.dst_h ):
for j in range(self.dst_w ):
snake_case : int = self.img[self.get_y(UpperCamelCase__ )][self.get_x(UpperCamelCase__ )]
def lowerCamelCase ( self , UpperCamelCase__ ) -> int:
'''simple docstring'''
return int(self.ratio_x * x )
def lowerCamelCase ( self , UpperCamelCase__ ) -> int:
'''simple docstring'''
return int(self.ratio_y * y )
if __name__ == "__main__":
__snake_case , __snake_case = 800, 600
__snake_case = imread("""image_data/lena.jpg""", 1)
__snake_case = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
F'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
)
waitKey(0)
destroyAllWindows()
| 352
|
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int ) -> int:
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 112
| 0
|
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
print(F'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumerate(__snake_case ):
print(F'''{i}\t\t{d}''' )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
'''simple docstring'''
for j in range(__snake_case ):
lowercase_ , lowercase_ , lowercase_ = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
return True
return False
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[float]:
'''simple docstring'''
lowercase_ = [float("""inf""" )] * vertex_count
lowercase_ = 0.0
for _ in range(vertex_count - 1 ):
for j in range(__snake_case ):
lowercase_ , lowercase_ , lowercase_ = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
lowercase_ = distance[u] + w
lowercase_ = check_negative_cycle(__snake_case , __snake_case , __snake_case )
if negative_cycle_exists:
raise Exception("""Negative cycle found""" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase : List[Any] = int(input("Enter number of vertices: ").strip())
UpperCAmelCase : Any = int(input("Enter number of edges: ").strip())
UpperCAmelCase : int = [{} for _ in range(E)]
for i in range(E):
print("Edge ", i + 1)
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
UpperCAmelCase : Optional[int] = {"src": src, "dst": dest, "weight": weight}
UpperCAmelCase : Tuple = int(input("\nEnter shortest path source:").strip())
UpperCAmelCase : Optional[int] = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 136
|
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """Speech2TextFeatureExtractor"""
lowerCAmelCase_ = """Speech2TextTokenizer"""
def __init__( self , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
super().__init__(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = self.feature_extractor
lowerCamelCase__ = False
def __call__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*__lowerCAmelCase , **__lowerCAmelCase )
if "raw_speech" in kwargs:
warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' )
lowerCamelCase__ = kwargs.pop('''raw_speech''' )
else:
lowerCamelCase__ = kwargs.pop('''audio''' , __lowerCAmelCase )
lowerCamelCase__ = kwargs.pop('''sampling_rate''' , __lowerCAmelCase )
lowerCamelCase__ = kwargs.pop('''text''' , __lowerCAmelCase )
if len(__lowerCAmelCase ) > 0:
lowerCamelCase__ = args[0]
lowerCamelCase__ = 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:
lowerCamelCase__ = self.feature_extractor(__lowerCAmelCase , *__lowerCAmelCase , sampling_rate=__lowerCAmelCase , **__lowerCAmelCase )
if text is not None:
lowerCamelCase__ = self.tokenizer(__lowerCAmelCase , **__lowerCAmelCase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowerCamelCase__ = encodings['''input_ids''']
return inputs
def __lowerCamelCase ( self , *__lowerCAmelCase , **__lowerCAmelCase ):
'''simple docstring'''
return self.tokenizer.batch_decode(*__lowerCAmelCase , **__lowerCAmelCase )
def __lowerCamelCase ( self , *__lowerCAmelCase , **__lowerCAmelCase ):
'''simple docstring'''
return self.tokenizer.decode(*__lowerCAmelCase , **__lowerCAmelCase )
@contextmanager
def __lowerCamelCase ( self ):
'''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.''' )
lowerCamelCase__ = True
lowerCamelCase__ = self.tokenizer
yield
lowerCamelCase__ = self.feature_extractor
lowerCamelCase__ = False
| 209
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase__ = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 352
|
'''simple docstring'''
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , _A : int ):
'''simple docstring'''
UpperCAmelCase__ : str = n
UpperCAmelCase__ : Union[str, Any] = [None] * self.n
UpperCAmelCase__ : Tuple = 0 # index of the first element
UpperCAmelCase__ : int = 0
UpperCAmelCase__ : int = 0
def __len__( self : Optional[Any] ):
'''simple docstring'''
return self.size
def lowercase_ ( self : Dict ):
'''simple docstring'''
return self.size == 0
def lowercase_ ( self : List[str] ):
'''simple docstring'''
return False if self.is_empty() else self.array[self.front]
def lowercase_ ( self : List[Any] , _A : int ):
'''simple docstring'''
if self.size >= self.n:
raise Exception('''QUEUE IS FULL''' )
UpperCAmelCase__ : str = data
UpperCAmelCase__ : Optional[Any] = (self.rear + 1) % self.n
self.size += 1
return self
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
if self.size == 0:
raise Exception('''UNDERFLOW''' )
UpperCAmelCase__ : Any = self.array[self.front]
UpperCAmelCase__ : List[Any] = None
UpperCAmelCase__ : Tuple = (self.front + 1) % self.n
self.size -= 1
return temp
| 299
| 0
|
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
def __init__( self , *UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , **UpperCAmelCase ):
"""simple docstring"""
super().__init__(*UpperCAmelCase , **UpperCAmelCase )
_UpperCAmelCase = eval_examples
_UpperCAmelCase = post_process_function
def UpperCamelCase ( self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase = "eval" ):
"""simple docstring"""
_UpperCAmelCase = self.eval_dataset if eval_dataset is None else eval_dataset
_UpperCAmelCase = self.get_eval_dataloader(UpperCAmelCase )
_UpperCAmelCase = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_UpperCAmelCase = self.compute_metrics
_UpperCAmelCase = None
_UpperCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_UpperCAmelCase = time.time()
try:
_UpperCAmelCase = eval_loop(
UpperCAmelCase , description='Evaluation' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCAmelCase , metric_key_prefix=UpperCAmelCase , )
finally:
_UpperCAmelCase = compute_metrics
_UpperCAmelCase = self.args.eval_batch_size * self.args.world_size
if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
UpperCAmelCase , UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
_UpperCAmelCase = self.post_process_function(UpperCAmelCase , UpperCAmelCase , output.predictions )
_UpperCAmelCase = self.compute_metrics(UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"""{metric_key_prefix}_""" ):
_UpperCAmelCase = metrics.pop(UpperCAmelCase )
metrics.update(output.metrics )
else:
_UpperCAmelCase = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(UpperCAmelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_UpperCAmelCase = self.callback_handler.on_evaluate(self.args , self.state , self.control , UpperCAmelCase )
return metrics
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase = "test" ):
"""simple docstring"""
_UpperCAmelCase = self.get_test_dataloader(UpperCAmelCase )
# Temporarily disable metric computation, we will do it in the loop here.
_UpperCAmelCase = self.compute_metrics
_UpperCAmelCase = None
_UpperCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_UpperCAmelCase = time.time()
try:
_UpperCAmelCase = eval_loop(
UpperCAmelCase , description='Prediction' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCAmelCase , metric_key_prefix=UpperCAmelCase , )
finally:
_UpperCAmelCase = compute_metrics
_UpperCAmelCase = self.args.eval_batch_size * self.args.world_size
if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
UpperCAmelCase , UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
_UpperCAmelCase = self.post_process_function(UpperCAmelCase , UpperCAmelCase , output.predictions , 'predict' )
_UpperCAmelCase = self.compute_metrics(UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"""{metric_key_prefix}_""" ):
_UpperCAmelCase = metrics.pop(UpperCAmelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=UpperCAmelCase )
| 39
|
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase=13 , UpperCAmelCase=32 , UpperCAmelCase=2 , UpperCAmelCase=3 , UpperCAmelCase=16 , UpperCAmelCase=[1, 2, 1] , UpperCAmelCase=[2, 2, 4] , UpperCAmelCase=2 , UpperCAmelCase=2.0 , UpperCAmelCase=True , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase=0.1 , UpperCAmelCase="gelu" , UpperCAmelCase=False , UpperCAmelCase=True , UpperCAmelCase=0.02 , UpperCAmelCase=1e-5 , UpperCAmelCase=True , UpperCAmelCase=None , UpperCAmelCase=True , UpperCAmelCase=10 , UpperCAmelCase=8 , UpperCAmelCase=["stage1", "stage2", "stage3"] , UpperCAmelCase=[1, 2, 3] , ):
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_absolute_embeddings
_UpperCAmelCase = patch_norm
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = is_training
_UpperCAmelCase = scope
_UpperCAmelCase = use_labels
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = encoder_stride
_UpperCAmelCase = out_features
_UpperCAmelCase = out_indices
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ):
"""simple docstring"""
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = MaskFormerSwinModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_UpperCAmelCase = model(UpperCAmelCase )
_UpperCAmelCase = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
_UpperCAmelCase = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = MaskFormerSwinBackbone(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_UpperCAmelCase = model(UpperCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(UpperCAmelCase ):
_UpperCAmelCase = ['stem']
_UpperCAmelCase = MaskFormerSwinBackbone(config=UpperCAmelCase )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCamelCase ( snake_case__ , snake_case__ , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
UpperCamelCase__ = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = MaskFormerSwinModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=UpperCAmelCase , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
'`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
' `nn.DataParallel`'
) )
def UpperCamelCase ( self ):
"""simple docstring"""
pass
def UpperCamelCase ( self ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCamelCase ( self ):
"""simple docstring"""
return
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*UpperCAmelCase )
@unittest.skip('Swin does not use inputs_embeds' )
def UpperCamelCase ( self ):
"""simple docstring"""
pass
@unittest.skip('Swin does not support feedforward chunking' )
def UpperCamelCase ( self ):
"""simple docstring"""
pass
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_UpperCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase , nn.Linear ) )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(UpperCAmelCase )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , UpperCAmelCase )
@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions' )
def UpperCamelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone' )
def UpperCamelCase ( self ):
"""simple docstring"""
pass
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase )
# Swin has a different seq_length
_UpperCAmelCase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_UpperCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
_UpperCAmelCase = True
self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = 3
_UpperCAmelCase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
_UpperCAmelCase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_UpperCAmelCase = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
_UpperCAmelCase = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
_UpperCAmelCase = True
self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , (padded_height, padded_width) )
@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints' )
def UpperCamelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def UpperCamelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def UpperCamelCase ( self ):
"""simple docstring"""
pass
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(UpperCAmelCase ):
_UpperCAmelCase = 0
return t
def check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase={} ):
with torch.no_grad():
_UpperCAmelCase = model(**UpperCAmelCase , return_dict=UpperCAmelCase , **UpperCAmelCase )
_UpperCAmelCase = model(**UpperCAmelCase , return_dict=UpperCAmelCase , **UpperCAmelCase ).to_tuple()
def recursive_check(UpperCAmelCase , UpperCAmelCase ):
if isinstance(UpperCAmelCase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase , UpperCAmelCase ):
recursive_check(UpperCAmelCase , UpperCAmelCase )
elif isinstance(UpperCAmelCase , UpperCAmelCase ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(UpperCAmelCase , UpperCAmelCase )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(UpperCAmelCase ) , set_nan_tensor_to_zero(UpperCAmelCase ) , atol=1e-5 ) , msg=(
'Tuple and dict output are not equal. Difference:'
F""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
F""" {torch.isnan(UpperCAmelCase ).any()} and `inf`: {torch.isinf(UpperCAmelCase )}. Dict has"""
F""" `nan`: {torch.isnan(UpperCAmelCase ).any()} and `inf`: {torch.isinf(UpperCAmelCase )}."""
) , )
recursive_check(UpperCAmelCase , UpperCAmelCase )
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
_UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
_UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
_UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
_UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
_UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , {'output_hidden_states': True} )
_UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
_UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , {'output_hidden_states': True} )
@require_torch
class __lowerCamelCase ( unittest.TestCase , snake_case__):
"""simple docstring"""
UpperCamelCase__ = (MaskFormerSwinBackbone,) if is_torch_available() else ()
UpperCamelCase__ = MaskFormerSwinConfig
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = MaskFormerSwinModelTester(self )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = inputs_dict['pixel_values'].shape[0]
for backbone_class in self.all_model_classes:
_UpperCAmelCase = backbone_class(UpperCAmelCase )
backbone.to(UpperCAmelCase )
backbone.eval()
_UpperCAmelCase = backbone(**UpperCAmelCase )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , UpperCAmelCase )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
_UpperCAmelCase = backbone(**UpperCAmelCase , output_hidden_states=UpperCAmelCase )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
_UpperCAmelCase = backbone(**UpperCAmelCase , output_attentions=UpperCAmelCase )
self.assertIsNotNone(outputs.attentions )
| 39
| 1
|
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class SCREAMING_SNAKE_CASE_ :
def __init__( self : List[Any] , _A : Optional[int] , _A : Tuple=13 , _A : Union[str, Any]=7 , _A : str=True , _A : Dict=True , _A : str=True , _A : Union[str, Any]=True , _A : str=99 , _A : List[Any]=64 , _A : List[str]=5 , _A : Any=4 , _A : List[Any]=37 , _A : Optional[Any]="gelu" , _A : Union[str, Any]=0.1 , _A : Tuple=0.1 , _A : Optional[Any]=512 , _A : Dict=16 , _A : Tuple=2 , _A : int=0.0_2 , _A : int=3 , _A : Tuple=4 , _A : Optional[int]=None , ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Optional[int] = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : Optional[int] = seq_length
snake_case_ : str = is_training
snake_case_ : Optional[int] = use_input_mask
snake_case_ : Any = use_token_type_ids
snake_case_ : Union[str, Any] = use_labels
snake_case_ : Union[str, Any] = vocab_size
snake_case_ : Union[str, Any] = hidden_size
snake_case_ : Tuple = num_hidden_layers
snake_case_ : Dict = num_attention_heads
snake_case_ : str = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : List[str] = attention_probs_dropout_prob
snake_case_ : Dict = max_position_embeddings
snake_case_ : Any = type_vocab_size
snake_case_ : Tuple = type_sequence_label_size
snake_case_ : str = initializer_range
snake_case_ : int = num_labels
snake_case_ : List[Any] = num_choices
snake_case_ : Dict = scope
snake_case_ : Optional[Any] = vocab_size - 1
def UpperCAmelCase_ ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : int = None
if self.use_input_mask:
snake_case_ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Dict = None
if self.use_labels:
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ : str = self.get_config()
return config, input_ids, input_mask, token_labels
def UpperCAmelCase_ ( self : int ) -> str:
"""simple docstring"""
return GPTNeoXConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_A , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , )
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ : str = self.prepare_config_and_inputs()
snake_case_ : Tuple = True
return config, input_ids, input_mask, token_labels
def UpperCAmelCase_ ( self : str , _A : Any , _A : List[str] , _A : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Any = GPTNeoXModel(config=_A )
model.to(_A )
model.eval()
snake_case_ : List[Any] = model(_A , attention_mask=_A )
snake_case_ : List[str] = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self : Union[str, Any] , _A : Dict , _A : List[Any] , _A : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : List[str] = True
snake_case_ : Optional[Any] = GPTNeoXModel(_A )
model.to(_A )
model.eval()
snake_case_ : Optional[int] = model(_A , attention_mask=_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self : str , _A : Optional[int] , _A : Any , _A : Optional[Any] , _A : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : Tuple = GPTNeoXForCausalLM(config=_A )
model.to(_A )
model.eval()
snake_case_ : int = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ ( self : str , _A : int , _A : List[Any] , _A : List[Any] , _A : Union[str, Any] ) -> Any:
"""simple docstring"""
snake_case_ : int = self.num_labels
snake_case_ : int = GPTNeoXForQuestionAnswering(_A )
model.to(_A )
model.eval()
snake_case_ : List[Any] = model(_A , attention_mask=_A )
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 UpperCAmelCase_ ( self : List[str] , _A : Optional[Any] , _A : Dict , _A : Dict , _A : Any ) -> int:
"""simple docstring"""
snake_case_ : str = self.num_labels
snake_case_ : Tuple = GPTNeoXForSequenceClassification(_A )
model.to(_A )
model.eval()
snake_case_ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : Any = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self : Dict , _A : List[str] , _A : Optional[Any] , _A : Union[str, Any] , _A : Any ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Tuple = self.num_labels
snake_case_ : Tuple = GPTNeoXForTokenClassification(_A )
model.to(_A )
model.eval()
snake_case_ : List[str] = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase_ ( self : List[Any] , _A : List[Any] , _A : Optional[Any] , _A : Union[str, Any] ) -> int:
"""simple docstring"""
snake_case_ : List[Any] = True
snake_case_ : Dict = GPTNeoXForCausalLM(config=_A )
model.to(_A )
model.eval()
# first forward pass
snake_case_ : List[str] = model(_A , attention_mask=_A , use_cache=_A )
snake_case_ : Optional[int] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
