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from __future__ import annotations
snake_case__ = 8.988e9 # units = N * m^s * C^-2
def lowerCamelCase__ ( a : float , a : float , a : float , a : float ) -> dict[str, float]:
"""simple docstring"""
a__ :Dict = abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if distance < 0:
raise ValueError("Distance cannot be negative" )
if force == 0:
a__ :int = COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
a__ :int = abs(a ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
a__ :List[str] = abs(a ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
a__ :List[Any] = (COULOMBS_CONSTANT * charge_product / abs(a )) ** 0.5
return {"distance": distance}
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 395
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
snake_case__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
snake_case__ = TaTokenizerFast
snake_case__ = {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = [
'''MT5EncoderModel''',
'''MT5ForConditionalGeneration''',
'''MT5ForQuestionAnswering''',
'''MT5Model''',
'''MT5PreTrainedModel''',
'''MT5Stack''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
snake_case__ = _LazyModule(
__name__,
globals()['''__file__'''],
_import_structure,
extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
module_spec=__spec__,
)
| 395
| 1
|
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a__ = """."""
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
a__ = [
"""Assert""",
"""AssignVariableOp""",
"""EmptyTensorList""",
"""MergeV2Checkpoints""",
"""ReadVariableOp""",
"""ResourceGather""",
"""RestoreV2""",
"""SaveV2""",
"""ShardedFilename""",
"""StatefulPartitionedCall""",
"""StaticRegexFullMatch""",
"""VarHandleOp""",
]
def lowercase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Tuple ) -> List[str]:
_snake_case : str = SavedModel()
_snake_case : List[Any] = []
with open(os.path.join(__lowercase , """utils""" , """tf_ops""" , """onnx.json""" ) ) as f:
_snake_case : Any = json.load(__lowercase )['opsets']
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(__lowercase )] )
with open(__lowercase , """rb""" ) as f:
saved_model.ParseFromString(f.read() )
_snake_case : int = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
_snake_case : List[str] = sorted(__lowercase )
_snake_case : Any = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(__lowercase )
if strict and len(__lowercase ) > 0:
raise Exception(F'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops )
elif len(__lowercase ) > 0:
print(F'''Found the following incompatible ops for the opset {opset}:''' )
print(*__lowercase , sep="""\n""" )
else:
print(F'''The saved model {saved_model_path} can properly be converted with ONNX.''' )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
parser.add_argument(
"""--opset""", default=12, type=int, help="""The ONNX opset against which the model has to be tested."""
)
parser.add_argument(
"""--framework""", choices=["""onnx"""], default="""onnx""", help="""Frameworks against which to test the saved model."""
)
parser.add_argument(
"""--strict""", action="""store_true""", help="""Whether make the checking strict (raise errors) or not (raise warnings)"""
)
a__ = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 704
|
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 snake_case ( SCREAMING_SNAKE_CASE_ ,unittest.TestCase ):
'''simple docstring'''
snake_case_ : Optional[Any] = BertJapaneseTokenizer
snake_case_ : Optional[int] = False
snake_case_ : Optional[int] = True
def UpperCamelCase_ ( self : Optional[int]) -> int:
"""simple docstring"""
super().setUp()
_snake_case : List[str] = [
"""[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 : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""])
with open(self.vocab_file , """w""" , encoding="""utf-8""") as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]))
def UpperCamelCase_ ( self : Union[str, Any] , lowerCAmelCase : str) -> int:
"""simple docstring"""
_snake_case : List[Any] = """こんにちは、世界。 \nこんばんは、世界。"""
_snake_case : Optional[Any] = """こんにちは 、 世界 。 こんばんは 、 世界 。"""
return input_text, output_text
def UpperCamelCase_ ( self : Tuple , lowerCAmelCase : Any) -> Optional[Any]:
"""simple docstring"""
_snake_case , _snake_case : Dict = self.get_input_output_texts(lowerCAmelCase)
_snake_case : Optional[Any] = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase)
_snake_case : Tuple = tokenizer.decode(lowerCAmelCase , clean_up_tokenization_spaces=lowerCAmelCase)
return text, ids
def UpperCamelCase_ ( self : List[Any]) -> int:
"""simple docstring"""
pass # TODO add if relevant
def UpperCamelCase_ ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
pass # TODO add if relevant
def UpperCamelCase_ ( self : List[Any]) -> Dict:
"""simple docstring"""
pass # TODO add if relevant
def UpperCamelCase_ ( self : Dict) -> Any:
"""simple docstring"""
_snake_case : List[str] = self.tokenizer_class(self.vocab_file)
_snake_case : List[Any] = 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 UpperCamelCase_ ( self : int) -> Dict:
"""simple docstring"""
_snake_case : Tuple = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""mecab""")
self.assertIsNotNone(lowerCAmelCase)
_snake_case : List[str] = """こんにちは、世界。\nこんばんは、世界。"""
_snake_case : Dict = 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 : Tuple = 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 : Dict = pickle.load(lowerCAmelCase)
_snake_case : Optional[int] = tokenizer_new.tokenize(lowerCAmelCase)
self.assertListEqual(lowerCAmelCase , lowerCAmelCase)
def UpperCamelCase_ ( self : str) -> int:
"""simple docstring"""
_snake_case : Optional[Any] = 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 UpperCamelCase_ ( self : Optional[int]) -> Union[str, Any]:
"""simple docstring"""
try:
_snake_case : Optional[int] = 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 UpperCamelCase_ ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
try:
_snake_case : List[Any] = 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 UpperCamelCase_ ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
_snake_case : List[str] = 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 UpperCamelCase_ ( self : Optional[int]) -> int:
"""simple docstring"""
try:
_snake_case : Dict = 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 UpperCamelCase_ ( self : Optional[Any]) -> Tuple:
"""simple docstring"""
_snake_case : str = 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 UpperCamelCase_ ( self : Union[str, Any]) -> str:
"""simple docstring"""
_snake_case : Optional[Any] = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""sudachi""")
self.assertIsNotNone(lowerCAmelCase)
_snake_case : List[str] = """こんにちは、世界。\nこんばんは、世界。"""
_snake_case : str = 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 : Optional[Any] = 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 : Optional[Any] = pickle.load(lowerCAmelCase)
_snake_case : Tuple = tokenizer_new.tokenize(lowerCAmelCase)
self.assertListEqual(lowerCAmelCase , lowerCAmelCase)
@require_sudachi
def UpperCamelCase_ ( self : List[Any]) -> List[str]:
"""simple docstring"""
_snake_case : Union[str, Any] = 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 UpperCamelCase_ ( self : str) -> Tuple:
"""simple docstring"""
_snake_case : Optional[Any] = 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 UpperCamelCase_ ( self : Union[str, Any]) -> List[str]:
"""simple docstring"""
_snake_case : Dict = 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 UpperCamelCase_ ( self : Dict) -> str:
"""simple docstring"""
_snake_case : Union[str, Any] = 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 UpperCamelCase_ ( self : Tuple) -> Tuple:
"""simple docstring"""
_snake_case : List[str] = 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 UpperCamelCase_ ( self : Optional[int]) -> Union[str, Any]:
"""simple docstring"""
_snake_case : Dict = 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 UpperCamelCase_ ( self : Any) -> Union[str, Any]:
"""simple docstring"""
_snake_case : Tuple = 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 UpperCamelCase_ ( self : Any) -> Any:
"""simple docstring"""
_snake_case : int = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""jumanpp""")
self.assertIsNotNone(lowerCAmelCase)
_snake_case : Optional[Any] = """こんにちは、世界。\nこんばんは、世界。"""
_snake_case : Tuple = 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 : str = 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 : int = pickle.load(lowerCAmelCase)
_snake_case : List[Any] = tokenizer_new.tokenize(lowerCAmelCase)
self.assertListEqual(lowerCAmelCase , lowerCAmelCase)
@require_jumanpp
def UpperCamelCase_ ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_snake_case : Optional[Any] = 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 UpperCamelCase_ ( self : Dict) -> Optional[int]:
"""simple docstring"""
_snake_case : Union[str, Any] = 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 UpperCamelCase_ ( self : int) -> List[str]:
"""simple docstring"""
_snake_case : Union[str, Any] = 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 UpperCamelCase_ ( self : Optional[int]) -> List[str]:
"""simple docstring"""
_snake_case : str = 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 UpperCamelCase_ ( self : Optional[int]) -> int:
"""simple docstring"""
_snake_case : Union[str, Any] = 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 UpperCamelCase_ ( self : List[str]) -> Optional[Any]:
"""simple docstring"""
_snake_case : Union[str, Any] = ["""[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 : str = {}
for i, token in enumerate(lowerCAmelCase):
_snake_case : List[Any] = i
_snake_case : List[Any] = 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 UpperCamelCase_ ( self : Optional[Any]) -> str:
"""simple docstring"""
_snake_case : Optional[int] = BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp""")
_snake_case : Tuple = tokenizer.subword_tokenizer
_snake_case : Tuple = 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 : Union[str, Any] = 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 UpperCamelCase_ ( self : Union[str, Any]) -> Optional[int]:
"""simple docstring"""
_snake_case : int = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese""")
_snake_case : str = tokenizer.encode("""ありがとう。""" , add_special_tokens=lowerCAmelCase)
_snake_case : Optional[Any] = tokenizer.encode("""どういたしまして。""" , add_special_tokens=lowerCAmelCase)
_snake_case : str = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase)
_snake_case : Dict = 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 snake_case ( SCREAMING_SNAKE_CASE_ ,unittest.TestCase ):
'''simple docstring'''
snake_case_ : Tuple = BertJapaneseTokenizer
snake_case_ : Dict = False
def UpperCamelCase_ ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
super().setUp()
_snake_case : Optional[Any] = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
_snake_case : Optional[Any] = 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 UpperCamelCase_ ( self : str , **lowerCAmelCase : Union[str, Any]) -> Any:
"""simple docstring"""
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type="""character""" , **lowerCAmelCase)
def UpperCamelCase_ ( self : str , lowerCAmelCase : Tuple) -> Optional[int]:
"""simple docstring"""
_snake_case : Any = """こんにちは、世界。 \nこんばんは、世界。"""
_snake_case : List[Any] = """こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"""
return input_text, output_text
def UpperCamelCase_ ( self : str) -> int:
"""simple docstring"""
pass # TODO add if relevant
def UpperCamelCase_ ( self : List[str]) -> Optional[Any]:
"""simple docstring"""
pass # TODO add if relevant
def UpperCamelCase_ ( self : str) -> Any:
"""simple docstring"""
pass # TODO add if relevant
def UpperCamelCase_ ( self : Optional[int]) -> int:
"""simple docstring"""
_snake_case : Union[str, Any] = self.tokenizer_class(self.vocab_file , subword_tokenizer_type="""character""")
_snake_case : Dict = 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 UpperCamelCase_ ( self : List[str]) -> int:
"""simple docstring"""
_snake_case : str = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
_snake_case : int = {}
for i, token in enumerate(lowerCAmelCase):
_snake_case : int = i
_snake_case : Optional[int] = 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 UpperCamelCase_ ( self : int) -> List[str]:
"""simple docstring"""
_snake_case : Optional[int] = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese-char""")
_snake_case : List[str] = tokenizer.encode("""ありがとう。""" , add_special_tokens=lowerCAmelCase)
_snake_case : Optional[int] = tokenizer.encode("""どういたしまして。""" , add_special_tokens=lowerCAmelCase)
_snake_case : str = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase)