snake_case_ : str = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ : Union[str, Any] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
snake_case_ : str = torch.cat([input_ids, next_tokens] , dim=-1 )
snake_case_ : List[Any] = torch.cat([input_mask, next_mask] , dim=-1 )
snake_case_ : Any = model(_A , attention_mask=_A , output_hidden_states=_A )
snake_case_ : Union[str, Any] = output_from_no_past['hidden_states'][0]
snake_case_ : str = model(
_A , attention_mask=_A , past_key_values=_A , output_hidden_states=_A , )['hidden_states'][0]
# select random slice
snake_case_ : Dict = ids_tensor((1,) , output_from_past.shape[-1] ).item()
snake_case_ : Dict = output_from_no_past[:, -3:, random_slice_idx].detach()
snake_case_ : str = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_A , _A , atol=1E-3 ) )
def UpperCAmelCase_ ( self : str ) -> List[Any]:
"""simple docstring"""
snake_case_ : Dict = self.prepare_config_and_inputs()
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ : Union[str, Any] = config_and_inputs
snake_case_ : Tuple = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE_ ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
__magic_name__: Optional[int] = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
__magic_name__: str = (GPTNeoXForCausalLM,) if is_torch_available() else ()
__magic_name__: Optional[Any] = (
{
"feature-extraction": GPTNeoXModel,
"question-answering": GPTNeoXForQuestionAnswering,
"text-classification": GPTNeoXForSequenceClassification,
"text-generation": GPTNeoXForCausalLM,
"token-classification": GPTNeoXForTokenClassification,
"zero-shot": GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
__magic_name__: Any = False
__magic_name__: Union[str, Any] = False
__magic_name__: Dict = False
__magic_name__: Any = False
def UpperCAmelCase_ ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
snake_case_ : List[Any] = GPTNeoXModelTester(self )
snake_case_ : str = ConfigTester(self , config_class=_A , hidden_size=64 , num_attention_heads=8 )
def UpperCAmelCase_ ( self : Tuple ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(_A , _A , _A )
def UpperCAmelCase_ ( self : Any ) -> str:
"""simple docstring"""
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(_A , _A , _A )
def UpperCAmelCase_ ( self : Optional[Any] ) -> int:
"""simple docstring"""
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ : str = self.model_tester.prepare_config_and_inputs_for_decoder()
snake_case_ : Any = None
self.model_tester.create_and_check_model_as_decoder(_A , _A , _A )
def UpperCAmelCase_ ( self : Optional[int] ) -> str:
"""simple docstring"""
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(_A , _A , _A )
def UpperCAmelCase_ ( self : Tuple ) -> int:
"""simple docstring"""
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*_A )
def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_A )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> str:
"""simple docstring"""
snake_case_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_A )
def UpperCAmelCase_ ( self : str ) -> str:
"""simple docstring"""
snake_case_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_A )
@unittest.skip(reason='Feed forward chunking is not implemented' )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def UpperCAmelCase_ ( self : List[Any] , _A : List[Any] ) -> str:
"""simple docstring"""
snake_case_ ,snake_case_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Optional[Any] = ids_tensor([1, 10] , config.vocab_size )
snake_case_ : int = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
snake_case_ : Optional[Any] = GPTNeoXModel(_A )
original_model.to(_A )
original_model.eval()
snake_case_ : Optional[Any] = original_model(_A ).last_hidden_state
snake_case_ : Dict = original_model(_A ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
snake_case_ : Any = {'type': scaling_type, 'factor': 1_0.0}
snake_case_ : Tuple = GPTNeoXModel(_A )
scaled_model.to(_A )
scaled_model.eval()
snake_case_ : Optional[Any] = scaled_model(_A ).last_hidden_state
snake_case_ : List[str] = scaled_model(_A ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(_A , _A , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(_A , _A , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_A , _A , atol=1E-5 ) )
@require_torch
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
@slow
def UpperCAmelCase_ ( self : List[Any] ) -> Tuple:
"""simple docstring"""
snake_case_ : Dict = AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped' )
for checkpointing in [True, False]:
snake_case_ : List[Any] = GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped' )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(_A )
snake_case_ : List[str] = tokenizer('My favorite food is' , return_tensors='pt' ).to(_A )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
snake_case_ : Union[str, Any] = 'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'
snake_case_ : int = model.generate(**_A , do_sample=_A , max_new_tokens=20 )
snake_case_ : Dict = tokenizer.batch_decode(_A )[0]
self.assertEqual(_A , _A )
| 88
|
from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise ValueError('Undefined for non-natural numbers' )
snake_case_ : Dict = precision
snake_case_ : str = ceil(precision / 14 )
snake_case_ : str = 42_68_80 * Decimal(1_00_05 ).sqrt()
snake_case_ : Tuple = 1
snake_case_ : int = 13_59_14_09
snake_case_ : Tuple = Decimal(__a )
for k in range(1 , __a ):
snake_case_ : List[Any] = factorial(6 * k ) // (factorial(3 * k ) * factorial(__a ) ** 3)
linear_term += 5_45_14_01_34
exponential_term *= -26_25_37_41_26_40_76_80_00
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = 50
print(F'''The first {n} digits of pi is: {pi(n)}''')
| 88
| 1
|
"""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 lowerCamelCase ( lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = BioGptTokenizer
SCREAMING_SNAKE_CASE = False
def _a (self ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase__ : List[str] = [
"""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>""",
]
UpperCAmelCase__ : str = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) )
UpperCAmelCase__ : Union[str, Any] = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
UpperCAmelCase__ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase__ : str = 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 _a (self , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = """lower newer"""
UpperCAmelCase__ : int = """lower newer"""
return input_text, output_text
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = BioGptTokenizer(self.vocab_file , self.merges_file )
UpperCAmelCase__ : List[str] = """lower"""
UpperCAmelCase__ : Optional[Any] = ["""low""", """er</w>"""]
UpperCAmelCase__ : Union[str, Any] = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
UpperCAmelCase__ : Optional[int] = tokens + ["""<unk>"""]
UpperCAmelCase__ : List[str] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , _lowerCamelCase )
@slow
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
UpperCAmelCase__ : Optional[Any] = tokenizer.encode("""sequence builders""" , add_special_tokens=_lowerCamelCase )
UpperCAmelCase__ : Optional[Any] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=_lowerCamelCase )
UpperCAmelCase__ : Dict = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
UpperCAmelCase__ : str = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase , _lowerCamelCase )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 171
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_A = {
"""configuration_owlvit""": [
"""OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""OwlViTConfig""",
"""OwlViTOnnxConfig""",
"""OwlViTTextConfig""",
"""OwlViTVisionConfig""",
],
"""processing_owlvit""": ["""OwlViTProcessor"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["""OwlViTFeatureExtractor"""]
_A = ["""OwlViTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"""OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""OwlViTModel""",
"""OwlViTPreTrainedModel""",
"""OwlViTTextModel""",
"""OwlViTVisionModel""",
"""OwlViTForObjectDetection""",
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 171
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] = logging.get_logger(__name__)
A_ : Dict = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : List[str] ='''cvt'''
def __init__( self , _lowerCamelCase=3 , _lowerCamelCase=[7, 3, 3] , _lowerCamelCase=[4, 2, 2] , _lowerCamelCase=[2, 1, 1] , _lowerCamelCase=[6_4, 1_9_2, 3_8_4] , _lowerCamelCase=[1, 3, 6] , _lowerCamelCase=[1, 2, 1_0] , _lowerCamelCase=[4.0, 4.0, 4.0] , _lowerCamelCase=[0.0, 0.0, 0.0] , _lowerCamelCase=[0.0, 0.0, 0.0] , _lowerCamelCase=[0.0, 0.0, 0.1] , _lowerCamelCase=[True, True, True] , _lowerCamelCase=[False, False, True] , _lowerCamelCase=["dw_bn", "dw_bn", "dw_bn"] , _lowerCamelCase=[3, 3, 3] , _lowerCamelCase=[1, 1, 1] , _lowerCamelCase=[2, 2, 2] , _lowerCamelCase=[1, 1, 1] , _lowerCamelCase=[1, 1, 1] , _lowerCamelCase=0.0_2 , _lowerCamelCase=1e-12 , **_lowerCamelCase , ):
super().__init__(**_lowerCamelCase )
UpperCamelCase_: Tuple = num_channels
UpperCamelCase_: Optional[int] = patch_sizes
UpperCamelCase_: Union[str, Any] = patch_stride
UpperCamelCase_: List[Any] = patch_padding
UpperCamelCase_: List[Any] = embed_dim
UpperCamelCase_: str = num_heads
UpperCamelCase_: Tuple = depth
UpperCamelCase_: Dict = mlp_ratio
UpperCamelCase_: Union[str, Any] = attention_drop_rate
UpperCamelCase_: int = drop_rate
UpperCamelCase_: Optional[Any] = drop_path_rate
UpperCamelCase_: Optional[int] = qkv_bias
UpperCamelCase_: Tuple = cls_token
UpperCamelCase_: List[str] = qkv_projection_method
UpperCamelCase_: Tuple = kernel_qkv
UpperCamelCase_: Dict = padding_kv
UpperCamelCase_: Optional[int] = stride_kv
UpperCamelCase_: Optional[Any] = padding_q
UpperCamelCase_: Dict = stride_q
UpperCamelCase_: Union[str, Any] = initializer_range
UpperCamelCase_: List[Any] = layer_norm_eps
| 292
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _lowerCAmelCase( unittest.TestCase ):
"""simple docstring"""
def _a ( self ):
UpperCamelCase_: Any = torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
UpperCamelCase_: List[str] = get_activation('gelu' )
self.assertTrue(torch.allclose(gelu_python(_lowerCamelCase ) , torch_builtin(_lowerCamelCase ) ) )
self.assertFalse(torch.allclose(gelu_python(_lowerCamelCase ) , gelu_new(_lowerCamelCase ) ) )
def _a ( self ):
UpperCamelCase_: Optional[Any] = torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
UpperCamelCase_: Union[str, Any] = get_activation('gelu' )
UpperCamelCase_: int = get_activation('gelu_10' )
UpperCamelCase_: Union[str, Any] = torch_builtin(_lowerCamelCase )
UpperCamelCase_: List[str] = geluaa(_lowerCamelCase )
UpperCamelCase_: Dict = torch.where(y_gelu_aa < 1_0.0 , 1 , 0 )
self.assertTrue(torch.max(_lowerCamelCase ).item() == 1_0.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def _a ( self ):
get_activation('gelu' )
get_activation('gelu_10' )
get_activation('gelu_fast' )
get_activation('gelu_new' )
get_activation('gelu_python' )
get_activation('gelu_pytorch_tanh' )
get_activation('linear' )
get_activation('mish' )
get_activation('quick_gelu' )
get_activation('relu' )
get_activation('sigmoid' )
get_activation('silu' )
get_activation('swish' )
get_activation('tanh' )
with self.assertRaises(_lowerCamelCase ):
get_activation('bogus' )
with self.assertRaises(_lowerCamelCase ):
get_activation(_lowerCamelCase )
def _a ( self ):
UpperCamelCase_: str = get_activation('gelu' )
UpperCamelCase_: str = 1
UpperCamelCase_: int = get_activation('gelu' )
self.assertEqual(acta.a , 1 )
with self.assertRaises(_lowerCamelCase ):
UpperCamelCase_: Tuple = acta.a
| 292
| 1
|
"""simple docstring"""
from __future__ import annotations
def _snake_case ( UpperCamelCase : Dict , UpperCamelCase : Any , UpperCamelCase : Optional[int] , UpperCamelCase : Any ): # noqa: E741
while r - l > 1:
UpperCAmelCase : int = (l + r) // 2
if v[m] >= key:
UpperCAmelCase : Union[str, Any] = m
else:
UpperCAmelCase : Dict = m # noqa: E741
return r
def _snake_case ( UpperCamelCase : list[int] ):
if len(UpperCamelCase ) == 0:
return 0
UpperCAmelCase : Union[str, Any] = [0] * len(UpperCamelCase )
UpperCAmelCase : Union[str, Any] = 1
UpperCAmelCase : Optional[Any] = v[0]
for i in range(1 , len(UpperCamelCase ) ):
if v[i] < tail[0]:
UpperCAmelCase : List[str] = v[i]
elif v[i] > tail[length - 1]:
UpperCAmelCase : Dict = v[i]
length += 1
else:
UpperCAmelCase : Optional[Any] = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 109
|
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: list[int] , _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : str = int(_lowerCamelCase )
# Initialize Result
__SCREAMING_SNAKE_CASE : Tuple = []
# Traverse through all denomination
for denomination in reversed(_lowerCamelCase ):
# Find denominations
while int(_lowerCamelCase ) >= int(_lowerCamelCase ):
total_value -= int(_lowerCamelCase )
answer.append(_lowerCamelCase ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
UpperCamelCase__ : int = []
UpperCamelCase__ : List[Any] = '''0'''
if (
input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
== "y"
):
UpperCamelCase__ : Tuple = int(input('''Enter the number of denominations you want to add: ''').strip())
for i in range(0, n):
denominations.append(int(input(f"Denomination {i}: ").strip()))
UpperCamelCase__ : str = input('''Enter the change you want to make in Indian Currency: ''').strip()
else:
# All denominations of Indian Currency if user does not enter
UpperCamelCase__ : List[Any] = [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
UpperCamelCase__ : str = input('''Enter the change you want to make: ''').strip()
if int(value) == 0 or int(value) < 0:
print('''The total value cannot be zero or negative.''')
else:
print(f"Following is minimal change for {value}: ")
UpperCamelCase__ : int = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=''' ''')
| 112
| 0
|
def a__ ( UpperCAmelCase : int ) -> bool:
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : List[str] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCAmelCase )
if number < 0:
return False
UpperCAmelCase : List[str] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 99
|
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
AccelerateTestCase,
TempDirTestCase,
execute_subprocess_async,
require_cuda,
require_fsdp,
require_multi_gpu,
slow,
)
from accelerate.utils.constants import (
FSDP_AUTO_WRAP_POLICY,
FSDP_BACKWARD_PREFETCH,
FSDP_SHARDING_STRATEGY,
FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment
set_seed(4_2)
_lowerCamelCase : List[str] = "bert-base-cased"
_lowerCamelCase : str = "fp16"
_lowerCamelCase : Optional[int] = "bf16"
_lowerCamelCase : List[str] = [FPaa, BFaa]
@require_fsdp
@require_cuda
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Tuple ):
super().setUp()
UpperCAmelCase : Optional[Any] = dict(
ACCELERATE_USE_FSDP='''true''', MASTER_ADDR='''localhost''', MASTER_PORT='''10999''', RANK='''0''', LOCAL_RANK='''0''', WORLD_SIZE='''1''', )
def __magic_name__ ( self : int ):
from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
for i, strategy in enumerate(__A ):
UpperCAmelCase : List[Any] = self.dist_env.copy()
UpperCAmelCase : Union[str, Any] = F'''{i + 1}'''
UpperCAmelCase : Union[str, Any] = strategy
with mockenv_context(**__A ):
UpperCAmelCase : Union[str, Any] = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.sharding_strategy, ShardingStrategy(i + 1 ) )
def __magic_name__ ( self : Dict ):
from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch
for i, prefetch_policy in enumerate(__A ):
UpperCAmelCase : int = self.dist_env.copy()
UpperCAmelCase : Dict = prefetch_policy
with mockenv_context(**__A ):
UpperCAmelCase : Optional[int] = FullyShardedDataParallelPlugin()
if prefetch_policy == "NO_PREFETCH":
self.assertIsNone(fsdp_plugin.backward_prefetch )
else:
self.assertEqual(fsdp_plugin.backward_prefetch, BackwardPrefetch(i + 1 ) )
def __magic_name__ ( self : Any ):
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
for i, state_dict_type in enumerate(__A ):
UpperCAmelCase : Any = self.dist_env.copy()
UpperCAmelCase : int = state_dict_type
with mockenv_context(**__A ):
UpperCAmelCase : str = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.state_dict_type, StateDictType(i + 1 ) )
if state_dict_type == "FULL_STATE_DICT":
self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu )
self.assertTrue(fsdp_plugin.state_dict_config.ranka_only )
def __magic_name__ ( self : int ):
UpperCAmelCase : Optional[int] = AutoModel.from_pretrained(__A )
for policy in FSDP_AUTO_WRAP_POLICY:
UpperCAmelCase : Any = self.dist_env.copy()
UpperCAmelCase : List[Any] = policy
if policy == "TRANSFORMER_BASED_WRAP":
UpperCAmelCase : Tuple = '''BertLayer'''
elif policy == "SIZE_BASED_WRAP":
UpperCAmelCase : List[str] = '''2000'''
with mockenv_context(**__A ):
UpperCAmelCase : Optional[Any] = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(__A )
if policy == "NO_WRAP":
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
else:
self.assertIsNotNone(fsdp_plugin.auto_wrap_policy )
UpperCAmelCase : List[str] = self.dist_env.copy()
UpperCAmelCase : Tuple = '''TRANSFORMER_BASED_WRAP'''
UpperCAmelCase : Optional[Any] = '''T5Layer'''
with mockenv_context(**__A ):
UpperCAmelCase : int = FullyShardedDataParallelPlugin()
with self.assertRaises(__A ) as cm:
fsdp_plugin.set_auto_wrap_policy(__A )
self.assertTrue('''Could not find the transformer layer class to wrap in the model.''' in str(cm.exception ) )
UpperCAmelCase : List[Any] = self.dist_env.copy()
UpperCAmelCase : str = '''SIZE_BASED_WRAP'''
UpperCAmelCase : str = '''0'''
with mockenv_context(**__A ):
UpperCAmelCase : Optional[int] = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(__A )
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
def __magic_name__ ( self : int ):
from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
for mp_dtype in dtypes:
UpperCAmelCase : List[Any] = self.dist_env.copy()
UpperCAmelCase : int = mp_dtype
with mockenv_context(**__A ):
UpperCAmelCase : int = Accelerator()
if mp_dtype == "fp16":
UpperCAmelCase : Any = torch.floataa
elif mp_dtype == "bf16":
UpperCAmelCase : Any = torch.bfloataa
UpperCAmelCase : Optional[Any] = MixedPrecision(param_dtype=__A, reduce_dtype=__A, buffer_dtype=__A )
self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy, __A )
if mp_dtype == FPaa:
self.assertTrue(isinstance(accelerator.scaler, __A ) )
elif mp_dtype == BFaa:
self.assertIsNone(accelerator.scaler )
AcceleratorState._reset_state(__A )
def __magic_name__ ( self : Optional[int] ):
from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload
for flag in [True, False]:
UpperCAmelCase : Any = self.dist_env.copy()
UpperCAmelCase : int = str(__A ).lower()
with mockenv_context(**__A ):
UpperCAmelCase : Union[str, Any] = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.cpu_offload, CPUOffload(offload_params=__A ) )
@require_fsdp
@require_multi_gpu
@slow
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : List[Any] ):
super().setUp()
UpperCAmelCase : int = 0.8_2
UpperCAmelCase : List[str] = [
'''fsdp_shard_grad_op_transformer_based_wrap''',
'''fsdp_full_shard_transformer_based_wrap''',
]
UpperCAmelCase : int = {
'''multi_gpu_fp16''': 3_2_0_0,
'''fsdp_shard_grad_op_transformer_based_wrap_fp16''': 2_0_0_0,
'''fsdp_full_shard_transformer_based_wrap_fp16''': 1_9_0_0,
# Disabling below test as it overwhelms the RAM memory usage
# on CI self-hosted runner leading to tests getting killed.
# "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500, # fp16 was leading to indefinite hang
}
UpperCAmelCase : List[str] = 1_6_0
UpperCAmelCase : Optional[int] = 1_6_0
UpperCAmelCase : Union[str, Any] = inspect.getfile(accelerate.test_utils )
UpperCAmelCase : Dict = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''external_deps'''] )
def __magic_name__ ( self : str ):
UpperCAmelCase : str = os.path.join(self.test_scripts_folder, '''test_performance.py''' )
UpperCAmelCase : Any = ['''accelerate''', '''launch''', '''--num_processes=2''', '''--num_machines=1''', '''--machine_rank=0''', '''--use_fsdp''']
for config in self.performance_configs:
UpperCAmelCase : Union[str, Any] = cmd.copy()
for i, strategy in enumerate(__A ):
if strategy.lower() in config:
cmd_config.append(F'''--fsdp_sharding_strategy={i+1}''' )
break
if "fp32" in config:
cmd_config.append('''--mixed_precision=no''' )
else:
cmd_config.append('''--mixed_precision=fp16''' )
if "cpu_offload" in config:
cmd_config.append('''--fsdp_offload_params=True''' )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in config:
cmd_config.append(F'''--fsdp_auto_wrap_policy={policy}''' )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer''' )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append('''--fsdp_min_num_params=2000''' )
cmd_config.extend(
[
self.test_file_path,
F'''--output_dir={self.tmpdir}''',
F'''--performance_lower_bound={self.performance_lower_bound}''',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__A, env=os.environ.copy() )
def __magic_name__ ( self : Tuple ):
UpperCAmelCase : Dict = os.path.join(self.test_scripts_folder, '''test_checkpointing.py''' )
UpperCAmelCase : Optional[int] = [
'''accelerate''',
'''launch''',
'''--num_processes=2''',
'''--num_machines=1''',
'''--machine_rank=0''',
'''--use_fsdp''',
'''--mixed_precision=fp16''',
'''--fsdp_transformer_layer_cls_to_wrap=BertLayer''',
]
for i, strategy in enumerate(__A ):
UpperCAmelCase : Optional[Any] = cmd.copy()
cmd_config.append(F'''--fsdp_sharding_strategy={i+1}''' )
if strategy != "FULL_SHARD":
continue
UpperCAmelCase : Any = len(__A )
for state_dict_type in FSDP_STATE_DICT_TYPE:
UpperCAmelCase : List[Any] = cmd_config[:state_dict_config_index]
cmd_config.append(F'''--fsdp_state_dict_type={state_dict_type}''' )
cmd_config.extend(
[
self.test_file_path,
F'''--output_dir={self.tmpdir}''',
'''--partial_train_epoch=1''',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__A, env=os.environ.copy() )
UpperCAmelCase : Tuple = cmd_config[:-1]
UpperCAmelCase : List[str] = os.path.join(self.tmpdir, '''epoch_0''' )
cmd_config.extend(
[
F'''--resume_from_checkpoint={resume_from_checkpoint}''',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__A, env=os.environ.copy() )
def __magic_name__ ( self : Any ):
UpperCAmelCase : Optional[Any] = os.path.join(self.test_scripts_folder, '''test_peak_memory_usage.py''' )
UpperCAmelCase : str = [
'''accelerate''',
'''launch''',
'''--num_processes=2''',
'''--num_machines=1''',
'''--machine_rank=0''',
]
for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
UpperCAmelCase : List[Any] = cmd.copy()
if "fp16" in spec:
cmd_config.extend(['''--mixed_precision=fp16'''] )
else:
cmd_config.extend(['''--mixed_precision=no'''] )
if "multi_gpu" in spec:
continue
else:
cmd_config.extend(['''--use_fsdp'''] )
for i, strategy in enumerate(__A ):
if strategy.lower() in spec:
cmd_config.append(F'''--fsdp_sharding_strategy={i+1}''' )
break
if "cpu_offload" in spec:
cmd_config.append('''--fsdp_offload_params=True''' )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in spec:
cmd_config.append(F'''--fsdp_auto_wrap_policy={policy}''' )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer''' )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append('''--fsdp_min_num_params=2000''' )
cmd_config.extend(
[
self.test_file_path,
F'''--output_dir={self.tmpdir}''',
F'''--peak_memory_upper_bound={peak_mem_upper_bound}''',
F'''--n_train={self.n_train}''',
F'''--n_val={self.n_val}''',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__A, env=os.environ.copy() )
| 99
| 1
|
from collections.abc import Iterable
from typing import Any
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , _A = None ) -> Dict:
SCREAMING_SNAKE_CASE_ = value
SCREAMING_SNAKE_CASE_ = None # Added in order to delete a node easier
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = None
def __repr__( self ) -> str:
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({F'''{self.value}''': (self.left, self.right)} , indent=1 )
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , _A = None ) -> Tuple:
SCREAMING_SNAKE_CASE_ = root
def __str__( self ) -> str:
return str(self.root )
def _UpperCamelCase ( self , _A , _A ) -> None:
if new_children is not None: # reset its kids
SCREAMING_SNAKE_CASE_ = node.parent
if node.parent is not None: # reset its parent
if self.is_right(_A ): # If it is the right children
SCREAMING_SNAKE_CASE_ = new_children
else:
SCREAMING_SNAKE_CASE_ = new_children
else:
SCREAMING_SNAKE_CASE_ = new_children
def _UpperCamelCase ( self , _A ) -> bool:
if node.parent and node.parent.right:
return node == node.parent.right
return False
def _UpperCamelCase ( self ) -> bool:
return self.root is None
def _UpperCamelCase ( self , _A ) -> None:
SCREAMING_SNAKE_CASE_ = Node(_A ) # create a new Node
if self.empty(): # if Tree is empty
SCREAMING_SNAKE_CASE_ = new_node # set its root
else: # Tree is not empty
SCREAMING_SNAKE_CASE_ = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
SCREAMING_SNAKE_CASE_ = new_node # We insert the new node in a leaf
break
else:
SCREAMING_SNAKE_CASE_ = parent_node.left
else:
if parent_node.right is None:
SCREAMING_SNAKE_CASE_ = new_node
break
else:
SCREAMING_SNAKE_CASE_ = parent_node.right
SCREAMING_SNAKE_CASE_ = parent_node
def _UpperCamelCase ( self , *_A ) -> None:
for value in values:
self.__insert(_A )
def _UpperCamelCase ( self , _A ) -> Node | None:
if self.empty():
raise IndexError('''Warning: Tree is empty! please use another.''' )
else:
SCREAMING_SNAKE_CASE_ = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
SCREAMING_SNAKE_CASE_ = node.left if value < node.value else node.right
return node
def _UpperCamelCase ( self , _A = None ) -> Node | None:
if node is None:
if self.root is None:
return None
SCREAMING_SNAKE_CASE_ = self.root
if not self.empty():
while node.right is not None:
SCREAMING_SNAKE_CASE_ = node.right
return node
def _UpperCamelCase ( self , _A = None ) -> Node | None:
if node is None:
SCREAMING_SNAKE_CASE_ = self.root
if self.root is None:
return None
if not self.empty():
SCREAMING_SNAKE_CASE_ = self.root
while node.left is not None:
SCREAMING_SNAKE_CASE_ = node.left
return node
def _UpperCamelCase ( self , _A ) -> None:
SCREAMING_SNAKE_CASE_ = self.search(_A ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(_A , _A )
elif node.left is None: # Has only right children
self.__reassign_nodes(_A , node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(_A , node.left )
else:
SCREAMING_SNAKE_CASE_ = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
SCREAMING_SNAKE_CASE_ = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def _UpperCamelCase ( self , _A ) -> Iterable:
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def _UpperCamelCase ( self , _A=None ) -> Any:
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def _UpperCamelCase ( self , _A , _A ) -> None:
if node:
self.inorder(_A , node.left )
arr.append(node.value )
self.inorder(_A , node.right )
def _UpperCamelCase ( self , _A , _A ) -> int:
SCREAMING_SNAKE_CASE_ = []
self.inorder(_A , _A ) # append all values to list using inorder traversal
return arr[k - 1]
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = []
if curr_node is not None:
SCREAMING_SNAKE_CASE_ = postorder(curr_node.left ) + postorder(curr_node.right ) + [curr_node]
return node_list
def A__ ( ):
SCREAMING_SNAKE_CASE_ = (8, 3, 6, 1, 10, 14, 13, 4, 7)
SCREAMING_SNAKE_CASE_ = BinarySearchTree()
for i in testlist:
t.insert(__lowerCamelCase )
# Prints all the elements of the list in order traversal
print(__lowerCamelCase )
if t.search(6 ) is not None:
print('''The value 6 exists''' )
else:
print('''The value 6 doesn\'t exist''' )
if t.search(-1 ) is not None:
print('''The value -1 exists''' )
else:
print('''The value -1 doesn\'t exist''' )
if not t.empty():
print('''Max Value: ''', t.get_max().value ) # type: ignore
print('''Min Value: ''', t.get_min().value ) # type: ignore
for i in testlist:
t.remove(__lowerCamelCase )
print(__lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 299
|
def A__ ( __lowerCamelCase ):
if not isinstance(__lowerCamelCase, __lowerCamelCase ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1, input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 299
| 1
|
'''simple docstring'''
import doctest
from collections import deque
import numpy as np
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ) -> None:
lowerCAmelCase__ : str = [2, 1, 2, -1]
lowerCAmelCase__ : List[str] = [1, 2, 3, 4]
def UpperCAmelCase_ ( self ) -> list[float]:
lowerCAmelCase__ : List[str] = len(self.first_signal )
lowerCAmelCase__ : Union[str, Any] = len(self.second_signal )
lowerCAmelCase__ : Optional[int] = max(__UpperCAmelCase ,__UpperCAmelCase )
# create a zero matrix of max_length x max_length
lowerCAmelCase__ : str = [[0] * max_length for i in range(__UpperCAmelCase )]
# fills the smaller signal with zeros to make both signals of same length
if length_first_signal < length_second_signal:
self.first_signal += [0] * (max_length - length_first_signal)
elif length_first_signal > length_second_signal:
self.second_signal += [0] * (max_length - length_second_signal)
for i in range(__UpperCAmelCase ):
lowerCAmelCase__ : Any = deque(self.second_signal )
rotated_signal.rotate(__UpperCAmelCase )
for j, item in enumerate(__UpperCAmelCase ):
matrix[i][j] += item
# multiply the matrix with the first signal
lowerCAmelCase__ : List[str] = np.matmul(np.transpose(__UpperCAmelCase ) ,np.transpose(self.first_signal ) )
# rounding-off to two decimal places
return [round(__UpperCAmelCase ,2 ) for i in final_signal]
if __name__ == "__main__":
doctest.testmod()
| 184
|
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def UpperCAmelCase_ ( self ) -> Dict:
lowerCAmelCase__ : str = tempfile.mkdtemp()
lowerCAmelCase__ : List[Any] = 8
# DPR tok
lowerCAmelCase__ : int = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowerCAmelCase__ : List[Any] = os.path.join(self.tmpdirname ,"""dpr_tokenizer""" )
os.makedirs(__UpperCAmelCase ,exist_ok=__UpperCAmelCase )
lowerCAmelCase__ : Dict = os.path.join(__UpperCAmelCase ,DPR_VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
# BART tok
lowerCAmelCase__ : str = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
lowerCAmelCase__ : List[Any] = dict(zip(__UpperCAmelCase ,range(len(__UpperCAmelCase ) ) ) )
lowerCAmelCase__ : Optional[Any] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
lowerCAmelCase__ : Any = {"""unk_token""": """<unk>"""}
lowerCAmelCase__ : str = os.path.join(self.tmpdirname ,"""bart_tokenizer""" )
os.makedirs(__UpperCAmelCase ,exist_ok=__UpperCAmelCase )
lowerCAmelCase__ : Any = os.path.join(__UpperCAmelCase ,BART_VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase__ : Dict = os.path.join(__UpperCAmelCase ,BART_VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(__UpperCAmelCase ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(__UpperCAmelCase ) )
def UpperCAmelCase_ ( self ) -> DPRQuestionEncoderTokenizer:
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,"""dpr_tokenizer""" ) )
def UpperCAmelCase_ ( self ) -> BartTokenizer:
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname ,"""bart_tokenizer""" ) )
def UpperCAmelCase_ ( self ) -> Any:
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def UpperCAmelCase_ ( self ) -> int:
lowerCAmelCase__ : Any = os.path.join(self.tmpdirname ,"""rag_tokenizer""" )
lowerCAmelCase__ : Any = RagConfig(question_encoder=DPRConfig().to_dict() ,generator=BartConfig().to_dict() )
lowerCAmelCase__ : str = RagTokenizer(question_encoder=self.get_dpr_tokenizer() ,generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(__UpperCAmelCase )
rag_tokenizer.save_pretrained(__UpperCAmelCase )
lowerCAmelCase__ : Any = RagTokenizer.from_pretrained(__UpperCAmelCase ,config=__UpperCAmelCase )
self.assertIsInstance(new_rag_tokenizer.question_encoder ,__UpperCAmelCase )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() ,rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator ,__UpperCAmelCase )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() ,rag_tokenizer.generator.get_vocab() )
@slow
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
lowerCAmelCase__ : List[str] = RagTokenizer.from_pretrained("""facebook/rag-token-nq""" )
lowerCAmelCase__ : Optional[Any] = [
"""who got the first nobel prize in physics""",
"""when is the next deadpool movie being released""",
"""which mode is used for short wave broadcast service""",
"""who is the owner of reading football club""",
"""when is the next scandal episode coming out""",
"""when is the last time the philadelphia won the superbowl""",
"""what is the most current adobe flash player version""",
"""how many episodes are there in dragon ball z""",
"""what is the first step in the evolution of the eye""",
"""where is gall bladder situated in human body""",
"""what is the main mineral in lithium batteries""",
"""who is the president of usa right now""",
"""where do the greasers live in the outsiders""",
"""panda is a national animal of which country""",
"""what is the name of manchester united stadium""",
]
lowerCAmelCase__ : Dict = tokenizer(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@slow
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
lowerCAmelCase__ : str = RagTokenizer.from_pretrained("""facebook/rag-sequence-nq""" )
lowerCAmelCase__ : str = [
"""who got the first nobel prize in physics""",
"""when is the next deadpool movie being released""",
"""which mode is used for short wave broadcast service""",
"""who is the owner of reading football club""",
"""when is the next scandal episode coming out""",
"""when is the last time the philadelphia won the superbowl""",
"""what is the most current adobe flash player version""",
"""how many episodes are there in dragon ball z""",
"""what is the first step in the evolution of the eye""",
"""where is gall bladder situated in human body""",
"""what is the main mineral in lithium batteries""",
"""who is the president of usa right now""",
"""where do the greasers live in the outsiders""",
"""panda is a national animal of which country""",
"""what is the name of manchester united stadium""",
]
lowerCAmelCase__ : Tuple = tokenizer(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
| 184
| 1
|
from __future__ import annotations
from typing import TypedDict
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
a__ = 42
a__ = 42
def a__ ( A_ ):
'''simple docstring'''
if not isinstance(A_, A_ ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(A_ ) )]
def a__ ( A_ ):
'''simple docstring'''
if not isinstance(A_, A_ ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
__magic_name__ = all_rotations(A_ )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
__magic_name__ = {
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(A_ ),
}
return response
def a__ ( A_, A_ ):
'''simple docstring'''
if not isinstance(A_, A_ ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
__magic_name__ = int(A_ )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(A_ ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
__magic_name__ = [""""""] * len(A_ )
for _ in range(len(A_ ) ):
for i in range(len(A_ ) ):
__magic_name__ = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
__lowerCAmelCase : Tuple = 'Provide a string that I will generate its BWT transform: '
__lowerCAmelCase : str = input(entry_msg).strip()
__lowerCAmelCase : Dict = bwt_transform(s)
print(
F'''Burrows Wheeler transform for string \'{s}\' results '''
F'''in \'{result["bwt_string"]}\''''
)
__lowerCAmelCase : Optional[Any] = reverse_bwt(result['bwt_string'], result['idx_original_string'])
print(
F'''Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '''
F'''we get original string \'{original_string}\''''
)
| 88
|
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
__lowerCAmelCase : Any = [
{'dataset': 'wikipedia', 'config_name': '20220301.de'},
{'dataset': 'wikipedia', 'config_name': '20220301.en'},
{'dataset': 'wikipedia', 'config_name': '20220301.fr'},
{'dataset': 'wikipedia', 'config_name': '20220301.frr'},
{'dataset': 'wikipedia', 'config_name': '20220301.it'},
{'dataset': 'wikipedia', 'config_name': '20220301.simple'},
{'dataset': 'snli', 'config_name': 'plain_text'},
{'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
{'dataset': 'wiki40b', 'config_name': 'en'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
{'dataset': 'natural_questions', 'config_name': 'default'},
]
def a__ ( A_=True ):
'''simple docstring'''
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_A ) )
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
a__ = None
a__ = None
def _lowercase ( self : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
with TemporaryDirectory() as tmp_dir:
__magic_name__ = dataset_module_factory(UpperCamelCase__ , cache_dir=UpperCamelCase__ )
__magic_name__ = import_main_class(dataset_module.module_path , dataset=UpperCamelCase__ )
__magic_name__ = builder_cls(
cache_dir=UpperCamelCase__ , config_name=UpperCamelCase__ , hash=dataset_module.hash , )
__magic_name__ = """/""".join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=UpperCamelCase__ ).replace(os.sep , """/""" ),
config.DATASET_INFO_FILENAME,
] )
__magic_name__ = cached_path(UpperCamelCase__ , cache_dir=UpperCamelCase__ )
self.assertTrue(os.path.exists(UpperCamelCase__ ) )
@pytest.mark.integration
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = tmp_path_factory.mktemp("""test_hf_gcp""" ) / """test_wikipedia_simple"""
__magic_name__ = dataset_module_factory("""wikipedia""", cache_dir=A_ )
__magic_name__ = import_main_class(dataset_module.module_path )
__magic_name__ = builder_cls(
cache_dir=A_, config_name="""20220301.frr""", hash=dataset_module.hash, )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
__magic_name__ = None
builder_instance.download_and_prepare()
__magic_name__ = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = dataset_module_factory("""wikipedia""", cache_dir=A_ )
__magic_name__ = import_main_class(dataset_module.module_path, dataset=A_ )
__magic_name__ = builder_cls(
cache_dir=A_, config_name="""20220301.frr""", hash=dataset_module.hash, )
__magic_name__ = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(A_, A_ )
assert "train" in ds
assert isinstance(ds["""train"""], A_ )
assert next(iter(ds["""train"""] ) )
| 88
| 1
|
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
_enforce_args(UpperCamelCase , UpperCamelCase )
if n == 0:
return 0
lowerCAmelCase__ : List[str] = float("""-inf""" )
for i in range(1 , n + 1 ):
lowerCAmelCase__ : Optional[int] = max(
UpperCamelCase , prices[i - 1] + naive_cut_rod_recursive(n - i , UpperCamelCase ) )
return max_revue
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
_enforce_args(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : int = [float("""-inf""" ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(UpperCamelCase , UpperCamelCase , UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
lowerCAmelCase__ : Optional[int] = float("""-inf""" )
for i in range(1 , n + 1 ):
lowerCAmelCase__ : Dict = max(
UpperCamelCase , prices[i - 1] + _top_down_cut_rod_recursive(n - i , UpperCamelCase , UpperCamelCase ) , )
lowerCAmelCase__ : Union[str, Any] = max_revenue
return max_rev[n]
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
_enforce_args(UpperCamelCase , UpperCamelCase )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
lowerCAmelCase__ : int = [float("""-inf""" ) for _ in range(n + 1 )]
lowerCAmelCase__ : int = 0
for i in range(1 , n + 1 ):
lowerCAmelCase__ : Optional[int] = max_rev[i]
for j in range(1 , i + 1 ):
lowerCAmelCase__ : Optional[int] = max(UpperCamelCase , prices[j - 1] + max_rev[i - j] )