_snake_case : Any = 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 snake_case ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self : int) -> List[Any]:
"""simple docstring"""
_snake_case : List[str] = """cl-tohoku/bert-base-japanese"""
_snake_case : int = AutoTokenizer.from_pretrained(lowerCAmelCase)
self.assertIsInstance(lowerCAmelCase , lowerCAmelCase)
class snake_case ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self : str) -> Any:
"""simple docstring"""
_snake_case : str = """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 : Any = """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."""))
| 198
| 0
|
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class SCREAMING_SNAKE_CASE (a__ ):
def __init__( self , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = False , _UpperCAmelCase = False , _UpperCAmelCase = None , **_UpperCAmelCase , ):
'''simple docstring'''
__A : int = path_or_paths
__A : List[str] = split if split or isinstance(_UpperCAmelCase , _UpperCAmelCase) else 'train'
__A : Any = features
__A : Dict = cache_dir
__A : List[str] = keep_in_memory
__A : Union[str, Any] = streaming
__A : Tuple = num_proc
__A : Union[str, Any] = kwargs
@abstractmethod
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
pass
class SCREAMING_SNAKE_CASE (a__ ):
def __init__( self , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = False , _UpperCAmelCase = False , _UpperCAmelCase = None , **_UpperCAmelCase , ):
'''simple docstring'''
__A : Dict = features
__A : Any = cache_dir
__A : str = keep_in_memory
__A : Optional[Any] = streaming
__A : List[str] = num_proc
__A : List[Any] = kwargs
@abstractmethod
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
pass
| 8
|
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowercase__ : int = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
'''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
'''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''ctc_proj''',
'''mask_emb''': '''masked_spec_embed''',
}
lowercase__ : Dict = [
'''ctc_proj''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def _lowerCAmelCase ( __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Tuple , __snake_case : Any , __snake_case : List[str] ) -> Union[str, Any]:
for attribute in key.split('.' ):
__A : int = getattr(__snake_case , __snake_case )
if weight_type is not None:
__A : Optional[int] = getattr(__snake_case , __snake_case ).shape
else:
__A : List[str] = hf_pointer.shape
assert hf_shape == value.shape, (
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}'
)
if weight_type == "weight":
__A : Tuple = value
elif weight_type == "weight_g":
__A : Union[str, Any] = value
elif weight_type == "weight_v":
__A : Optional[Any] = value
elif weight_type == "bias":
__A : Optional[int] = value
else:
__A : Optional[int] = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def _lowerCAmelCase ( __snake_case : Any , __snake_case : List[str] ) -> List[Any]:
__A : Optional[Any] = []
__A : Any = fairseq_model.state_dict()
__A : Union[str, Any] = hf_model.feature_extractor
for name, value in fairseq_dict.items():
__A : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
__snake_case , __snake_case , __snake_case , __snake_case , hf_model.config.feat_extract_norm == 'group' , )
__A : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
__A : int = True
if "*" in mapped_key:
__A : Any = name.split(__snake_case )[0].split('.' )[-2]
__A : List[Any] = mapped_key.replace('*' , __snake_case )
if "weight_g" in name:
__A : Optional[Any] = 'weight_g'
elif "weight_v" in name:
__A : Union[str, Any] = 'weight_v'
elif "bias" in name and "relative_attention_bias" not in name:
__A : Optional[Any] = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__A : Tuple = 'weight'
else:
__A : Dict = None
set_recursively(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
continue
if not is_used:
unused_weights.append(__snake_case )
logger.warning(f'Unused weights: {unused_weights}' )
def _lowerCAmelCase ( __snake_case : List[str] , __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : List[str] , __snake_case : Optional[int] ) -> int:
__A : int = full_name.split('conv_layers.' )[-1]
__A : List[str] = name.split('.' )
__A : Optional[int] = int(items[0] )
__A : str = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
__A : Optional[int] = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
__A : Union[str, Any] = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
__A : Dict = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
__A : Any = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(__snake_case )
@torch.no_grad()
def _lowerCAmelCase ( __snake_case : Union[str, Any] , __snake_case : List[str] , __snake_case : Tuple=None ) -> Any:
# load the pre-trained checkpoints
__A : List[str] = torch.load(__snake_case )
__A : Dict = WavLMConfigOrig(checkpoint['cfg'] )
__A : Optional[int] = WavLMOrig(__snake_case )
model.load_state_dict(checkpoint['model'] )
model.eval()
if config_path is not None:
__A : List[Any] = WavLMConfig.from_pretrained(__snake_case )
else:
__A : Dict = WavLMConfig()
__A : Optional[Any] = WavLMModel(__snake_case )
recursively_load_weights(__snake_case , __snake_case )
hf_wavlm.save_pretrained(__snake_case )
if __name__ == "__main__":
lowercase__ : Tuple = 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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
lowercase__ : Any = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 8
| 1
|
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __UpperCAmelCase ( _snake_case : str, _snake_case : List[Any] ):
_lowercase = []
for part_id in partition_order:
_lowercase = df.where(f"""SPARK_PARTITION_ID() = {part_id}""" ).collect()
for row_idx, row in enumerate(_snake_case ):
expected_row_ids_and_row_dicts.append((f"""{part_id}_{row_idx}""", row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
_lowercase = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_lowercase = spark.range(1_0_0 ).repartition(1 )
_lowercase = Spark(_snake_case )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=1_6 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 5_0
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
_lowercase = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_lowercase = spark.range(1_0 ).repartition(2 )
_lowercase = [1, 0]
_lowercase = _generate_iterable_examples(_snake_case, _snake_case ) # Reverse the partitions.