lowerCAmelCase__ : Optional[Any] = max_revenue_i
return max_rev[n]
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
if n < 0:
lowerCAmelCase__ : List[Any] = f"""n must be greater than or equal to 0. Got n = {n}"""
raise ValueError(UpperCamelCase )
if n > len(UpperCamelCase ):
lowerCAmelCase__ : List[Any] = (
"""Each integral piece of rod must have a corresponding price. """
f"""Got n = {n} but length of prices = {len(UpperCamelCase )}"""
)
raise ValueError(UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = [6, 10, 12, 15, 20, 23]
lowerCAmelCase__ : Tuple = len(UpperCamelCase )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
lowerCAmelCase__ : int = 36
lowerCAmelCase__ : Tuple = top_down_cut_rod(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Tuple = bottom_up_cut_rod(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = naive_cut_rod_recursive(UpperCamelCase , UpperCamelCase )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 184
|
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def UpperCAmelCase_ ( self ) -> Dict:
lowerCAmelCase__ : str = tempfile.mkdtemp()
lowerCAmelCase__ : List[Any] = 8
# DPR tok
lowerCAmelCase__ : int = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowerCAmelCase__ : List[Any] = os.path.join(self.tmpdirname ,"""dpr_tokenizer""" )
os.makedirs(__UpperCAmelCase ,exist_ok=__UpperCAmelCase )
lowerCAmelCase__ : Dict = os.path.join(__UpperCAmelCase ,DPR_VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
# BART tok
lowerCAmelCase__ : str = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
lowerCAmelCase__ : List[Any] = dict(zip(__UpperCAmelCase ,range(len(__UpperCAmelCase ) ) ) )
lowerCAmelCase__ : Optional[Any] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
lowerCAmelCase__ : Any = {"""unk_token""": """<unk>"""}
lowerCAmelCase__ : str = os.path.join(self.tmpdirname ,"""bart_tokenizer""" )
os.makedirs(__UpperCAmelCase ,exist_ok=__UpperCAmelCase )
lowerCAmelCase__ : Any = os.path.join(__UpperCAmelCase ,BART_VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase__ : Dict = os.path.join(__UpperCAmelCase ,BART_VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(__UpperCAmelCase ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(__UpperCAmelCase ) )
def UpperCAmelCase_ ( self ) -> DPRQuestionEncoderTokenizer:
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,"""dpr_tokenizer""" ) )
def UpperCAmelCase_ ( self ) -> BartTokenizer:
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname ,"""bart_tokenizer""" ) )
def UpperCAmelCase_ ( self ) -> Any:
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def UpperCAmelCase_ ( self ) -> int:
lowerCAmelCase__ : Any = os.path.join(self.tmpdirname ,"""rag_tokenizer""" )
lowerCAmelCase__ : Any = RagConfig(question_encoder=DPRConfig().to_dict() ,generator=BartConfig().to_dict() )
lowerCAmelCase__ : str = RagTokenizer(question_encoder=self.get_dpr_tokenizer() ,generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(__UpperCAmelCase )
rag_tokenizer.save_pretrained(__UpperCAmelCase )
lowerCAmelCase__ : Any = RagTokenizer.from_pretrained(__UpperCAmelCase ,config=__UpperCAmelCase )
self.assertIsInstance(new_rag_tokenizer.question_encoder ,__UpperCAmelCase )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() ,rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator ,__UpperCAmelCase )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() ,rag_tokenizer.generator.get_vocab() )
@slow
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
lowerCAmelCase__ : List[str] = RagTokenizer.from_pretrained("""facebook/rag-token-nq""" )
lowerCAmelCase__ : Optional[Any] = [
"""who got the first nobel prize in physics""",
"""when is the next deadpool movie being released""",
"""which mode is used for short wave broadcast service""",
"""who is the owner of reading football club""",
"""when is the next scandal episode coming out""",
"""when is the last time the philadelphia won the superbowl""",
"""what is the most current adobe flash player version""",
"""how many episodes are there in dragon ball z""",
"""what is the first step in the evolution of the eye""",
"""where is gall bladder situated in human body""",
"""what is the main mineral in lithium batteries""",
"""who is the president of usa right now""",
"""where do the greasers live in the outsiders""",
"""panda is a national animal of which country""",
"""what is the name of manchester united stadium""",
]
lowerCAmelCase__ : Dict = tokenizer(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@slow
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
lowerCAmelCase__ : str = RagTokenizer.from_pretrained("""facebook/rag-sequence-nq""" )
lowerCAmelCase__ : str = [
"""who got the first nobel prize in physics""",
"""when is the next deadpool movie being released""",
"""which mode is used for short wave broadcast service""",
"""who is the owner of reading football club""",
"""when is the next scandal episode coming out""",
"""when is the last time the philadelphia won the superbowl""",
"""what is the most current adobe flash player version""",
"""how many episodes are there in dragon ball z""",
"""what is the first step in the evolution of the eye""",
"""where is gall bladder situated in human body""",
"""what is the main mineral in lithium batteries""",
"""who is the president of usa right now""",
"""where do the greasers live in the outsiders""",
"""panda is a national animal of which country""",
"""what is the name of manchester united stadium""",
]
lowerCAmelCase__ : Tuple = tokenizer(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
| 184
| 1
|
"""simple docstring"""
from __future__ import annotations
def A__ ( UpperCamelCase ): # This function is recursive
A = len(UpperCamelCase )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
A = array[0]
A = False
A = 1
A = []
while not is_found and i < array_length:
if array[i] < pivot:
A = True
A = [element for element in array[i:] if element >= array[i]]
A = longest_subsequence(UpperCamelCase )
if len(UpperCamelCase ) > len(UpperCamelCase ):
A = temp_array
else:
i += 1
A = [element for element in array[1:] if element >= pivot]
A = [pivot, *longest_subsequence(UpperCamelCase )]
if len(UpperCamelCase ) > len(UpperCamelCase ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 292
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Optional[int] = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class _UpperCAmelCase ( lowercase_ ):
UpperCamelCase = '''vivit'''
def __init__( self :Optional[Any] , __UpperCamelCase :Dict=2_24 , __UpperCamelCase :int=32 , __UpperCamelCase :Union[str, Any]=[2, 16, 16] , __UpperCamelCase :Optional[Any]=3 , __UpperCamelCase :Optional[Any]=7_68 , __UpperCamelCase :Any=12 , __UpperCamelCase :List[str]=12 , __UpperCamelCase :List[str]=30_72 , __UpperCamelCase :Any="gelu_fast" , __UpperCamelCase :List[Any]=0.0 , __UpperCamelCase :str=0.0 , __UpperCamelCase :Dict=0.02 , __UpperCamelCase :Optional[Any]=1e-06 , __UpperCamelCase :Dict=True , **__UpperCamelCase :Tuple , ):
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = intermediate_size
A = hidden_act
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = initializer_range
A = layer_norm_eps
A = image_size
A = num_frames
A = tubelet_size
A = num_channels
A = qkv_bias
super().__init__(**__UpperCamelCase )
| 292
| 1
|
import pytest
lowerCAmelCase__ : Optional[int] ='__dummy_dataset1__'
lowerCAmelCase__ : str ='\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n def _info(self):\n features = datasets.Features(\n {\n "tokens": datasets.Sequence(datasets.Value("string")),\n "ner_tags": datasets.Sequence(\n datasets.features.ClassLabel(\n names=[\n "O",\n "B-PER",\n "I-PER",\n "B-ORG",\n "I-ORG",\n "B-LOC",\n "I-LOC",\n ]\n )\n ),\n "langs": datasets.Sequence(datasets.Value("string")),\n "spans": datasets.Sequence(datasets.Value("string")),\n }\n )\n return datasets.DatasetInfo(features=features)\n\n def _split_generators(self, dl_manager):\n dl_path = dl_manager.download(URLS)\n return [\n datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),\n datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),\n ]\n\n def _generate_examples(self, filepath):\n with open(filepath, "r", encoding="utf-8") as f:\n for i, line in enumerate(f):\n yield i, json.loads(line)\n'
@pytest.fixture
def a__ ( ):
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def a__ ( ):
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def a__ ( A__, A__, A__ ):
SCREAMING_SNAKE_CASE_ : List[str] = dataset_loading_script_name
SCREAMING_SNAKE_CASE_ : Union[str, Any] = tmp_path / 'datasets' / script_name
script_dir.mkdir(parents=A__ )
SCREAMING_SNAKE_CASE_ : List[str] = script_dir / F'''{script_name}.py'''
with open(A__, 'w' ) as f:
f.write(A__ )
return str(A__ )
| 359
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ : List[Any] =logging.get_logger(__name__)
lowerCAmelCase__ : Tuple ={
'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}
class __lowercase (__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCAmelCase = """focalnet"""
def __init__( self , lowerCAmelCase__=2_2_4 , lowerCAmelCase__=4 , lowerCAmelCase__=3 , lowerCAmelCase__=9_6 , lowerCAmelCase__=False , lowerCAmelCase__=[1_9_2, 3_8_4, 7_6_8, 7_6_8] , lowerCAmelCase__=[2, 2, 6, 2] , lowerCAmelCase__=[2, 2, 2, 2] , lowerCAmelCase__=[3, 3, 3, 3] , lowerCAmelCase__="gelu" , lowerCAmelCase__=4.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.1 , lowerCAmelCase__=False , lowerCAmelCase__=1E-4 , lowerCAmelCase__=False , lowerCAmelCase__=False , lowerCAmelCase__=False , lowerCAmelCase__=0.02 , lowerCAmelCase__=1E-5 , lowerCAmelCase__=3_2 , lowerCAmelCase__=None , lowerCAmelCase__=None , **lowerCAmelCase__ , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Dict = image_size
SCREAMING_SNAKE_CASE_ : str = patch_size
SCREAMING_SNAKE_CASE_ : List[Any] = num_channels
SCREAMING_SNAKE_CASE_ : Union[str, Any] = embed_dim
SCREAMING_SNAKE_CASE_ : Any = use_conv_embed
SCREAMING_SNAKE_CASE_ : Dict = hidden_sizes
SCREAMING_SNAKE_CASE_ : Any = depths
SCREAMING_SNAKE_CASE_ : Optional[Any] = focal_levels
SCREAMING_SNAKE_CASE_ : Any = focal_windows
SCREAMING_SNAKE_CASE_ : Tuple = hidden_act
SCREAMING_SNAKE_CASE_ : Dict = mlp_ratio
SCREAMING_SNAKE_CASE_ : Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : Tuple = drop_path_rate
SCREAMING_SNAKE_CASE_ : List[Any] = use_layerscale
SCREAMING_SNAKE_CASE_ : List[Any] = layerscale_value
SCREAMING_SNAKE_CASE_ : List[str] = use_post_layernorm
SCREAMING_SNAKE_CASE_ : Optional[int] = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE_ : str = normalize_modulator
SCREAMING_SNAKE_CASE_ : List[str] = initializer_range
SCREAMING_SNAKE_CASE_ : str = layer_norm_eps
SCREAMING_SNAKE_CASE_ : Dict = encoder_stride
SCREAMING_SNAKE_CASE_ : Dict = ['stem'] + [F'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[str] = get_aligned_output_features_output_indices(
out_features=lowerCAmelCase__ , out_indices=lowerCAmelCase__ , stage_names=self.stage_names )
| 162
| 0
|
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 99
|
def A_ ( A__ , A__ ) -> str:
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
a__ : List[str] = str(bin(A__ ) )[2:] # remove the leading "0b"
a__ : Optional[int] = str(bin(A__ ) )[2:] # remove the leading "0b"
a__ : List[str] = max(len(A__ ) , len(A__ ) )
return "0b" + "".join(
str(int(char_a == '1' and char_b == '1' ) )
for char_a, char_b in zip(a_binary.zfill(A__ ) , b_binary.zfill(A__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 99
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 lowercase ( snake_case__ , unittest.TestCase):
"""simple docstring"""
a__ : Dict = KandinskyImgaImgPipeline
a__ : Union[str, Any] = ["prompt", "image_embeds", "negative_image_embeds", "image"]
a__ : List[Any] = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
]
a__ : Any = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
a__ : List[str] = False
@property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Dict:
return 32
@property
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Tuple:
return 32
@property
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> Union[str, Any]:
return self.time_input_dim
@property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> List[Any]:
return self.time_input_dim * 4
@property
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[Any]:
return 100
@property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Dict:
UpperCAmelCase_= XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> int:
torch.manual_seed(0 )
UpperCAmelCase_= MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_005 , )
UpperCAmelCase_= MultilingualCLIP(__UpperCAmelCase )
UpperCAmelCase_= text_encoder.eval()
return text_encoder
@property
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> List[str]:
torch.manual_seed(0 )
UpperCAmelCase_= {
"""in_channels""": 4,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""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""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
UpperCAmelCase_= UNetaDConditionModel(**__UpperCAmelCase )
return model
@property
def _SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]:
return {
"block_out_channels": [32, 64],
"down_block_types": ["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",
],
"vq_embed_dim": 4,
}
@property
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> List[Any]:
torch.manual_seed(0 )
UpperCAmelCase_= VQModel(**self.dummy_movq_kwargs )
return model
def _SCREAMING_SNAKE_CASE ( self : str ) -> Any:
UpperCAmelCase_= self.dummy_text_encoder
UpperCAmelCase_= self.dummy_tokenizer
UpperCAmelCase_= self.dummy_unet
UpperCAmelCase_= self.dummy_movq
UpperCAmelCase_= {
"""num_train_timesteps""": 1_000,
"""beta_schedule""": """linear""",
"""beta_start""": 0.00_085,
"""beta_end""": 0.012,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
UpperCAmelCase_= DDIMScheduler(**__UpperCAmelCase )
UpperCAmelCase_= {
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def _SCREAMING_SNAKE_CASE ( self : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : Union[str, Any]=0 ) -> Union[str, Any]:
UpperCAmelCase_= floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
UpperCAmelCase_= floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__UpperCAmelCase )
# create init_image
UpperCAmelCase_= floats_tensor((1, 3, 64, 64) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
UpperCAmelCase_= image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase_= Image.fromarray(np.uinta(__UpperCAmelCase ) ).convert("""RGB""" ).resize((256, 256) )
if str(__UpperCAmelCase ).startswith("""mps""" ):
UpperCAmelCase_= torch.manual_seed(__UpperCAmelCase )
else:
UpperCAmelCase_= torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
UpperCAmelCase_= {
"""prompt""": """horse""",
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 10,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[Any]:
UpperCAmelCase_= """cpu"""
UpperCAmelCase_= self.get_dummy_components()
UpperCAmelCase_= self.pipeline_class(**__UpperCAmelCase )
UpperCAmelCase_= pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
UpperCAmelCase_= pipe(**self.get_dummy_inputs(__UpperCAmelCase ) )
UpperCAmelCase_= output.images
UpperCAmelCase_= pipe(
**self.get_dummy_inputs(__UpperCAmelCase ) , return_dict=__UpperCAmelCase , )[0]
UpperCAmelCase_= image[0, -3:, -3:, -1]
UpperCAmelCase_= image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase_= np.array(
[0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233] )
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 lowercase ( unittest.TestCase):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[int]:
UpperCAmelCase_= load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_img2img_frog.npy""" )
UpperCAmelCase_= load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
UpperCAmelCase_= """A red cartoon frog, 4k"""
UpperCAmelCase_= KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(__UpperCAmelCase )
UpperCAmelCase_= KandinskyImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1""" , torch_dtype=torch.floataa )
UpperCAmelCase_= pipeline.to(__UpperCAmelCase )
pipeline.set_progress_bar_config(disable=__UpperCAmelCase )
UpperCAmelCase_= torch.Generator(device="""cpu""" ).manual_seed(0 )
UpperCAmelCase_, UpperCAmelCase_= pipe_prior(
__UpperCAmelCase , generator=__UpperCAmelCase , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
UpperCAmelCase_= pipeline(
__UpperCAmelCase , image=__UpperCAmelCase , image_embeds=__UpperCAmelCase , negative_image_embeds=__UpperCAmelCase , generator=__UpperCAmelCase , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type="""np""" , )
UpperCAmelCase_= output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__UpperCAmelCase , __UpperCAmelCase )
| 277
|
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__A = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
'''
__A = '''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''
__A = '''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
\'google_bleu\': google_bleu score
Examples:
Example 1:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.44
Example 2:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.61
Example 3:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results["google_bleu"], 2))
0.53
Example 4:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results["google_bleu"], 2))
0.4
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowercase ( datasets.Metric):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> MetricInfo:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ),
"""references""": datasets.Sequence(
datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ),
} ) , )
def _SCREAMING_SNAKE_CASE ( self : Any , __UpperCAmelCase : List[List[List[str]]] , __UpperCAmelCase : List[List[str]] , __UpperCAmelCase : int = 1 , __UpperCAmelCase : int = 4 , ) -> Dict[str, float]:
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=__UpperCAmelCase , hypotheses=__UpperCAmelCase , min_len=__UpperCAmelCase , max_len=__UpperCAmelCase )
}
| 277
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A : List[Any] = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
"configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[str] = ["MaskFormerFeatureExtractor"]
A : Dict = ["MaskFormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = [
"MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"MaskFormerForInstanceSegmentation",
"MaskFormerModel",
"MaskFormerPreTrainedModel",
]
A : int = [
"MaskFormerSwinBackbone",
"MaskFormerSwinModel",
"MaskFormerSwinPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
from .configuration_maskformer_swin import MaskFormerSwinConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_maskformer import MaskFormerFeatureExtractor
from .image_processing_maskformer import MaskFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskformer import (
MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskFormerForInstanceSegmentation,
MaskFormerModel,
MaskFormerPreTrainedModel,
)
from .modeling_maskformer_swin import (
MaskFormerSwinBackbone,
MaskFormerSwinModel,
MaskFormerSwinPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 184
|
def lowercase_ ( _A : int , _A : int ):
"""simple docstring"""
while a != 0:
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = b % a, a
return b
def lowercase_ ( _A : int , _A : int ):
"""simple docstring"""
if gcd(_A , _A ) != 1:
lowerCamelCase__ : List[str] = F"mod inverse of {a!r} and {m!r} does not exist"
raise ValueError(_A )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Tuple = 1, 0, a
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] = 0, 1, m
while va != 0:
lowerCamelCase__ : Tuple = ua // va
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 184
| 1
|
"""simple docstring"""
import numpy as np
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
return vector * sigmoid(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 326
|
"""simple docstring"""
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 ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
UpperCamelCase__ = (
{
"""feature-extraction""": TFMobileBertModel,
"""fill-mask""": TFMobileBertForMaskedLM,
"""question-answering""": TFMobileBertForQuestionAnswering,
"""text-classification""": TFMobileBertForSequenceClassification,
"""token-classification""": TFMobileBertForTokenClassification,
"""zero-shot""": TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