_lowercase = _get_expected_row_ids_and_row_dicts_for_partition_order(_snake_case, _snake_case )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
_lowercase , _lowercase = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
_lowercase = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_lowercase = spark.range(1_0 ).repartition(1 )
_lowercase = SparkExamplesIterable(_snake_case )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(_snake_case ):
assert row_id == f"""0_{i}"""
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
_lowercase = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_lowercase = spark.range(3_0 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("numpy.random.Generator" ) as generator_mock:
_lowercase = lambda _snake_case : x.reverse()
_lowercase = _get_expected_row_ids_and_row_dicts_for_partition_order(_snake_case, [2, 1, 0] )
_lowercase = SparkExamplesIterable(_snake_case ).shuffle_data_sources(_snake_case )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(_snake_case ):
_lowercase , _lowercase = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
_lowercase = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_lowercase = spark.range(2_0 ).repartition(4 )
# Partitions 0 and 2
_lowercase = SparkExamplesIterable(_snake_case ).shard_data_sources(worker_id=0, num_workers=2 )
assert shard_it_a.n_shards == 2
_lowercase = _get_expected_row_ids_and_row_dicts_for_partition_order(_snake_case, [0, 2] )
for i, (row_id, row_dict) in enumerate(_snake_case ):
_lowercase , _lowercase = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
_lowercase = SparkExamplesIterable(_snake_case ).shard_data_sources(worker_id=1, num_workers=2 )
assert shard_it_a.n_shards == 2
_lowercase = _get_expected_row_ids_and_row_dicts_for_partition_order(_snake_case, [1, 3] )
for i, (row_id, row_dict) in enumerate(_snake_case ):
_lowercase , _lowercase = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
_lowercase = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_lowercase = spark.range(1_0_0 ).repartition(1 )
_lowercase = Spark(_snake_case )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 1_0_0
| 709
|
"""simple docstring"""
def __UpperCAmelCase ( _snake_case : int, _snake_case : list ):
_enforce_args(_snake_case, _snake_case )
if n == 0:
return 0
_lowercase = float("-inf" )
for i in range(1, n + 1 ):
_lowercase = max(
_snake_case, prices[i - 1] + naive_cut_rod_recursive(n - i, _snake_case ) )
return max_revue
def __UpperCAmelCase ( _snake_case : int, _snake_case : list ):
_enforce_args(_snake_case, _snake_case )
_lowercase = [float("-inf" ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(_snake_case, _snake_case, _snake_case )
def __UpperCAmelCase ( _snake_case : int, _snake_case : list, _snake_case : list ):
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
_lowercase = float("-inf" )
for i in range(1, n + 1 ):
_lowercase = max(
_snake_case, prices[i - 1] + _top_down_cut_rod_recursive(n - i, _snake_case, _snake_case ), )
_lowercase = max_revenue
return max_rev[n]
def __UpperCAmelCase ( _snake_case : int, _snake_case : list ):
_enforce_args(_snake_case, _snake_case )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
_lowercase = [float("-inf" ) for _ in range(n + 1 )]
_lowercase = 0
for i in range(1, n + 1 ):
_lowercase = max_rev[i]
for j in range(1, i + 1 ):
_lowercase = max(_snake_case, prices[j - 1] + max_rev[i - j] )
_lowercase = max_revenue_i
return max_rev[n]
def __UpperCAmelCase ( _snake_case : int, _snake_case : list ):
if n < 0:
_lowercase = f"""n must be greater than or equal to 0. Got n = {n}"""
raise ValueError(_snake_case )
if n > len(_snake_case ):
_lowercase = (
"Each integral piece of rod must have a corresponding price. "
f"""Got n = {n} but length of prices = {len(_snake_case )}"""
)
raise ValueError(_snake_case )
def __UpperCAmelCase ( ):
_lowercase = [6, 1_0, 1_2, 1_5, 2_0, 2_3]
_lowercase = len(_snake_case )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
_lowercase = 3_6
_lowercase = top_down_cut_rod(_snake_case, _snake_case )
_lowercase = bottom_up_cut_rod(_snake_case, _snake_case )
_lowercase = naive_cut_rod_recursive(_snake_case, _snake_case )
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()
| 227
| 0
|
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
__A = logging.get_logger(__name__)
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :int = ["""pixel_values"""]
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BILINEAR , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 2_5_5 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
lowerCAmelCase__ :int = size if size is not None else {'shortest_edge': 2_5_6}
lowerCAmelCase__ :Optional[int] = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
lowerCAmelCase__ :str = crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
lowerCAmelCase__ :Optional[int] = get_size_dict(__UpperCAmelCase , param_name='crop_size' )
lowerCAmelCase__ :Optional[Any] = do_resize
lowerCAmelCase__ :Dict = size
lowerCAmelCase__ :Tuple = resample
lowerCAmelCase__ :Any = do_center_crop
lowerCAmelCase__ :Tuple = crop_size
lowerCAmelCase__ :List[Any] = do_rescale
lowerCAmelCase__ :Any = rescale_factor
lowerCAmelCase__ :Tuple = do_normalize