def lowercase__ ( self : Tuple , __UpperCamelCase : List[Any] , __UpperCamelCase : Any , __UpperCamelCase : str=False )->Optional[Any]:
_UpperCAmelCase = super()._prepare_for_class(__UpperCamelCase , __UpperCamelCase , return_labels=__UpperCamelCase )
if return_labels:
if model_class in get_values(__UpperCamelCase ):
_UpperCAmelCase = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : Union[str, Any] , __UpperCamelCase : Tuple , __UpperCamelCase : Any=1_3 , __UpperCamelCase : Any=7 , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : Optional[Any]=True , __UpperCamelCase : Union[str, Any]=True , __UpperCamelCase : Union[str, Any]=True , __UpperCamelCase : Dict=9_9 , __UpperCamelCase : Optional[int]=3_2 , __UpperCamelCase : Union[str, Any]=3_2 , __UpperCamelCase : List[str]=2 , __UpperCamelCase : Dict=4 , __UpperCamelCase : Optional[Any]=3_7 , __UpperCamelCase : List[str]="gelu" , __UpperCamelCase : List[Any]=0.1 , __UpperCamelCase : Optional[int]=0.1 , __UpperCamelCase : Optional[Any]=5_1_2 , __UpperCamelCase : Any=1_6 , __UpperCamelCase : Dict=2 , __UpperCamelCase : Optional[int]=0.0_2 , __UpperCamelCase : Optional[int]=3 , __UpperCamelCase : Tuple=4 , __UpperCamelCase : List[str]=None , )->Any:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_choices
_UpperCAmelCase = scope
_UpperCAmelCase = embedding_size
def lowercase__ ( self : Optional[int] )->int:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase = 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 : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Dict , __UpperCamelCase : Optional[Any] )->List[Any]:
_UpperCAmelCase = TFMobileBertModel(config=__UpperCamelCase )
_UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
_UpperCAmelCase = model(__UpperCamelCase )
_UpperCAmelCase = [input_ids, input_mask]
_UpperCAmelCase = model(__UpperCamelCase )
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowercase__ ( self : int , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] )->Tuple:
_UpperCAmelCase = TFMobileBertForMaskedLM(config=__UpperCamelCase )
_UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase__ ( self : List[Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Tuple , __UpperCamelCase : int , __UpperCamelCase : Tuple , __UpperCamelCase : Any )->List[Any]:
_UpperCAmelCase = TFMobileBertForNextSentencePrediction(config=__UpperCamelCase )
_UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : int , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : Optional[Any] , __UpperCamelCase : int , __UpperCamelCase : Dict )->List[Any]:
_UpperCAmelCase = TFMobileBertForPreTraining(config=__UpperCamelCase )
_UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
_UpperCAmelCase = model(__UpperCamelCase )
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 : Optional[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Dict , __UpperCamelCase : Any , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] , __UpperCamelCase : str , __UpperCamelCase : Optional[Any] )->Any:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = TFMobileBertForSequenceClassification(config=__UpperCamelCase )
_UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase__ ( self : Dict , __UpperCamelCase : str , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str] , __UpperCamelCase : Dict , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] )->List[str]:
_UpperCAmelCase = self.num_choices
_UpperCAmelCase = TFMobileBertForMultipleChoice(config=__UpperCamelCase )
_UpperCAmelCase = tf.tile(tf.expand_dims(__UpperCamelCase , 1 ) , (1, self.num_choices, 1) )
_UpperCAmelCase = tf.tile(tf.expand_dims(__UpperCamelCase , 1 ) , (1, self.num_choices, 1) )
_UpperCAmelCase = tf.tile(tf.expand_dims(__UpperCamelCase , 1 ) , (1, self.num_choices, 1) )
_UpperCAmelCase = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowercase__ ( self : Any , __UpperCamelCase : int , __UpperCamelCase : List[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : str , __UpperCamelCase : Optional[int] , __UpperCamelCase : str , __UpperCamelCase : Any )->Dict:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = TFMobileBertForTokenClassification(config=__UpperCamelCase )
_UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase__ ( self : Optional[int] , __UpperCamelCase : List[str] , __UpperCamelCase : str , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] )->List[Any]:
_UpperCAmelCase = TFMobileBertForQuestionAnswering(config=__UpperCamelCase )
_UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
_UpperCAmelCase = 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 lowercase__ ( self : List[str] )->Optional[Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
def lowercase__ ( self : List[Any] )->str:
_UpperCAmelCase = TFMobileBertModelTest.TFMobileBertModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=3_7 )
def lowercase__ ( self : List[Any] )->List[str]:
self.config_tester.run_common_tests()
def lowercase__ ( self : Optional[Any] )->Union[str, Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__UpperCamelCase )
def lowercase__ ( self : Any )->Union[str, Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__UpperCamelCase )
def lowercase__ ( self : List[Any] )->Optional[int]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__UpperCamelCase )
def lowercase__ ( self : str )->Optional[int]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__UpperCamelCase )
def lowercase__ ( self : Any )->List[str]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__UpperCamelCase )
def lowercase__ ( self : Dict )->Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__UpperCamelCase )
def lowercase__ ( self : Any )->Optional[Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__UpperCamelCase )
def lowercase__ ( self : List[str] )->Tuple:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__UpperCamelCase )
@slow
def lowercase__ ( self : Tuple )->List[str]:
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/mobilebert-uncased"]:
_UpperCAmelCase = TFMobileBertModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
@require_tf
class _a ( unittest.TestCase):
"""simple docstring"""
@slow
def lowercase__ ( self : str )->Dict:
_UpperCAmelCase = TFMobileBertForPreTraining.from_pretrained('''google/mobilebert-uncased''' )
_UpperCAmelCase = tf.constant([[0, 1, 2, 3, 4, 5]] )
_UpperCAmelCase = model(__UpperCamelCase )[0]
_UpperCAmelCase = [1, 6, 3_0_5_2_2]
self.assertEqual(output.shape , __UpperCamelCase )
_UpperCAmelCase = tf.constant(
[
[
[-4.5_9_1_9_5_4_7, -9.2_4_8_2_9_5, -9.6_4_5_2_5_6],
[-6.7_3_0_6_1_7_5, -6.4_4_0_2_8_4, -6.6_0_5_2_8_3_7],
[-7.2_7_4_3_5_0_6, -6.7_8_4_7_9_1_5, -6.0_2_4_6_7_3],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1e-4 )
| 326
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Optional[int] = logging.get_logger(__name__)
A : 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 _lowercase ( lowercase__):
"""simple docstring"""
A__ = "luke"
def __init__( self : Tuple , __lowerCamelCase : Any=50267 , __lowerCamelCase : Any=500000 , __lowerCamelCase : str=768 , __lowerCamelCase : int=256 , __lowerCamelCase : str=12 , __lowerCamelCase : int=12 , __lowerCamelCase : Optional[int]=3072 , __lowerCamelCase : Dict="gelu" , __lowerCamelCase : Optional[Any]=0.1 , __lowerCamelCase : str=0.1 , __lowerCamelCase : Dict=512 , __lowerCamelCase : List[str]=2 , __lowerCamelCase : Optional[int]=0.0_2 , __lowerCamelCase : Union[str, Any]=1E-1_2 , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : List[str]=None , __lowerCamelCase : Dict=1 , __lowerCamelCase : List[str]=0 , __lowerCamelCase : Tuple=2 , **__lowerCamelCase : List[str] , ):
'''simple docstring'''
super().__init__(pad_token_id=__lowerCamelCase , bos_token_id=__lowerCamelCase , eos_token_id=__lowerCamelCase , **__lowerCamelCase )
lowerCamelCase__ : Tuple = vocab_size
lowerCamelCase__ : Any = entity_vocab_size
lowerCamelCase__ : Dict = hidden_size
lowerCamelCase__ : Union[str, Any] = entity_emb_size
lowerCamelCase__ : List[Any] = num_hidden_layers
lowerCamelCase__ : str = num_attention_heads
lowerCamelCase__ : List[str] = hidden_act
lowerCamelCase__ : List[str] = intermediate_size
lowerCamelCase__ : Optional[Any] = hidden_dropout_prob
lowerCamelCase__ : str = attention_probs_dropout_prob
lowerCamelCase__ : Any = max_position_embeddings
lowerCamelCase__ : Dict = type_vocab_size
lowerCamelCase__ : Union[str, Any] = initializer_range
lowerCamelCase__ : Any = layer_norm_eps
lowerCamelCase__ : int = use_entity_aware_attention
lowerCamelCase__ : Any = classifier_dropout
| 184
|
def lowercase_ ( _A : int , _A : int ):
"""simple docstring"""
while a != 0:
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = b % a, a
return b
def lowercase_ ( _A : int , _A : int ):
"""simple docstring"""
if gcd(_A , _A ) != 1:
lowerCamelCase__ : List[str] = F"mod inverse of {a!r} and {m!r} does not exist"
raise ValueError(_A )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Tuple = 1, 0, a
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] = 0, 1, m
while va != 0:
lowerCamelCase__ : Tuple = ua // va
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 184
| 1
|
'''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 UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@parameterized.expand([(None,), ('''foo.json''',)] )
def _a ( self : Tuple , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : Union[str, Any] = GenerationConfig(
do_sample=_lowerCamelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_lowerCamelCase , config_name=_lowerCamelCase )
A_ : Optional[Any] = GenerationConfig.from_pretrained(_lowerCamelCase , config_name=_lowerCamelCase )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , _lowerCamelCase )
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 , _lowerCamelCase )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Optional[Any] = AutoConfig.from_pretrained('''gpt2''' )
A_ : Optional[Any] = GenerationConfig.from_model_config(_lowerCamelCase )
A_ : Optional[Any] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(_lowerCamelCase , _lowerCamelCase )
# 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 _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Tuple = GenerationConfig()
A_ : Optional[Any] = {
'''max_new_tokens''': 1024,
'''foo''': '''bar''',
}
A_ : List[Any] = copy.deepcopy(_lowerCamelCase )
A_ : int = generation_config.update(**_lowerCamelCase )
# update_kwargs was not modified (no side effects)
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
# 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(_lowerCamelCase , {'''foo''': '''bar'''} )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Tuple = GenerationConfig()
A_ : Any = '''bar'''
with tempfile.TemporaryDirectory('''test-generation-config''' ) as tmp_dir:
generation_config.save_pretrained(_lowerCamelCase )
A_ : Tuple = GenerationConfig.from_pretrained(_lowerCamelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , '''bar''' )
A_ : int = GenerationConfig.from_model_config(_lowerCamelCase )
assert not hasattr(_lowerCamelCase , '''foo''' ) # no new kwargs should be initialized if from config
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[int] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , _lowerCamelCase )
self.assertEqual(default_config.num_beams , 1 )
A_ : Dict = GenerationConfig(
do_sample=_lowerCamelCase , 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 , _lowerCamelCase )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_lowerCamelCase )
A_ : int = GenerationConfig.from_pretrained(_lowerCamelCase , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , _lowerCamelCase )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@classmethod
def _a ( cls : List[str] ):
"""simple docstring"""
A_ : Optional[int] = TOKEN
HfFolder.save_token(_lowerCamelCase )
@classmethod
def _a ( cls : Optional[Any] ):
"""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 _a ( self : Any ):
"""simple docstring"""
A_ : List[str] = GenerationConfig(
do_sample=_lowerCamelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''test-generation-config''' , use_auth_token=self._token )
A_ : List[Any] = GenerationConfig.from_pretrained(f'{USER}/test-generation-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# 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(
_lowerCamelCase , repo_id='''test-generation-config''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : Any = GenerationConfig.from_pretrained(f'{USER}/test-generation-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Optional[Any] = GenerationConfig(
do_sample=_lowerCamelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''valid_org/test-generation-config-org''' , use_auth_token=self._token )
A_ : Dict = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# 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(
_lowerCamelCase , repo_id='''valid_org/test-generation-config-org''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : Union[str, Any] = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
| 4
|
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
snake_case__ = get_tests_dir("""fixtures""")
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] ):
"""simple docstring"""
A_ : List[Any] = mock.Mock()
A_ : List[str] = 500
A_ : Tuple = {}
A_ : int = HTTPError
A_ : Optional[Any] = {}
# Download this model to make sure it's in the cache.
A_ : Tuple = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=_lowerCamelCase ) as mock_head:
A_ : List[Any] = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# This check we did call the fake head request
mock_head.assert_called()
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = ViTImageProcessor.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json''' )
def _a ( self : Dict ):
"""simple docstring"""
with self.assertRaises(_lowerCamelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
A_ : Any = AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants''' )
A_ : Tuple = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/stable-diffusion-all-variants''' , subfolder='''feature_extractor''' )
self.assertIsNotNone(_lowerCamelCase )
@is_staging_test
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@classmethod
def _a ( cls : Tuple ):
"""simple docstring"""
A_ : int = TOKEN
HfFolder.save_token(_lowerCamelCase )
@classmethod
def _a ( cls : str ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='''test-image-processor''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-image-processor-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-image-processor''' )
except HTTPError:
pass
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Dict = ViTImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''test-image-processor''' , use_auth_token=self._token )
A_ : Optional[int] = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCamelCase , repo_id='''test-image-processor''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : List[Any] = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : int = ViTImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''valid_org/test-image-processor''' , use_auth_token=self._token )
A_ : List[str] = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCamelCase , repo_id='''valid_org/test-image-processor-org''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : Any = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
CustomImageProcessor.register_for_auto_class()
A_ : Any = CustomImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''test-dynamic-image-processor''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''} , )
A_ : str = AutoImageProcessor.from_pretrained(
f'{USER}/test-dynamic-image-processor' , trust_remote_code=_lowerCamelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , '''CustomImageProcessor''' )
| 4
| 1
|
def __lowercase ( _UpperCamelCase ) ->bool:
"""simple docstring"""
lowercase : Optional[int] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 337
|
'''simple docstring'''
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__lowerCamelCase = '''src/transformers'''
__lowerCamelCase = '''docs/source/en/tasks'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> List[str]:
with open(UpperCAmelCase__, """r""", encoding="""utf-8""", newline="""\n""" ) as f:
A_ = f.readlines()
# Find the start prompt.
A_ = 0
while not lines[start_index].startswith(UpperCAmelCase__ ):
start_index += 1
start_index += 1
A_ = start_index
while not lines[end_index].startswith(UpperCAmelCase__ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase = direct_transformers_import(TRANSFORMERS_PATH)
__lowerCamelCase = {
'''asr.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
'''audio_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
'''language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
'''image_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
'''masked_language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
'''multiple_choice.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
'''object_detection.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
'''question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
'''semantic_segmentation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
'''sequence_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
'''summarization.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'''token_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
'''translation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'''video_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
'''document_question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
'''monocular_depth_estimation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__lowerCamelCase = {
'''summarization.md''': ('''nllb''',),
'''translation.md''': ('''nllb''',),
}
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Dict:
A_ = TASK_GUIDE_TO_MODELS[task_guide]
A_ = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase__, set() )
A_ = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([F'''[{name}](../model_doc/{code})''' for code, name in model_names.items()] ) + "\n"
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__=False ) -> Optional[Any]:
A_ , A_ , A_ , A_ = _find_text_in_file(
filename=os.path.join(UpperCAmelCase__, UpperCAmelCase__ ), start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->""", end_prompt="""<!--End of the generated tip-->""", )
A_ = get_model_list_for_task(UpperCAmelCase__ )
if current_list != new_list:
if overwrite:
with open(os.path.join(UpperCAmelCase__, UpperCAmelCase__ ), """w""", encoding="""utf-8""", newline="""\n""" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
F'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
""" to fix this.""" )
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
__lowerCamelCase = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
| 162
| 0
|
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
UpperCamelCase = pd.read_csv('''sample_data.csv''', header=None)
UpperCamelCase = df.shape[:1][0]
# If you're using some other dataset input the target column
UpperCamelCase = df.iloc[:, 1:2]
UpperCamelCase = actual_data.values.reshape(len_data, 1)
UpperCamelCase = MinMaxScaler().fit_transform(actual_data)
UpperCamelCase = 10
UpperCamelCase = 5
UpperCamelCase = 20
UpperCamelCase = len_data - periods * look_back
UpperCamelCase = actual_data[:division]
UpperCamelCase = actual_data[division - look_back :]
UpperCamelCase = [], []
UpperCamelCase = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
UpperCamelCase = np.array(train_x)
UpperCamelCase = np.array(test_x)
UpperCamelCase = np.array([list(i.ravel()) for i in train_y])
UpperCamelCase = np.array([list(i.ravel()) for i in test_y])
UpperCamelCase = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss='''mean_squared_error''', optimizer='''adam''')
UpperCamelCase = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
UpperCamelCase = model.predict(x_test)
| 364
|
def lowercase_ ( _lowerCamelCase : int = 1 , _lowerCamelCase : int = 1000):
lowercase__ : Union[str, Any] = 1
lowercase__ : int = 0
for divide_by_number in range(_lowerCamelCase , digit + 1):
lowercase__ : list[int] = []
lowercase__ : Dict = numerator
for _ in range(1 , digit + 1):
if now_divide in has_been_divided:
if longest_list_length < len(_lowerCamelCase):
lowercase__ : Union[str, Any] = len(_lowerCamelCase)
lowercase__ : Optional[int] = divide_by_number
else:
has_been_divided.append(_lowerCamelCase)