lowerCAmelCase__ :Optional[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase__ :List[str] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
lowerCAmelCase__ :Any = get_resize_output_image_size(__UpperCAmelCase , size=size['shortest_edge'] , default_to_square=__UpperCAmelCase )
return resize(__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
lowerCAmelCase__ :Dict = get_size_dict(__UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(__UpperCAmelCase , size=(size['height'], size['width']) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase ):
'''simple docstring'''
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ):
'''simple docstring'''
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase__ :int = size if size is not None else self.size
lowerCAmelCase__ :Optional[Any] = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
lowerCAmelCase__ :List[Any] = resample if resample is not None else self.resample
lowerCAmelCase__ :List[str] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCAmelCase__ :List[str] = crop_size if crop_size is not None else self.crop_size
lowerCAmelCase__ :Union[str, Any] = get_size_dict(__UpperCAmelCase , param_name='crop_size' )
lowerCAmelCase__ :List[str] = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase__ :List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase__ :List[Any] = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase__ :Union[str, Any] = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase__ :Optional[int] = image_std if image_std is not None else self.image_std
lowerCAmelCase__ :List[str] = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
lowerCAmelCase__ :Optional[int] = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
lowerCAmelCase__ :Tuple = [self.resize(image=__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase ) for image in images]
if do_center_crop:
lowerCAmelCase__ :Union[str, Any] = [self.center_crop(image=__UpperCAmelCase , size=__UpperCAmelCase ) for image in images]
if do_rescale:
lowerCAmelCase__ :Union[str, Any] = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_normalize:
lowerCAmelCase__ :List[str] = [self.normalize(image=__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase ) for image in images]
lowerCAmelCase__ :Dict = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
lowerCAmelCase__ :List[str] = {'pixel_values': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
lowerCAmelCase__ :int = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(__UpperCAmelCase ) != len(__UpperCAmelCase ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(__UpperCAmelCase ):
lowerCAmelCase__ :Optional[Any] = target_sizes.numpy()
lowerCAmelCase__ :List[str] = []
for idx in range(len(__UpperCAmelCase ) ):
lowerCAmelCase__ :Optional[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=__UpperCAmelCase )
lowerCAmelCase__ :Tuple = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(__UpperCAmelCase )
else:
lowerCAmelCase__ :List[str] = logits.argmax(dim=1 )
lowerCAmelCase__ :Tuple = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 93
|
import copy
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
from ..auto import CONFIG_MAPPING
a__: Optional[Any] = logging.get_logger(__name__)
a__: Any = {
'microsoft/conditional-detr-resnet-50': (
'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
),
}
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = '''conditional_detr'''
__SCREAMING_SNAKE_CASE = ['''past_key_values''']
__SCREAMING_SNAKE_CASE = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self,__lowerCamelCase=True,__lowerCamelCase=None,__lowerCamelCase=3,__lowerCamelCase=300,__lowerCamelCase=6,__lowerCamelCase=2048,__lowerCamelCase=8,__lowerCamelCase=6,__lowerCamelCase=2048,__lowerCamelCase=8,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=True,__lowerCamelCase="relu",__lowerCamelCase=256,__lowerCamelCase=0.1,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=0.02,__lowerCamelCase=1.0,__lowerCamelCase=False,__lowerCamelCase="sine",__lowerCamelCase="resnet50",__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=2,__lowerCamelCase=5,__lowerCamelCase=2,__lowerCamelCase=1,__lowerCamelCase=1,__lowerCamelCase=2,__lowerCamelCase=5,__lowerCamelCase=2,__lowerCamelCase=0.25,**__lowerCamelCase,):
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
A__ = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(__lowerCamelCase,__lowerCamelCase ):
A__ = backbone_config.get('''model_type''' )
A__ = CONFIG_MAPPING[backbone_model_type]
A__ = config_class.from_dict(__lowerCamelCase )
A__ = use_timm_backbone
A__ = backbone_config
A__ = num_channels
A__ = num_queries
A__ = d_model
A__ = encoder_ffn_dim
A__ = encoder_layers
A__ = encoder_attention_heads
A__ = decoder_ffn_dim
A__ = decoder_layers
A__ = decoder_attention_heads
A__ = dropout
A__ = attention_dropout
A__ = activation_dropout
A__ = activation_function
A__ = init_std
A__ = init_xavier_std
A__ = encoder_layerdrop
A__ = decoder_layerdrop
A__ = encoder_layers
A__ = auxiliary_loss
A__ = position_embedding_type
A__ = backbone
A__ = use_pretrained_backbone
A__ = dilation
# Hungarian matcher
A__ = class_cost
A__ = bbox_cost
A__ = giou_cost
# Loss coefficients
A__ = mask_loss_coefficient
A__ = dice_loss_coefficient
A__ = cls_loss_coefficient
A__ = bbox_loss_coefficient
A__ = giou_loss_coefficient
A__ = focal_alpha
super().__init__(is_encoder_decoder=__lowerCamelCase,**__lowerCamelCase )
@property
def UpperCamelCase ( self ):
return self.encoder_attention_heads
@property
def UpperCamelCase ( self ):
return self.d_model
def UpperCamelCase ( self ):
A__ = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
A__ = self.backbone_config.to_dict()
A__ = self.__class__.model_type
return output
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = version.parse('''1.11''' )
@property
def UpperCamelCase ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def UpperCamelCase ( self ):
return 1E-5
@property
def UpperCamelCase ( self ):
return 12
| 190
| 0
|
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
lowerCAmelCase_ : List[Any] = logging.get_logger(__name__)
def __a ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : int , __lowerCamelCase : List[str] ) -> List[str]:
'''simple docstring'''
return [
int(1_000 * (box[0] / width) ),
int(1_000 * (box[1] / height) ),
int(1_000 * (box[2] / width) ),
int(1_000 * (box[3] / height) ),
]
def __a ( __lowerCamelCase : np.ndarray , __lowerCamelCase : Optional[str] , __lowerCamelCase : Optional[str] ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = to_pil_image(__lowerCamelCase )
lowercase_ , lowercase_ = pil_image.size
lowercase_ = pytesseract.image_to_data(__lowerCamelCase , lang=__lowerCamelCase , output_type="dict" , config=__lowerCamelCase )
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ = data["text"], data["left"], data["top"], data["width"], data["height"]
# filter empty words and corresponding coordinates
lowercase_ = [idx for idx, word in enumerate(__lowerCamelCase ) if not word.strip()]
lowercase_ = [word for idx, word in enumerate(__lowerCamelCase ) if idx not in irrelevant_indices]
lowercase_ = [coord for idx, coord in enumerate(__lowerCamelCase ) if idx not in irrelevant_indices]
lowercase_ = [coord for idx, coord in enumerate(__lowerCamelCase ) if idx not in irrelevant_indices]
lowercase_ = [coord for idx, coord in enumerate(__lowerCamelCase ) if idx not in irrelevant_indices]
lowercase_ = [coord for idx, coord in enumerate(__lowerCamelCase ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
lowercase_ = []
for x, y, w, h in zip(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
lowercase_ = [x, y, x + w, y + h]
actual_boxes.append(__lowerCamelCase )
# finally, normalize the bounding boxes
lowercase_ = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) )
assert len(__lowerCamelCase ) == len(__lowerCamelCase ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class lowercase ( __lowerCamelCase ):
lowerCamelCase_ =['pixel_values']
def __init__( self : Optional[int] , __lowerCAmelCase : bool = True , __lowerCAmelCase : Dict[str, int] = None , __lowerCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , __lowerCAmelCase : bool = True , __lowerCAmelCase : float = 1 / 255 , __lowerCAmelCase : bool = True , __lowerCAmelCase : Union[float, Iterable[float]] = None , __lowerCAmelCase : Union[float, Iterable[float]] = None , __lowerCAmelCase : bool = True , __lowerCAmelCase : Optional[str] = None , __lowerCAmelCase : Optional[str] = "" , **__lowerCAmelCase : str , ) -> None:
super().__init__(**__lowerCAmelCase)
lowercase_ = size if size is not None else {"height": 224, "width": 224}
lowercase_ = get_size_dict(__lowerCAmelCase)
lowercase_ = do_resize
lowercase_ = size
lowercase_ = resample
lowercase_ = do_rescale
lowercase_ = rescale_value
lowercase_ = do_normalize
lowercase_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
lowercase_ = apply_ocr
lowercase_ = ocr_lang
lowercase_ = tesseract_config
def __UpperCAmelCase ( self : Tuple , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : Dict[str, int] , __lowerCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , __lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCAmelCase : Any , ) -> np.ndarray:
lowercase_ = get_size_dict(__lowerCAmelCase)
if "height" not in size or "width" not in size:
raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}')
lowercase_ = (size["height"], size["width"])
return resize(__lowerCAmelCase , size=__lowerCAmelCase , resample=__lowerCAmelCase , data_format=__lowerCAmelCase , **__lowerCAmelCase)
def __UpperCAmelCase ( self : Dict , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : Union[int, float] , __lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCAmelCase : Union[str, Any] , ) -> np.ndarray:
return rescale(__lowerCAmelCase , scale=__lowerCAmelCase , data_format=__lowerCAmelCase , **__lowerCAmelCase)
def __UpperCAmelCase ( self : Tuple , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : Union[float, Iterable[float]] , __lowerCAmelCase : Union[float, Iterable[float]] , __lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCAmelCase : Any , ) -> np.ndarray:
return normalize(__lowerCAmelCase , mean=__lowerCAmelCase , std=__lowerCAmelCase , data_format=__lowerCAmelCase , **__lowerCAmelCase)