lowercase__ : Optional[Any] = now_divide * 10 % divide_by_number
return the_digit
# Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 333
| 0
|
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""image_processor""", """tokenizer"""]
_SCREAMING_SNAKE_CASE = """BlipImageProcessor"""
_SCREAMING_SNAKE_CASE = """AutoTokenizer"""
def __init__( self : List[str], _snake_case : Union[str, Any], _snake_case : Any ) ->Optional[int]:
snake_case__ : List[str] = False
super().__init__(_snake_case, _snake_case )
snake_case__ : int = self.image_processor
def __call__( self : List[str], _snake_case : ImageInput = None, _snake_case : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, _snake_case : bool = True, _snake_case : Union[bool, str, PaddingStrategy] = False, _snake_case : Union[bool, str, TruncationStrategy] = None, _snake_case : Optional[int] = None, _snake_case : int = 0, _snake_case : Optional[int] = None, _snake_case : Optional[bool] = None, _snake_case : bool = False, _snake_case : bool = False, _snake_case : bool = False, _snake_case : bool = False, _snake_case : bool = False, _snake_case : bool = True, _snake_case : Optional[Union[str, TensorType]] = None, **_snake_case : Optional[int], ) ->BatchEncoding:
if images is None and text is None:
raise ValueError('You have to specify either images or text.' )
# Get only text
if images is None:
snake_case__ : List[Any] = self.tokenizer
snake_case__ : Dict = self.tokenizer(
text=_snake_case, add_special_tokens=_snake_case, padding=_snake_case, truncation=_snake_case, max_length=_snake_case, stride=_snake_case, pad_to_multiple_of=_snake_case, return_attention_mask=_snake_case, return_overflowing_tokens=_snake_case, return_special_tokens_mask=_snake_case, return_offsets_mapping=_snake_case, return_token_type_ids=_snake_case, return_length=_snake_case, verbose=_snake_case, return_tensors=_snake_case, **_snake_case, )
return text_encoding
# add pixel_values
snake_case__ : List[str] = self.image_processor(_snake_case, return_tensors=_snake_case )
if text is not None:
snake_case__ : Dict = self.tokenizer(
text=_snake_case, add_special_tokens=_snake_case, padding=_snake_case, truncation=_snake_case, max_length=_snake_case, stride=_snake_case, pad_to_multiple_of=_snake_case, return_attention_mask=_snake_case, return_overflowing_tokens=_snake_case, return_special_tokens_mask=_snake_case, return_offsets_mapping=_snake_case, return_token_type_ids=_snake_case, return_length=_snake_case, verbose=_snake_case, return_tensors=_snake_case, **_snake_case, )
else:
snake_case__ : Dict = None
if text_encoding is not None:
encoding_image_processor.update(_snake_case )
return encoding_image_processor
def lowercase_ ( self : str, *_snake_case : int, **_snake_case : Optional[int] ) ->int:
return self.tokenizer.batch_decode(*_snake_case, **_snake_case )
def lowercase_ ( self : str, *_snake_case : Optional[int], **_snake_case : List[str] ) ->Dict:
return self.tokenizer.decode(*_snake_case, **_snake_case )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def lowercase_ ( self : Tuple ) ->List[str]:
snake_case__ : Tuple = self.tokenizer.model_input_names
snake_case__ : Any = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 277
|
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
a_ :Tuple = logging.get_logger(__name__)
a_ :Union[str, Any] = {
"microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
"microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
"microsoft/deberta-v2-xlarge-mnli": (
"https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
),
"microsoft/deberta-v2-xxlarge-mnli": (
"https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
),
}
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """deberta-v2"""
def __init__( self : Union[str, Any], _snake_case : Dict=1_2_8_1_0_0, _snake_case : Any=1_5_3_6, _snake_case : Tuple=2_4, _snake_case : int=2_4, _snake_case : Optional[int]=6_1_4_4, _snake_case : Optional[int]="gelu", _snake_case : Optional[int]=0.1, _snake_case : List[str]=0.1, _snake_case : str=5_1_2, _snake_case : Optional[int]=0, _snake_case : Optional[int]=0.0_2, _snake_case : Dict=1e-7, _snake_case : int=False, _snake_case : Any=-1, _snake_case : List[str]=0, _snake_case : Tuple=True, _snake_case : Any=None, _snake_case : Union[str, Any]=0, _snake_case : Tuple="gelu", **_snake_case : Union[str, Any], ) ->Optional[int]:
super().__init__(**_snake_case )
snake_case__ : Dict = hidden_size
snake_case__ : Optional[int] = num_hidden_layers
snake_case__ : Any = num_attention_heads
snake_case__ : List[Any] = intermediate_size
snake_case__ : List[Any] = hidden_act
snake_case__ : Union[str, Any] = hidden_dropout_prob
snake_case__ : Dict = attention_probs_dropout_prob
snake_case__ : List[str] = max_position_embeddings
snake_case__ : List[str] = type_vocab_size
snake_case__ : Optional[Any] = initializer_range
snake_case__ : Optional[int] = relative_attention
snake_case__ : Tuple = max_relative_positions
snake_case__ : Union[str, Any] = pad_token_id
snake_case__ : Optional[int] = position_biased_input
# Backwards compatibility
if type(_snake_case ) == str:
snake_case__ : int = [x.strip() for x in pos_att_type.lower().split('|' )]
snake_case__ : List[str] = pos_att_type
snake_case__ : Union[str, Any] = vocab_size
snake_case__ : Optional[int] = layer_norm_eps
snake_case__ : Optional[int] = kwargs.get('pooler_hidden_size', _snake_case )
snake_case__ : int = pooler_dropout
snake_case__ : str = pooler_hidden_act
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
@property
def lowercase_ ( self : Optional[int] ) ->Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
snake_case__ : List[Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
snake_case__ : int = {0: 'batch', 1: 'sequence'}
if self._config.type_vocab_size > 0:
return OrderedDict(
[('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)] )
else:
return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)] )
@property
def lowercase_ ( self : Dict ) ->int:
return 1_2
def lowercase_ ( self : Tuple, _snake_case : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], _snake_case : int = -1, _snake_case : int = -1, _snake_case : int = -1, _snake_case : bool = False, _snake_case : Optional["TensorType"] = None, _snake_case : int = 3, _snake_case : int = 4_0, _snake_case : int = 4_0, _snake_case : "PreTrainedTokenizerBase" = None, ) ->Mapping[str, Any]:
snake_case__ : Union[str, Any] = super().generate_dummy_inputs(preprocessor=_snake_case, framework=_snake_case )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 277
| 1
|
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCamelCase :
'''simple docstring'''
def __init__( self : Dict , a_ : List[str] , a_ : int=13 , a_ : int=7 , a_ : Optional[Any]=True , a_ : Optional[int]=True , a_ : Dict=True , a_ : Dict=True , a_ : List[Any]=True , a_ : Dict=False , a_ : Dict=False , a_ : Any=False , a_ : str=2 , a_ : Any=99 , a_ : Tuple=0 , a_ : int=32 , a_ : Optional[int]=5 , a_ : List[Any]=4 , a_ : List[str]=0.1 , a_ : Optional[Any]=0.1 , a_ : Tuple=5_12 , a_ : Any=2 , a_ : List[Any]=0.02 , a_ : Optional[Any]=2 , a_ : Tuple=4 , a_ : Optional[int]="last" , a_ : Dict=True , a_ : List[str]=None , a_ : Dict=0 , ):
lowerCAmelCase_ : Tuple = parent
lowerCAmelCase_ : Optional[int] = batch_size
lowerCAmelCase_ : Union[str, Any] = seq_length
lowerCAmelCase_ : Any = is_training
lowerCAmelCase_ : List[str] = use_input_lengths
lowerCAmelCase_ : List[Any] = use_token_type_ids
lowerCAmelCase_ : List[str] = use_labels
lowerCAmelCase_ : Optional[int] = gelu_activation
lowerCAmelCase_ : List[Any] = sinusoidal_embeddings
lowerCAmelCase_ : Optional[int] = causal
lowerCAmelCase_ : List[str] = asm
lowerCAmelCase_ : Tuple = n_langs
lowerCAmelCase_ : Tuple = vocab_size
lowerCAmelCase_ : Optional[Any] = n_special
lowerCAmelCase_ : List[str] = hidden_size
lowerCAmelCase_ : str = num_hidden_layers
lowerCAmelCase_ : Union[str, Any] = num_attention_heads
lowerCAmelCase_ : List[str] = hidden_dropout_prob
lowerCAmelCase_ : Any = attention_probs_dropout_prob
lowerCAmelCase_ : int = max_position_embeddings
lowerCAmelCase_ : int = type_sequence_label_size
lowerCAmelCase_ : Dict = initializer_range
lowerCAmelCase_ : Dict = num_labels
lowerCAmelCase_ : int = num_choices
lowerCAmelCase_ : Optional[Any] = summary_type
lowerCAmelCase_ : int = use_proj
lowerCAmelCase_ : Dict = scope
lowerCAmelCase_ : str = bos_token_id
def lowerCamelCase ( self : List[str] ):
lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase_ : Dict = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ : Union[str, Any] = None
if self.use_input_lengths:
lowerCAmelCase_ : Optional[int] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
lowerCAmelCase_ : int = None
if self.use_token_type_ids:
lowerCAmelCase_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
lowerCAmelCase_ : Union[str, Any] = None
lowerCAmelCase_ : Union[str, Any] = None
lowerCAmelCase_ : Any = None
if self.use_labels:
lowerCAmelCase_ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ : int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase_ : Union[str, Any] = ids_tensor([self.batch_size] , 2 ).float()
lowerCAmelCase_ : List[str] = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase_ : List[str] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def lowerCamelCase ( self : Union[str, Any] ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def lowerCamelCase ( self : str , a_ : Any , a_ : str , a_ : Tuple , a_ : Optional[int] , a_ : int , a_ : Tuple , a_ : Tuple , a_ : Any , a_ : Any , ):
lowerCAmelCase_ : str = XLMModel(config=a_ )
model.to(a_ )
model.eval()
lowerCAmelCase_ : Tuple = model(a_ , lengths=a_ , langs=a_ )
lowerCAmelCase_ : Optional[Any] = model(a_ , langs=a_ )
lowerCAmelCase_ : Optional[int] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self : Optional[Any] , a_ : Tuple , a_ : str , a_ : List[str] , a_ : str , a_ : Any , a_ : Union[str, Any] , a_ : str , a_ : Dict , a_ : List[str] , ):
lowerCAmelCase_ : Dict = XLMWithLMHeadModel(a_ )
model.to(a_ )
model.eval()
lowerCAmelCase_ : int = model(a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self : Any , a_ : Optional[Any] , a_ : Any , a_ : List[Any] , a_ : Dict , a_ : List[Any] , a_ : Dict , a_ : str , a_ : str , a_ : Union[str, Any] , ):
lowerCAmelCase_ : int = XLMForQuestionAnsweringSimple(a_ )
model.to(a_ )
model.eval()
lowerCAmelCase_ : Dict = model(a_ )
lowerCAmelCase_ : List[str] = model(a_ , start_positions=a_ , end_positions=a_ )
lowerCAmelCase_ : List[Any] = outputs
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 lowerCamelCase ( self : Dict , a_ : Dict , a_ : List[Any] , a_ : List[str] , a_ : int , a_ : Dict , a_ : Any , a_ : Dict , a_ : Optional[Any] , a_ : int , ):
lowerCAmelCase_ : List[Any] = XLMForQuestionAnswering(a_ )
model.to(a_ )
model.eval()
lowerCAmelCase_ : str = model(a_ )
lowerCAmelCase_ : int = model(
a_ , start_positions=a_ , end_positions=a_ , cls_index=a_ , is_impossible=a_ , p_mask=a_ , )
lowerCAmelCase_ : List[Any] = model(
a_ , start_positions=a_ , end_positions=a_ , cls_index=a_ , is_impossible=a_ , )
((lowerCAmelCase_) , ) : Union[str, Any] = result_with_labels.to_tuple()
lowerCAmelCase_ : Tuple = model(a_ , start_positions=a_ , end_positions=a_ )
((lowerCAmelCase_) , ) : Any = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def lowerCamelCase ( self : Optional[int] , a_ : Any , a_ : Any , a_ : int , a_ : Tuple , a_ : Tuple , a_ : List[Any] , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : str , ):
lowerCAmelCase_ : Dict = XLMForSequenceClassification(a_ )
model.to(a_ )
model.eval()
lowerCAmelCase_ : List[str] = model(a_ )
lowerCAmelCase_ : Any = model(a_ , labels=a_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase ( self : Union[str, Any] , a_ : Optional[int] , a_ : List[Any] , a_ : Optional[int] , a_ : List[Any] , a_ : List[str] , a_ : int , a_ : List[Any] , a_ : int , a_ : Tuple , ):
lowerCAmelCase_ : List[str] = self.num_labels
lowerCAmelCase_ : str = XLMForTokenClassification(a_ )
model.to(a_ )
model.eval()
lowerCAmelCase_ : Tuple = model(a_ , attention_mask=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self : List[Any] , a_ : Union[str, Any] , a_ : Tuple , a_ : Any , a_ : Tuple , a_ : Tuple , a_ : Optional[Any] , a_ : Dict , a_ : int , a_ : int , ):
lowerCAmelCase_ : Dict = self.num_choices
lowerCAmelCase_ : Optional[Any] = XLMForMultipleChoice(config=a_ )
model.to(a_ )
model.eval()
lowerCAmelCase_ : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ : List[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ : Dict = model(
a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self : str ):
lowerCAmelCase_ : Tuple = self.prepare_config_and_inputs()
(
(
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) ,
) : Union[str, Any] = config_and_inputs
lowerCAmelCase_ : str = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
return config, inputs_dict
@require_torch
class __lowerCamelCase ( A__ , A__ , A__ , unittest.TestCase ):
'''simple docstring'''
a_ : Optional[Any] = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
a_ : List[str] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
a_ : Union[str, Any] = (
{
"""feature-extraction""": XLMModel,
"""fill-mask""": XLMWithLMHeadModel,
"""question-answering""": XLMForQuestionAnsweringSimple,
"""text-classification""": XLMForSequenceClassification,
"""text-generation""": XLMWithLMHeadModel,
"""token-classification""": XLMForTokenClassification,
"""zero-shot""": XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def lowerCamelCase ( self : Dict , a_ : Dict , a_ : Dict , a_ : List[str] , a_ : List[Any] , a_ : Optional[int] ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def lowerCamelCase ( self : Any , a_ : Union[str, Any] , a_ : Tuple , a_ : str=False ):
lowerCAmelCase_ : Union[str, Any] = super()._prepare_for_class(a_ , a_ , return_labels=a_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
lowerCAmelCase_ : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a_ )
lowerCAmelCase_ : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a_ )
return inputs_dict
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : Union[str, Any] = XLMModelTester(self )
lowerCAmelCase_ : Any = ConfigTester(self , config_class=a_ , emb_dim=37 )
def lowerCamelCase ( self : Optional[int] ):
self.config_tester.run_common_tests()
def lowerCamelCase ( self : int ):
lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*a_ )
def lowerCamelCase ( self : Dict ):
lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*a_ )
def lowerCamelCase ( self : int ):
lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*a_ )
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*a_ )
def lowerCamelCase ( self : str ):
lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*a_ )
def lowerCamelCase ( self : Optional[int] ):
lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*a_ )
def lowerCamelCase ( self : Union[str, Any] ):
lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*a_ )
def lowerCamelCase ( self : Any , a_ : Any , a_ : List[str] , a_ : Tuple , a_ : Optional[Any] , a_ : List[str] , a_ : Dict=False , a_ : str=1 ):
self.assertIsInstance(a_ , a_ )
self.assertListEqual(
[isinstance(a_ , a_ ) for iter_attentions in attentions] , [True] * len(a_ ) )
self.assertEqual(len(a_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(a_ ):
# adds PAD dummy token
lowerCAmelCase_ : List[Any] = min_length + idx + 1
lowerCAmelCase_ : str = min_length + idx + 1
lowerCAmelCase_ : Tuple = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(a_ ) )
def lowerCamelCase ( self : Dict , a_ : Optional[Any] , a_ : Any , a_ : str , a_ : str , a_ : Union[str, Any] , a_ : str=False , a_ : List[Any]=1 ):
self.assertIsInstance(a_ , a_ )
self.assertListEqual(
[isinstance(a_ , a_ ) for iter_hidden_states in hidden_states] , [True] * len(a_ ) , )
self.assertEqual(len(a_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(a_ ):
# adds PAD dummy token
lowerCAmelCase_ : List[Any] = min_length + idx + 1
lowerCAmelCase_ : str = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(a_ ) , )
pass
@slow
def lowerCamelCase ( self : Optional[Any] ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Any = XLMModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCamelCase ( self : int ):
lowerCAmelCase_ : Tuple = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048" )
model.to(a_ )
lowerCAmelCase_ : str = torch.tensor([[14, 4_47]] , dtype=torch.long , device=a_ ) # the president
lowerCAmelCase_ : Union[str, Any] = [
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
lowerCAmelCase_ : Optional[int] = model.generate(a_ , do_sample=a_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , a_ )
| 161
|
"""simple docstring"""
def __lowerCamelCase ( __UpperCamelCase = 50 ) -> int:
"""simple docstring"""
lowerCAmelCase_ : int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 161
| 1
|
import numpy as np
def lowerCAmelCase__( lowercase : np.ndarray ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase__( lowercase : np.ndarray ) -> np.ndarray:
return vector * sigmoid(lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 326
|
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class _lowerCamelCase ( a ):
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase=768 ) -> List[str]:
'''simple docstring'''
super().__init__(UpperCAmelCase )
__snake_case : Optional[int] = proj_size
__snake_case : str = CLIPVisionModel(UpperCAmelCase )
__snake_case : Tuple = PaintByExampleMapper(UpperCAmelCase )
__snake_case : Union[str, Any] = nn.LayerNorm(config.hidden_size )
__snake_case : Optional[Any] = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
__snake_case : Optional[int] = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase=False ) -> List[str]:
'''simple docstring'''
__snake_case : int = self.model(pixel_values=UpperCAmelCase )
__snake_case : Optional[int] = clip_output.pooler_output
__snake_case : Any = self.mapper(latent_states[:, None] )
__snake_case : Any = self.final_layer_norm(UpperCAmelCase )
__snake_case : str = self.proj_out(UpperCAmelCase )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class _lowerCamelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
super().__init__()
__snake_case : List[Any] = (config.num_hidden_layers + 1) // 5
__snake_case : Dict = config.hidden_size
__snake_case : str = 1
__snake_case : List[Any] = nn.ModuleList(
[
BasicTransformerBlock(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , activation_fn="gelu" , attention_bias=UpperCAmelCase )
for _ in range(UpperCAmelCase )
] )
def UpperCAmelCase ( self , UpperCAmelCase ) -> str:
'''simple docstring'''
for block in self.blocks:
__snake_case : int = block(UpperCAmelCase )
return hidden_states
| 326
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
),
}
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCAmelCase_ = "longformer"
def __init__(self , UpperCAmelCase = 512 , UpperCAmelCase = 2 , UpperCAmelCase = 1 , UpperCAmelCase = 0 , UpperCAmelCase = 2 , UpperCAmelCase = 30522 , UpperCAmelCase = 768 , UpperCAmelCase = 12 , UpperCAmelCase = 12 , UpperCAmelCase = 3072 , UpperCAmelCase = "gelu" , UpperCAmelCase = 0.1 , UpperCAmelCase = 0.1 , UpperCAmelCase = 512 , UpperCAmelCase = 2 , UpperCAmelCase = 0.02 , UpperCAmelCase = 1e-1_2 , UpperCAmelCase = False , **UpperCAmelCase , ) -> Union[str, Any]:
super().__init__(pad_token_id=UpperCAmelCase , **UpperCAmelCase )
_snake_case = attention_window
_snake_case = sep_token_id
_snake_case = bos_token_id
_snake_case = eos_token_id
_snake_case = vocab_size
_snake_case = hidden_size
_snake_case = num_hidden_layers
_snake_case = num_attention_heads
_snake_case = hidden_act
_snake_case = intermediate_size
_snake_case = hidden_dropout_prob
_snake_case = attention_probs_dropout_prob
_snake_case = max_position_embeddings
_snake_case = type_vocab_size
_snake_case = initializer_range
_snake_case = layer_norm_eps
_snake_case = onnx_export
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase = "default" , UpperCAmelCase = None ) -> Union[str, Any]:
super().__init__(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
_snake_case = True
@property
def lowercase (self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_snake_case = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_snake_case = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""global_attention_mask""", dynamic_axis),
] )
@property
def lowercase (self ) -> Mapping[str, Mapping[int, str]]:
_snake_case = super().outputs
if self.task == "default":
_snake_case = {0: """batch"""}
return outputs
@property
def lowercase (self ) -> float:
return 1e-4
@property
def lowercase (self ) -> int:
# needs to be >= 14 to support tril operator
return max(super().default_onnx_opset , 14 )
def lowercase (self , UpperCAmelCase , UpperCAmelCase = -1 , UpperCAmelCase = -1 , UpperCAmelCase = False , UpperCAmelCase = None , ) -> Mapping[str, Any]:
_snake_case = super().generate_dummy_inputs(
preprocessor=UpperCAmelCase , batch_size=UpperCAmelCase , seq_length=UpperCAmelCase , is_pair=UpperCAmelCase , framework=UpperCAmelCase )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
_snake_case = torch.zeros_like(inputs["""input_ids"""] )
# make every second token global
_snake_case = 1
return inputs
| 358
|
'''simple docstring'''
import qiskit
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
_snake_case = qiskit.QuantumCircuit(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0 )
circuit.x(1 )
# Map the quantum measurement to the classical bits
circuit.measure([0, 1] , [0, 1] )
# Execute the circuit on the qasm simulator
_snake_case = qiskit.execute(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowerCAmelCase = single_qubit_measure(2, 2)
print(f'''Total count for various states are: {counts}''')
| 270
| 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 UpperCAmelCase_ ( unittest.TestCase ):
@parameterized.expand([(None,), ('foo.json',)] )
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : Optional[Any] ) -> Tuple:
lowerCAmelCase = GenerationConfig(
do_sample=UpperCAmelCase__ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCAmelCase__ , config_name=UpperCAmelCase__ )
lowerCAmelCase = GenerationConfig.from_pretrained(UpperCAmelCase__ , config_name=UpperCAmelCase__ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , UpperCAmelCase__ )
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 , 5_0 )
self.assertEqual(loaded_config.max_length , 2_0 )
self.assertEqual(loaded_config.max_time , UpperCAmelCase__ )
def __UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
lowerCAmelCase = AutoConfig.from_pretrained('gpt2' )
lowerCAmelCase = GenerationConfig.from_model_config(UpperCAmelCase__ )
lowerCAmelCase = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# 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 __UpperCAmelCase ( self : str ) -> Dict:
lowerCAmelCase = GenerationConfig()
lowerCAmelCase = {
'max_new_tokens': 1_0_2_4,
'foo': 'bar',
}
lowerCAmelCase = copy.deepcopy(UpperCAmelCase__ )
lowerCAmelCase = generation_config.update(**UpperCAmelCase__ )
# update_kwargs was not modified (no side effects)
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1_0_2_4 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(UpperCAmelCase__ , {'foo': 'bar'} )
def __UpperCAmelCase ( self : int ) -> Any:
lowerCAmelCase = GenerationConfig()
lowerCAmelCase = 'bar'
with tempfile.TemporaryDirectory('test-generation-config' ) as tmp_dir:
generation_config.save_pretrained(UpperCAmelCase__ )
lowerCAmelCase = GenerationConfig.from_pretrained(UpperCAmelCase__ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , 'bar' )
lowerCAmelCase = GenerationConfig.from_model_config(UpperCAmelCase__ )
assert not hasattr(UpperCAmelCase__ , 'foo' ) # no new kwargs should be initialized if from config
def __UpperCAmelCase ( self : int ) -> int:
lowerCAmelCase = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , UpperCAmelCase__ )
self.assertEqual(default_config.num_beams , 1 )
lowerCAmelCase = GenerationConfig(
do_sample=UpperCAmelCase__ , 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 , UpperCAmelCase__ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCAmelCase__ )
lowerCAmelCase = GenerationConfig.from_pretrained(UpperCAmelCase__ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , UpperCAmelCase__ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class UpperCAmelCase_ ( unittest.TestCase ):
@classmethod
def __UpperCAmelCase ( cls : Dict ) -> List[str]:
lowerCAmelCase = TOKEN
HfFolder.save_token(UpperCAmelCase__ )
@classmethod
def __UpperCAmelCase ( cls : str ) -> str:
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 __UpperCAmelCase ( self : int ) -> List[Any]:
lowerCAmelCase = GenerationConfig(
do_sample=UpperCAmelCase__ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('test-generation-config' , use_auth_token=self._token )
lowerCAmelCase = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase__ , getattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
# 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(
UpperCAmelCase__ , repo_id='test-generation-config' , push_to_hub=UpperCAmelCase__ , use_auth_token=self._token )
lowerCAmelCase = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase__ , getattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
def __UpperCAmelCase ( self : Dict ) -> Optional[int]:
lowerCAmelCase = GenerationConfig(
do_sample=UpperCAmelCase__ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('valid_org/test-generation-config-org' , use_auth_token=self._token )
lowerCAmelCase = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase__ , getattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
# 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(
UpperCAmelCase__ , repo_id='valid_org/test-generation-config-org' , push_to_hub=UpperCAmelCase__ , use_auth_token=self._token )
lowerCAmelCase = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase__ , getattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
| 4
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
def __UpperCAmelCase ( self : Optional[int] ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self : Tuple ) -> Any:
lowerCAmelCase = StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' )
lowerCAmelCase = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
sd_pipe.set_scheduler('sample_euler' )
lowerCAmelCase = 'A painting of a squirrel eating a burger'
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = sd_pipe([prompt] , generator=UpperCAmelCase__ , guidance_scale=9.0 , num_inference_steps=2_0 , output_type='np' )
lowerCAmelCase = output.images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
lowerCAmelCase = np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __UpperCAmelCase ( self : List[str] ) -> Dict:
lowerCAmelCase = StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
lowerCAmelCase = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
sd_pipe.set_scheduler('sample_euler' )
lowerCAmelCase = 'A painting of a squirrel eating a burger'
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = sd_pipe([prompt] , generator=UpperCAmelCase__ , guidance_scale=9.0 , num_inference_steps=2_0 , output_type='np' )
lowerCAmelCase = output.images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
lowerCAmelCase = np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1
def __UpperCAmelCase ( self : Optional[Any] ) -> List[str]:
lowerCAmelCase = StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
lowerCAmelCase = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
sd_pipe.set_scheduler('sample_dpmpp_2m' )
lowerCAmelCase = 'A painting of a squirrel eating a burger'
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = sd_pipe(
[prompt] , generator=UpperCAmelCase__ , guidance_scale=7.5 , num_inference_steps=1_5 , output_type='np' , use_karras_sigmas=UpperCAmelCase__ , )
lowerCAmelCase = output.images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
lowerCAmelCase = np.array(
[0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 4
| 1
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase ( self ):
lowercase_ :Dict = XLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
lowercase_ :int = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]] )
# The dog is cute and lives in the garden house
lowercase_ :Any = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
lowercase_ :str = torch.tensor(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
lowercase_ :Optional[Any] = model(UpperCamelCase_ )['''last_hidden_state'''].detach()
self.assertEqual(output.shape , UpperCamelCase_ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , UpperCamelCase_ , atol=1E-3 ) )
@slow
def UpperCamelCase ( self ):
lowercase_ :List[str] = XLMRobertaModel.from_pretrained('''xlm-roberta-large''' )
lowercase_ :str = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]] )
# The dog is cute and lives in the garden house
lowercase_ :Union[str, Any] = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim
lowercase_ :Optional[int] = torch.tensor(
[[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
lowercase_ :str = model(UpperCamelCase_ )['''last_hidden_state'''].detach()
self.assertEqual(output.shape , UpperCamelCase_ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , UpperCamelCase_ , atol=1E-3 ) )
| 252
|
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
SCREAMING_SNAKE_CASE : Dict = [
"python",
"tqdm",
"regex",
"requests",
"packaging",
"filelock",
"numpy",
"tokenizers",
"huggingface-hub",
"safetensors",
"accelerate",
"pyyaml",
]
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
elif pkg == "accelerate":
# must be loaded here, or else tqdm check may fail
from .utils import is_accelerate_available
# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
# Transformers with PyTorch
if not is_accelerate_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
def UpperCamelCase ( _a , _a=None ) -> Optional[int]:
'''simple docstring'''
require_version(deps[pkg] , _a )
| 252
| 1
|
from maths.prime_factors import prime_factors
def A ( _lowerCamelCase ):
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : int = F"Input value of [number={number}] must be an integer"
raise TypeError(_lowerCamelCase )
if number < 1:
raise ValueError("Input must be a positive integer" )
return -1 if len(prime_factors(_lowerCamelCase ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36
|
A_ : List[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A_ : int = ['a', 'b', 'c', 'd', 'e']
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase = start
# add current to visited
visited.append(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
__UpperCAmelCase = topological_sort(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# if all neighbors visited add current to sort
sort.append(SCREAMING_SNAKE_CASE )
# if all vertices haven't been visited select a new one to visit
if len(SCREAMING_SNAKE_CASE ) != len(SCREAMING_SNAKE_CASE ):
for vertice in vertices:
if vertice not in visited:
__UpperCAmelCase = topological_sort(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# return sort
return sort
if __name__ == "__main__":
A_ : Tuple = topological_sort('a', [], [])
print(sort)
| 333
| 0
|
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowerCamelCase ( __lowerCAmelCase : int ) -> str:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("""Undefined for non-integers""" )
elif precision < 1:
raise ValueError("""Undefined for non-natural numbers""" )
snake_case = precision
snake_case = ceil(precision / 14 )
snake_case = 42_68_80 * Decimal(1_00_05 ).sqrt()
snake_case = 1
snake_case = 13_59_14_09
snake_case = Decimal(__lowerCAmelCase )
for k in range(1 , __lowerCAmelCase ):
snake_case = factorial(6 * k ) // (factorial(3 * k ) * factorial(__lowerCAmelCase ) ** 3)
linear_term += 5_45_14_01_34
exponential_term *= -26_25_37_41_26_40_76_80_00
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = 50
print(F"""The first {n} digits of pi is: {pi(n)}""")
| 359
|
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
_SCREAMING_SNAKE_CASE = get_tests_dir("fixtures/test_sentencepiece.model")
_SCREAMING_SNAKE_CASE = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
_SCREAMING_SNAKE_CASE = "pt" if is_torch_available() else "tf"
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( A__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = CamembertTokenizer
snake_case_ = CamembertTokenizerFast
snake_case_ = True
snake_case_ = True
def lowerCAmelCase ( self : Union[str, Any] )-> List[Any]:
super().setUp()
# We have a SentencePiece fixture for testing
snake_case = CamembertTokenizer(__snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCAmelCase ( self : Tuple )-> List[Any]:
snake_case = """<pad>"""
snake_case = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case ) , __snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case ) , __snake_case )
def lowerCAmelCase ( self : Dict )-> Optional[Any]:
snake_case = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>NOTUSED""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(__snake_case ) , 10_04 )
def lowerCAmelCase ( self : List[str] )-> Any:
self.assertEqual(self.get_tokenizer().vocab_size , 10_05 )
def lowerCAmelCase ( self : List[str] )-> List[str]:
snake_case = CamembertTokenizer(__snake_case )
tokenizer.save_pretrained(self.tmpdirname )
snake_case = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
snake_case = """I was born in 92000, and this is falsé."""
snake_case = tokenizer.encode(__snake_case )
snake_case = rust_tokenizer.encode(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
snake_case = tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
snake_case = rust_tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
self.assertListEqual(__snake_case , __snake_case )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
snake_case = tokenizer.convert_ids_to_tokens(__snake_case )
snake_case = rust_tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
def lowerCAmelCase ( self : str )-> Any:
if not self.test_rust_tokenizer:
return
snake_case = self.get_tokenizer()
snake_case = self.get_rust_tokenizer()
snake_case = """I was born in 92000, and this is falsé."""
snake_case = tokenizer.tokenize(__snake_case )
snake_case = rust_tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
snake_case = tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
snake_case = rust_tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
self.assertListEqual(__snake_case , __snake_case )
snake_case = self.get_rust_tokenizer()
snake_case = tokenizer.encode(__snake_case )
snake_case = rust_tokenizer.encode(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
@slow
def lowerCAmelCase ( self : Any )-> Optional[int]:
# fmt: off
snake_case = {"""input_ids""": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
snake_case = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=__snake_case , model_name="""camembert-base""" , revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf""" , sequences=__snake_case , )
| 3
| 0
|
'''simple docstring'''
from math import ceil
def snake_case ( UpperCAmelCase , UpperCAmelCase )-> str:
"""simple docstring"""
__A = list(range(0 , UpperCAmelCase ) )
__A = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
__A = []
for i in device_map_blocks:
if device_map_blocks.count(UpperCAmelCase ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(UpperCAmelCase )
# Missing blocks
__A = [i for i in blocks if i not in device_map_blocks]
__A = [i for i in device_map_blocks if i not in blocks]
if len(UpperCAmelCase ) != 0:
raise ValueError(
'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
' These attention blocks were specified more than once: ' + str(UpperCAmelCase ) )
if len(UpperCAmelCase ) != 0:
raise ValueError(
'There are attention blocks for this model that are not specified in the device_map. Add these attention '
'blocks to a device on the device_map: ' + str(UpperCAmelCase ) )
if len(UpperCAmelCase ) != 0:
raise ValueError(
'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
+ str(UpperCAmelCase ) )
def snake_case ( UpperCAmelCase , UpperCAmelCase )-> Union[str, Any]:
"""simple docstring"""
__A = list(range(UpperCAmelCase ) )
__A = int(ceil(n_layers / len(UpperCAmelCase ) ) )
__A = [layers[i : i + n_blocks] for i in range(0 , UpperCAmelCase , UpperCAmelCase )]
return dict(zip(UpperCAmelCase , UpperCAmelCase ) )
| 161
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : List[Any] = logging.get_logger(__name__)
a__ : Union[str, Any] = {
"Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE):
UpperCAmelCase__ : List[str] = 'instructblip_vision_model'
def __init__( self :List[str] , _A :str=1_408 , _A :List[str]=6_144 , _A :List[Any]=39 , _A :Optional[Any]=16 , _A :Tuple=224 , _A :Tuple=14 , _A :Tuple="gelu" , _A :Optional[Any]=1E-6 , _A :List[Any]=0.0 , _A :Dict=1E-10 , _A :List[str]=True , **_A :Dict , ) -> Dict:
'''simple docstring'''
super().__init__(**_A )
__A = hidden_size
__A = intermediate_size
__A = num_hidden_layers
__A = num_attention_heads
__A = patch_size
__A = image_size
__A = initializer_range
__A = attention_dropout
__A = layer_norm_eps
__A = hidden_act
__A = qkv_bias
@classmethod
def lowercase_ ( cls :Any , _A :Union[str, os.PathLike] , **_A :Tuple ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(_A )
__A , __A = cls.get_config_dict(_A , **_A )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type' ) == "instructblip":
__A = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_A , **_A )
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE):
UpperCAmelCase__ : List[str] = 'instructblip_qformer'
def __init__( self :Tuple , _A :int=30_522 , _A :List[str]=768 , _A :str=12 , _A :Optional[Any]=12 , _A :Union[str, Any]=3_072 , _A :str="gelu" , _A :Tuple=0.1 , _A :Dict=0.1 , _A :Dict=512 , _A :Union[str, Any]=0.02 , _A :int=1E-12 , _A :str=0 , _A :Union[str, Any]="absolute" , _A :List[str]=2 , _A :Optional[Any]=1_408 , **_A :Any , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(pad_token_id=_A , **_A )
__A = vocab_size
__A = hidden_size
__A = num_hidden_layers
__A = num_attention_heads
__A = hidden_act
__A = intermediate_size
__A = hidden_dropout_prob
__A = attention_probs_dropout_prob
__A = max_position_embeddings
__A = initializer_range
__A = layer_norm_eps
__A = position_embedding_type
__A = cross_attention_frequency
__A = encoder_hidden_size
@classmethod
def lowercase_ ( cls :int , _A :Union[str, os.PathLike] , **_A :int ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(_A )
__A , __A = cls.get_config_dict(_A , **_A )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type' ) == "instructblip":
__A = config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_A , **_A )
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE):
UpperCAmelCase__ : Any = 'instructblip'
UpperCAmelCase__ : List[Any] = True
def __init__( self :Dict , _A :int=None , _A :Optional[Any]=None , _A :Optional[Any]=None , _A :Optional[Any]=32 , **_A :List[Any] ) -> Tuple:
'''simple docstring'''
super().__init__(**_A )
if vision_config is None:
__A = {}
logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.' )
if qformer_config is None:
__A = {}
logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.' )
if text_config is None:
__A = {}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
__A = InstructBlipVisionConfig(**_A )
__A = InstructBlipQFormerConfig(**_A )
__A = text_config['model_type'] if 'model_type' in text_config else 'opt'
__A = CONFIG_MAPPING[text_model_type](**_A )
__A = self.text_config.tie_word_embeddings
__A = self.text_config.is_encoder_decoder
__A = num_query_tokens
__A = self.vision_config.hidden_size
__A = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__A = 1.0
__A = 0.02
@classmethod
def lowercase_ ( cls :int , _A :InstructBlipVisionConfig , _A :InstructBlipQFormerConfig , _A :PretrainedConfig , **_A :Any , ) -> Any:
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **_A , )
def lowercase_ ( self :int ) -> Tuple:
'''simple docstring'''
__A = copy.deepcopy(self.__dict__ )
__A = self.vision_config.to_dict()
__A = self.qformer_config.to_dict()
__A = self.text_config.to_dict()
__A = self.__class__.model_type
return output
| 161
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE_ = {
"""configuration_owlvit""": [
"""OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""OwlViTConfig""",
"""OwlViTOnnxConfig""",
"""OwlViTTextConfig""",
"""OwlViTVisionConfig""",
],
"""processing_owlvit""": ["""OwlViTProcessor"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""OwlViTFeatureExtractor"""]
SCREAMING_SNAKE_CASE_ = ["""OwlViTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""OwlViTModel""",
"""OwlViTPreTrainedModel""",
"""OwlViTTextModel""",
"""OwlViTVisionModel""",
"""OwlViTForObjectDetection""",
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 360
|
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
SCREAMING_SNAKE_CASE_ = """\
Text data.
Second line of data."""