def __UpperCAmelCase ( self : Optional[Any] , __lowerCAmelCase : ImageInput , __lowerCAmelCase : bool = None , __lowerCAmelCase : Dict[str, int] = None , __lowerCAmelCase : List[Any]=None , __lowerCAmelCase : bool = None , __lowerCAmelCase : float = None , __lowerCAmelCase : bool = None , __lowerCAmelCase : Union[float, Iterable[float]] = None , __lowerCAmelCase : Union[float, Iterable[float]] = None , __lowerCAmelCase : bool = None , __lowerCAmelCase : Optional[str] = None , __lowerCAmelCase : Optional[str] = None , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : ChannelDimension = ChannelDimension.FIRST , **__lowerCAmelCase : Any , ) -> PIL.Image.Image:
lowercase_ = do_resize if do_resize is not None else self.do_resize
lowercase_ = size if size is not None else self.size
lowercase_ = get_size_dict(__lowerCAmelCase)
lowercase_ = resample if resample is not None else self.resample
lowercase_ = do_rescale if do_rescale is not None else self.do_rescale
lowercase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase_ = do_normalize if do_normalize is not None else self.do_normalize
lowercase_ = image_mean if image_mean is not None else self.image_mean
lowercase_ = image_std if image_std is not None else self.image_std
lowercase_ = apply_ocr if apply_ocr is not None else self.apply_ocr
lowercase_ = ocr_lang if ocr_lang is not None else self.ocr_lang
lowercase_ = tesseract_config if tesseract_config is not None else self.tesseract_config
lowercase_ = make_list_of_images(__lowerCAmelCase)
if not valid_images(__lowerCAmelCase):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray.")
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True.")
if do_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("If do_normalize is True, image_mean and image_std must be specified.")
# All transformations expect numpy arrays.
lowercase_ = [to_numpy_array(__lowerCAmelCase) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , "pytesseract")
lowercase_ = []
lowercase_ = []
for image in images:
lowercase_ , lowercase_ = apply_tesseract(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase)
words_batch.append(__lowerCAmelCase)
boxes_batch.append(__lowerCAmelCase)
if do_resize:
lowercase_ = [self.resize(image=__lowerCAmelCase , size=__lowerCAmelCase , resample=__lowerCAmelCase) for image in images]
if do_rescale:
lowercase_ = [self.rescale(image=__lowerCAmelCase , scale=__lowerCAmelCase) for image in images]
if do_normalize:
lowercase_ = [self.normalize(image=__lowerCAmelCase , mean=__lowerCAmelCase , std=__lowerCAmelCase) for image in images]
lowercase_ = [to_channel_dimension_format(__lowerCAmelCase , __lowerCAmelCase) for image in images]
lowercase_ = BatchFeature(data={"pixel_values": images} , tensor_type=__lowerCAmelCase)
if apply_ocr:
lowercase_ = words_batch
lowercase_ = boxes_batch
return data
| 719
|
'''simple docstring'''
def __a ( __lowerCamelCase : list[int] , __lowerCamelCase : int ) -> bool:
'''simple docstring'''
lowercase_ = len(__lowerCamelCase )
lowercase_ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
lowercase_ = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
lowercase_ = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
lowercase_ = subset[i - 1][j]
if arr[i - 1] <= j:
lowercase_ = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 461
| 0
|
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 __A ( snake_case__ ,unittest.TestCase ):
'''simple docstring'''
a_ = KandinskyImgaImgPipeline
a_ = ["prompt", "image_embeds", "negative_image_embeds", "image"]
a_ = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
]
a_ = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
a_ = False
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 32
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 32
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.time_input_dim
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.time_input_dim * 4
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 100
@property
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : Dict = XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
return tokenizer
@property
def SCREAMING_SNAKE_CASE__ ( self ):
torch.manual_seed(0 )
_lowerCAmelCase : List[Any] = 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=1005 , )
_lowerCAmelCase : List[Any] = MultilingualCLIP(UpperCamelCase_ )
_lowerCAmelCase : int = text_encoder.eval()
return text_encoder
@property
def SCREAMING_SNAKE_CASE__ ( self ):
torch.manual_seed(0 )
_lowerCAmelCase : str = {
"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,
}
_lowerCAmelCase : Dict = UNetaDConditionModel(**UpperCamelCase_ )
return model
@property
def SCREAMING_SNAKE_CASE__ ( self ):
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 ):
torch.manual_seed(0 )
_lowerCAmelCase : List[str] = VQModel(**self.dummy_movq_kwargs )
return model
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : str = self.dummy_text_encoder
_lowerCAmelCase : List[str] = self.dummy_tokenizer
_lowerCAmelCase : int = self.dummy_unet
_lowerCAmelCase : Dict = self.dummy_movq
_lowerCAmelCase : Any = {
"num_train_timesteps": 1000,
"beta_schedule": "linear",
"beta_start": 0.0_0085,
"beta_end": 0.012,
"clip_sample": False,
"set_alpha_to_one": False,
"steps_offset": 0,
"prediction_type": "epsilon",
"thresholding": False,
}