SCREAMING_SNAKE_CASE_ = """file"""
@pytest.fixture(scope="""session""" )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / (FILE_PATH + """.zstd""")
SCREAMING_SNAKE_CASE = bytes(_SCREAMING_SNAKE_CASE , """utf-8""" )
with zstd.open(_SCREAMING_SNAKE_CASE , """wb""" ) as f:
f.write(_SCREAMING_SNAKE_CASE )
return path
@pytest.fixture
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _SCREAMING_SNAKE_CASE ) , """w""" ) as f:
f.write(_SCREAMING_SNAKE_CASE )
return FILE_PATH
@pytest.mark.parametrize("""compression_format""" , ["""gzip""", """xz""", """zstd"""] )
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
SCREAMING_SNAKE_CASE = input_paths[compression_format]
SCREAMING_SNAKE_CASE = tmp_path / """cache"""
SCREAMING_SNAKE_CASE = DownloadConfig(cache_dir=_SCREAMING_SNAKE_CASE , extract_compressed_file=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = cached_path(_SCREAMING_SNAKE_CASE , download_config=_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE ) as f:
SCREAMING_SNAKE_CASE = f.read()
with open(_SCREAMING_SNAKE_CASE ) as f:
SCREAMING_SNAKE_CASE = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("""default_extracted""" , [True, False] )
@pytest.mark.parametrize("""default_cache_dir""" , [True, False] )
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """custom_cache"""
SCREAMING_SNAKE_CASE = """custom_extracted_dir"""
SCREAMING_SNAKE_CASE = tmp_path / """custom_extracted_path"""
if default_extracted:
SCREAMING_SNAKE_CASE = ("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
else:
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR""" , _SCREAMING_SNAKE_CASE )
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH""" , str(_SCREAMING_SNAKE_CASE ) )
SCREAMING_SNAKE_CASE = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
SCREAMING_SNAKE_CASE = xz_file
SCREAMING_SNAKE_CASE = (
DownloadConfig(extract_compressed_file=_SCREAMING_SNAKE_CASE )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_SCREAMING_SNAKE_CASE )
)
SCREAMING_SNAKE_CASE = cached_path(_SCREAMING_SNAKE_CASE , download_config=_SCREAMING_SNAKE_CASE )
assert Path(_SCREAMING_SNAKE_CASE ).parent.parts[-2:] == expected
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = str(Path(_SCREAMING_SNAKE_CASE ).resolve() )
assert cached_path(_SCREAMING_SNAKE_CASE ) == text_file
# relative path
SCREAMING_SNAKE_CASE = str(Path(_SCREAMING_SNAKE_CASE ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_SCREAMING_SNAKE_CASE ) == text_file
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = str(tmp_path.resolve() / """__missing_file__.txt""" )
with pytest.raises(_SCREAMING_SNAKE_CASE ):
cached_path(_SCREAMING_SNAKE_CASE )
# relative path
SCREAMING_SNAKE_CASE = """./__missing_file__.txt"""
with pytest.raises(_SCREAMING_SNAKE_CASE ):
cached_path(_SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_from_cache(F"""tmp://{tmpfs_file}""" )
with open(_SCREAMING_SNAKE_CASE ) as f:
SCREAMING_SNAKE_CASE = f.read()
assert output_file_content == FILE_CONTENT
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , _SCREAMING_SNAKE_CASE )
def __lowercase ( ) -> Dict:
'''simple docstring'''
with pytest.raises(_SCREAMING_SNAKE_CASE ):
cached_path("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , _SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(_SCREAMING_SNAKE_CASE ):
http_get("""https://huggingface.co""" , temp_file=_SCREAMING_SNAKE_CASE )
with pytest.raises(_SCREAMING_SNAKE_CASE ):
http_head("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , _SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(_SCREAMING_SNAKE_CASE ):
ftp_get("""ftp://huggingface.co""" , temp_file=_SCREAMING_SNAKE_CASE )
with pytest.raises(_SCREAMING_SNAKE_CASE ):
ftp_head("""ftp://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , _SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(_SCREAMING_SNAKE_CASE ):
fsspec_get("""s3://huggingface.co""" , temp_file=_SCREAMING_SNAKE_CASE )
with pytest.raises(_SCREAMING_SNAKE_CASE ):
fsspec_head("""s3://huggingface.co""" )
| 193
| 0
|
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __UpperCAmelCase (__lowercase ,unittest.TestCase ):
__snake_case : str = DiTPipeline
__snake_case : List[Any] = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__snake_case : str = PipelineTesterMixin.required_optional_params - {
"""latents""",
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
__snake_case : List[str] = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__snake_case : int = False
def UpperCamelCase ( self: Any ):
'''simple docstring'''
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=SCREAMING_SNAKE_CASE__ , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=1_000 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=SCREAMING_SNAKE_CASE__ , )
_SCREAMING_SNAKE_CASE = AutoencoderKL()
_SCREAMING_SNAKE_CASE = DDIMScheduler()
_SCREAMING_SNAKE_CASE = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def UpperCamelCase ( self: Any , UpperCAmelCase_: str , UpperCAmelCase_: Any=0 ):
'''simple docstring'''
if str(SCREAMING_SNAKE_CASE__ ).startswith("""mps""" ):
_SCREAMING_SNAKE_CASE = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
_SCREAMING_SNAKE_CASE = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
_SCREAMING_SNAKE_CASE = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def UpperCamelCase ( self: Tuple ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = """cpu"""
_SCREAMING_SNAKE_CASE = self.get_dummy_components()
_SCREAMING_SNAKE_CASE = self.pipeline_class(**SCREAMING_SNAKE_CASE__ )
pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
_SCREAMING_SNAKE_CASE = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
_SCREAMING_SNAKE_CASE = pipe(**SCREAMING_SNAKE_CASE__ ).images
_SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_SCREAMING_SNAKE_CASE = np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57] )
_SCREAMING_SNAKE_CASE = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE__ , 1E-3 )
def UpperCamelCase ( self: Tuple ):
'''simple docstring'''
self._test_inference_batch_single_identical(relax_max_difference=SCREAMING_SNAKE_CASE__ , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def UpperCamelCase ( self: Any ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class __UpperCAmelCase (unittest.TestCase ):
def UpperCamelCase ( self: Union[str, Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self: Dict ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_SCREAMING_SNAKE_CASE = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_SCREAMING_SNAKE_CASE = pipe.get_label_ids(SCREAMING_SNAKE_CASE__ )
_SCREAMING_SNAKE_CASE = pipe(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
_SCREAMING_SNAKE_CASE = load_numpy(
F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def UpperCamelCase ( self: Dict ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_SCREAMING_SNAKE_CASE = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_SCREAMING_SNAKE_CASE = ["""vase""", """umbrella"""]
_SCREAMING_SNAKE_CASE = pipe.get_label_ids(SCREAMING_SNAKE_CASE__ )
_SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = pipe(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
_SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
F'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 306
|
from queue import PriorityQueue
from typing import Any
import numpy as np
def __magic_name__ ( __lowerCAmelCase : dict , __lowerCAmelCase : str , __lowerCAmelCase : set , __lowerCAmelCase : set , __lowerCAmelCase : dict , __lowerCAmelCase : dict , __lowerCAmelCase : PriorityQueue , __lowerCAmelCase : dict , __lowerCAmelCase : float | int , ) -> float | int:
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
__lowerCamelCase = cst_fwd.get(__lowerCAmelCase , np.inf )
__lowerCamelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
__lowerCamelCase = new_cost_f
__lowerCamelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
__lowerCamelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def __magic_name__ ( __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : dict , __lowerCAmelCase : dict ) -> int:
__lowerCamelCase = -1
__lowerCamelCase = set()
__lowerCamelCase = set()
__lowerCamelCase = {source: 0}
__lowerCamelCase = {destination: 0}
__lowerCamelCase = {source: None}
__lowerCamelCase = {destination: None}
__lowerCamelCase = PriorityQueue()
__lowerCamelCase = PriorityQueue()
__lowerCamelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
__lowerCamelCase , __lowerCamelCase = queue_forward.get()
visited_forward.add(__lowerCAmelCase )
__lowerCamelCase , __lowerCamelCase = queue_backward.get()
visited_backward.add(__lowerCAmelCase )
__lowerCamelCase = pass_and_relaxation(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )
__lowerCamelCase = pass_and_relaxation(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
__lowerCamelCase = shortest_distance
return shortest_path_distance
SCREAMING_SNAKE_CASE__ : List[Any] = {
"B": [["C", 1]],
"C": [["D", 1]],
"D": [["F", 1]],
"E": [["B", 1], ["G", 2]],
"F": [],
"G": [["F", 1]],
}
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"B": [["E", 1]],
"C": [["B", 1]],
"D": [["C", 1]],
"F": [["D", 1], ["G", 1]],
"E": [[None, np.inf]],
"G": [["E", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 270
| 0
|
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( _a ):
"""simple docstring"""
lowerCAmelCase = 42
class __a( _a , _a ):
"""simple docstring"""
lowerCAmelCase = True
@register_to_config
def __init__( self ,_SCREAMING_SNAKE_CASE = 3 ,_SCREAMING_SNAKE_CASE = 3 ,_SCREAMING_SNAKE_CASE = ("DownEncoderBlock2D",) ,_SCREAMING_SNAKE_CASE = ("UpDecoderBlock2D",) ,_SCREAMING_SNAKE_CASE = (64,) ,_SCREAMING_SNAKE_CASE = 1 ,_SCREAMING_SNAKE_CASE = "silu" ,_SCREAMING_SNAKE_CASE = 4 ,_SCREAMING_SNAKE_CASE = 32 ,_SCREAMING_SNAKE_CASE = 32 ,_SCREAMING_SNAKE_CASE = 0.1_82_15 ,) -> Optional[int]:
super().__init__()
# pass init params to Encoder
UpperCAmelCase_ : List[Any] = Encoder(
in_channels=_SCREAMING_SNAKE_CASE ,out_channels=_SCREAMING_SNAKE_CASE ,down_block_types=_SCREAMING_SNAKE_CASE ,block_out_channels=_SCREAMING_SNAKE_CASE ,layers_per_block=_SCREAMING_SNAKE_CASE ,act_fn=_SCREAMING_SNAKE_CASE ,norm_num_groups=_SCREAMING_SNAKE_CASE ,double_z=_SCREAMING_SNAKE_CASE ,)
# pass init params to Decoder
UpperCAmelCase_ : List[str] = Decoder(
in_channels=_SCREAMING_SNAKE_CASE ,out_channels=_SCREAMING_SNAKE_CASE ,up_block_types=_SCREAMING_SNAKE_CASE ,block_out_channels=_SCREAMING_SNAKE_CASE ,layers_per_block=_SCREAMING_SNAKE_CASE ,norm_num_groups=_SCREAMING_SNAKE_CASE ,act_fn=_SCREAMING_SNAKE_CASE ,)
UpperCAmelCase_ : int = nn.Convad(2 * latent_channels ,2 * latent_channels ,1 )
UpperCAmelCase_ : Union[str, Any] = nn.Convad(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,1 )
UpperCAmelCase_ : Optional[Any] = False
UpperCAmelCase_ : Dict = False
# only relevant if vae tiling is enabled
UpperCAmelCase_ : List[Any] = self.config.sample_size
UpperCAmelCase_ : List[str] = (
self.config.sample_size[0]
if isinstance(self.config.sample_size ,(list, tuple) )
else self.config.sample_size
)
UpperCAmelCase_ : List[str] = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
UpperCAmelCase_ : int = 0.25
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False ) -> int:
if isinstance(_SCREAMING_SNAKE_CASE ,(Encoder, Decoder) ):
UpperCAmelCase_ : Union[str, Any] = value
def a__ ( self ,_SCREAMING_SNAKE_CASE = True ) -> Optional[Any]:
UpperCAmelCase_ : Dict = use_tiling
def a__ ( self ) -> Optional[Any]:
self.enable_tiling(_SCREAMING_SNAKE_CASE )
def a__ ( self ) -> List[str]:
UpperCAmelCase_ : str = True
def a__ ( self ) -> Any:
UpperCAmelCase_ : Any = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def a__ ( self ) -> Dict[str, AttentionProcessor]:
UpperCAmelCase_ : int = {}
def fn_recursive_add_processors(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
if hasattr(_SCREAMING_SNAKE_CASE ,'''set_processor''' ):
UpperCAmelCase_ : Tuple = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f'''{name}.{sub_name}''' ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
return processors
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
UpperCAmelCase_ : Optional[int] = len(self.attn_processors.keys() )
if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) and len(_SCREAMING_SNAKE_CASE ) != count:
raise ValueError(
f'''A dict of processors was passed, but the number of processors {len(_SCREAMING_SNAKE_CASE )} does not match the'''
f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''' )
def fn_recursive_attn_processor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
if hasattr(_SCREAMING_SNAKE_CASE ,'''set_processor''' ):
if not isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
module.set_processor(_SCREAMING_SNAKE_CASE )
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}''' ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
for name, module in self.named_children():
fn_recursive_attn_processor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
def a__ ( self ) -> Tuple:
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = True ) -> AutoencoderKLOutput:
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(_SCREAMING_SNAKE_CASE ,return_dict=_SCREAMING_SNAKE_CASE )
if self.use_slicing and x.shape[0] > 1:
UpperCAmelCase_ : int = [self.encoder(_SCREAMING_SNAKE_CASE ) for x_slice in x.split(1 )]
UpperCAmelCase_ : List[str] = torch.cat(_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase_ : Optional[Any] = self.encoder(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Any = self.quant_conv(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Union[str, Any] = DiagonalGaussianDistribution(_SCREAMING_SNAKE_CASE )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = True ) -> Union[DecoderOutput, torch.FloatTensor]:
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(_SCREAMING_SNAKE_CASE ,return_dict=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Tuple = self.post_quant_conv(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Tuple = self.decoder(_SCREAMING_SNAKE_CASE )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_SCREAMING_SNAKE_CASE )
@apply_forward_hook
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = True ) -> Union[DecoderOutput, torch.FloatTensor]:
if self.use_slicing and z.shape[0] > 1:
UpperCAmelCase_ : Tuple = [self._decode(_SCREAMING_SNAKE_CASE ).sample for z_slice in z.split(1 )]
UpperCAmelCase_ : str = torch.cat(_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase_ : str = self._decode(_SCREAMING_SNAKE_CASE ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
UpperCAmelCase_ : str = min(a.shape[2] ,b.shape[2] ,_SCREAMING_SNAKE_CASE )
for y in range(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : str = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ : Any = min(a.shape[3] ,b.shape[3] ,_SCREAMING_SNAKE_CASE )
for x in range(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : List[str] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = True ) -> AutoencoderKLOutput:
UpperCAmelCase_ : Optional[int] = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
UpperCAmelCase_ : Any = int(self.tile_latent_min_size * self.tile_overlap_factor )
UpperCAmelCase_ : str = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
UpperCAmelCase_ : Tuple = []
for i in range(0 ,x.shape[2] ,_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : List[str] = []
for j in range(0 ,x.shape[3] ,_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : List[str] = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
UpperCAmelCase_ : int = self.encoder(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : str = self.quant_conv(_SCREAMING_SNAKE_CASE )
row.append(_SCREAMING_SNAKE_CASE )
rows.append(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Union[str, Any] = []
for i, row in enumerate(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : str = []
for j, tile in enumerate(_SCREAMING_SNAKE_CASE ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
UpperCAmelCase_ : Optional[Any] = self.blend_v(rows[i - 1][j] ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
if j > 0:
UpperCAmelCase_ : List[str] = self.blend_h(row[j - 1] ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(_SCREAMING_SNAKE_CASE ,dim=3 ) )
UpperCAmelCase_ : Optional[Any] = torch.cat(_SCREAMING_SNAKE_CASE ,dim=2 )
UpperCAmelCase_ : Union[str, Any] = DiagonalGaussianDistribution(_SCREAMING_SNAKE_CASE )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = True ) -> Union[DecoderOutput, torch.FloatTensor]:
UpperCAmelCase_ : Optional[Any] = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
UpperCAmelCase_ : str = int(self.tile_sample_min_size * self.tile_overlap_factor )
UpperCAmelCase_ : Any = 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.
UpperCAmelCase_ : Tuple = []
for i in range(0 ,z.shape[2] ,_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : Tuple = []
for j in range(0 ,z.shape[3] ,_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : Optional[int] = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
UpperCAmelCase_ : List[str] = self.post_quant_conv(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Dict = self.decoder(_SCREAMING_SNAKE_CASE )
row.append(_SCREAMING_SNAKE_CASE )
rows.append(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Optional[Any] = []
for i, row in enumerate(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : int = []
for j, tile in enumerate(_SCREAMING_SNAKE_CASE ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
UpperCAmelCase_ : Optional[int] = self.blend_v(rows[i - 1][j] ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
if j > 0:
UpperCAmelCase_ : Optional[int] = self.blend_h(row[j - 1] ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(_SCREAMING_SNAKE_CASE ,dim=3 ) )
UpperCAmelCase_ : Dict = torch.cat(_SCREAMING_SNAKE_CASE ,dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,) -> Union[DecoderOutput, torch.FloatTensor]:
UpperCAmelCase_ : str = sample
UpperCAmelCase_ : Optional[Any] = self.encode(_SCREAMING_SNAKE_CASE ).latent_dist
if sample_posterior:
UpperCAmelCase_ : List[Any] = posterior.sample(generator=_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase_ : Union[str, Any] = posterior.mode()
UpperCAmelCase_ : List[Any] = self.decode(_SCREAMING_SNAKE_CASE ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_SCREAMING_SNAKE_CASE )
| 235
|
from typing import Dict, List, Optional, 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,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__a = logging.get_logger(__name__)
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if isinstance(_lowercase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(_lowercase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(_lowercase ):
return [[videos]]
raise ValueError(f'''Could not make batched video from {videos}''' )
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = ['''pixel_values''']
def __init__( self ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = 1 / 255 ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,) -> None:
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : int = size if size is not None else {'''shortest_edge''': 224}
UpperCAmelCase_ : Any = get_size_dict(_SCREAMING_SNAKE_CASE ,default_to_square=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Any = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
UpperCAmelCase_ : List[str] = get_size_dict(_SCREAMING_SNAKE_CASE ,param_name='''crop_size''' )
UpperCAmelCase_ : str = do_resize
UpperCAmelCase_ : Union[str, Any] = size
UpperCAmelCase_ : int = do_center_crop
UpperCAmelCase_ : List[str] = crop_size
UpperCAmelCase_ : Optional[int] = resample
UpperCAmelCase_ : List[Any] = do_rescale
UpperCAmelCase_ : Tuple = rescale_factor
UpperCAmelCase_ : Optional[Any] = do_normalize
UpperCAmelCase_ : int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase_ : Optional[int] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,) -> np.ndarray:
UpperCAmelCase_ : Optional[int] = get_size_dict(_SCREAMING_SNAKE_CASE ,default_to_square=_SCREAMING_SNAKE_CASE )
if "shortest_edge" in size:
UpperCAmelCase_ : Dict = get_resize_output_image_size(_SCREAMING_SNAKE_CASE ,size['''shortest_edge'''] ,default_to_square=_SCREAMING_SNAKE_CASE )
elif "height" in size and "width" in size:
UpperCAmelCase_ : Tuple = (size['''height'''], size['''width'''])
else:
raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
return resize(_SCREAMING_SNAKE_CASE ,size=_SCREAMING_SNAKE_CASE ,resample=_SCREAMING_SNAKE_CASE ,data_format=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,) -> np.ndarray:
UpperCAmelCase_ : str = get_size_dict(_SCREAMING_SNAKE_CASE )
if "height" not in size or "width" not in size:
raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
return center_crop(_SCREAMING_SNAKE_CASE ,size=(size['''height'''], size['''width''']) ,data_format=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,) -> Dict:
return rescale(_SCREAMING_SNAKE_CASE ,scale=_SCREAMING_SNAKE_CASE ,data_format=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,) -> np.ndarray:
return normalize(_SCREAMING_SNAKE_CASE ,mean=_SCREAMING_SNAKE_CASE ,std=_SCREAMING_SNAKE_CASE ,data_format=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST ,) -> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample 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_ : Any = to_numpy_array(_SCREAMING_SNAKE_CASE )
if do_resize:
UpperCAmelCase_ : Union[str, Any] = self.resize(image=_SCREAMING_SNAKE_CASE ,size=_SCREAMING_SNAKE_CASE ,resample=_SCREAMING_SNAKE_CASE )
if do_center_crop:
UpperCAmelCase_ : Optional[int] = self.center_crop(_SCREAMING_SNAKE_CASE ,size=_SCREAMING_SNAKE_CASE )
if do_rescale:
UpperCAmelCase_ : str = self.rescale(image=_SCREAMING_SNAKE_CASE ,scale=_SCREAMING_SNAKE_CASE )
if do_normalize:
UpperCAmelCase_ : List[Any] = self.normalize(image=_SCREAMING_SNAKE_CASE ,mean=_SCREAMING_SNAKE_CASE ,std=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Optional[int] = to_channel_dimension_format(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
return image
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST ,**_SCREAMING_SNAKE_CASE ,) -> PIL.Image.Image:
UpperCAmelCase_ : Dict = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase_ : int = resample if resample is not None else self.resample
UpperCAmelCase_ : List[str] = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase_ : Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase_ : List[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase_ : Tuple = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase_ : Optional[int] = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase_ : Optional[int] = image_std if image_std is not None else self.image_std
UpperCAmelCase_ : List[str] = size if size is not None else self.size
UpperCAmelCase_ : Optional[int] = get_size_dict(_SCREAMING_SNAKE_CASE ,default_to_square=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[str] = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase_ : Any = get_size_dict(_SCREAMING_SNAKE_CASE ,param_name='''crop_size''' )
if not valid_images(_SCREAMING_SNAKE_CASE ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
UpperCAmelCase_ : List[Any] = make_batched(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : str = [
[
self._preprocess_image(
image=_SCREAMING_SNAKE_CASE ,do_resize=_SCREAMING_SNAKE_CASE ,size=_SCREAMING_SNAKE_CASE ,resample=_SCREAMING_SNAKE_CASE ,do_center_crop=_SCREAMING_SNAKE_CASE ,crop_size=_SCREAMING_SNAKE_CASE ,do_rescale=_SCREAMING_SNAKE_CASE ,rescale_factor=_SCREAMING_SNAKE_CASE ,do_normalize=_SCREAMING_SNAKE_CASE ,image_mean=_SCREAMING_SNAKE_CASE ,image_std=_SCREAMING_SNAKE_CASE ,data_format=_SCREAMING_SNAKE_CASE ,)
for img in video
]
for video in videos
]
UpperCAmelCase_ : Any = {'''pixel_values''': videos}
return BatchFeature(data=_SCREAMING_SNAKE_CASE ,tensor_type=_SCREAMING_SNAKE_CASE )
| 235
| 1
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.