_lowerCAmelCase : Optional[Any] = DDIMScheduler(**UpperCamelCase_ )
_lowerCAmelCase : Optional[Any] = {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case=0 ):
_lowerCAmelCase : List[Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
_lowerCAmelCase : Tuple = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(UpperCamelCase_ )
# create init_image
_lowerCAmelCase : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
_lowerCAmelCase : List[str] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_lowerCAmelCase : Optional[Any] = Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert("RGB" ).resize((256, 256) )
if str(UpperCamelCase_ ).startswith("mps" ):
_lowerCAmelCase : str = torch.manual_seed(UpperCamelCase_ )
else:
_lowerCAmelCase : Dict = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
_lowerCAmelCase : Optional[Any] = {
"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 ):
_lowerCAmelCase : Any = "cpu"
_lowerCAmelCase : int = self.get_dummy_components()
_lowerCAmelCase : Optional[int] = self.pipeline_class(**UpperCamelCase_ )
_lowerCAmelCase : Dict = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
_lowerCAmelCase : Dict = pipe(**self.get_dummy_inputs(UpperCamelCase_ ) )
_lowerCAmelCase : List[str] = output.images
_lowerCAmelCase : Optional[int] = pipe(
**self.get_dummy_inputs(UpperCamelCase_ ) , return_dict=UpperCamelCase_ , )[0]
_lowerCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_lowerCAmelCase : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCAmelCase : List[Any] = np.array(
[0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class __A ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : int = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinsky/kandinsky_img2img_frog.npy" )
_lowerCAmelCase : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
_lowerCAmelCase : int = "A red cartoon frog, 4k"
_lowerCAmelCase : Dict = KandinskyPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1-prior" , torch_dtype=torch.floataa )
pipe_prior.to(UpperCamelCase_ )
_lowerCAmelCase : Optional[Any] = KandinskyImgaImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1" , torch_dtype=torch.floataa )
_lowerCAmelCase : int = pipeline.to(UpperCamelCase_ )
pipeline.set_progress_bar_config(disable=UpperCamelCase_ )
_lowerCAmelCase : Optional[Any] = torch.Generator(device="cpu" ).manual_seed(0 )
_lowerCAmelCase : List[str] = pipe_prior(
UpperCamelCase_ , generator=UpperCamelCase_ , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
_lowerCAmelCase : Dict = 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" , )
_lowerCAmelCase : str = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_ )
| 424
|
'''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ ) -> bool:
"""simple docstring"""
__UpperCAmelCase : int = len(lowerCamelCase__ )
# We need to create solution object to save path.
__UpperCAmelCase : List[str] = [[0 for _ in range(lowerCamelCase__ )] for _ in range(lowerCamelCase__ )]
__UpperCAmelCase : Optional[Any] = run_maze(lowerCamelCase__ , 0 , 0 , lowerCamelCase__ )
if solved:
print("\n".join(str(lowerCamelCase__ ) for row in solutions ) )
else:
print("No solution exists!" )
return solved
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
"""simple docstring"""
__UpperCAmelCase : str = len(lowerCamelCase__ )
# Final check point.
if i == j == (size - 1):
__UpperCAmelCase : str = 1
return True
__UpperCAmelCase : Any = (not i < 0) and (not j < 0) # Check lower bounds
__UpperCAmelCase : Union[str, Any] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
__UpperCAmelCase : Tuple = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
__UpperCAmelCase : Optional[int] = 1
# check for directions
if (
run_maze(lowerCamelCase__ , i + 1 , lowerCamelCase__ , lowerCamelCase__ )
or run_maze(lowerCamelCase__ , lowerCamelCase__ , j + 1 , lowerCamelCase__ )
or run_maze(lowerCamelCase__ , i - 1 , lowerCamelCase__ , lowerCamelCase__ )
or run_maze(lowerCamelCase__ , lowerCamelCase__ , j - 1 , lowerCamelCase__ )
):
return True
__UpperCAmelCase : Dict = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 168
| 0
|
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class lowerCamelCase_ ( lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase = ShapEPipeline
__UpperCAmelCase = ["prompt"]
__UpperCAmelCase = ["prompt"]
__UpperCAmelCase = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
__UpperCAmelCase = False
@property
def A ( self ) -> Dict:
'''simple docstring'''
return 3_2
@property
def A ( self ) -> Tuple:
'''simple docstring'''
return 3_2
@property
def A ( self ) -> Union[str, Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A ( self ) -> List[str]:
'''simple docstring'''
return 8
@property
def A ( self ) -> List[str]:
'''simple docstring'''
__lowercase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def A ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=3_7 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
return CLIPTextModelWithProjection(snake_case_ )
@property
def A ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 1_6,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 3_2,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
__lowercase = PriorTransformer(**snake_case_ )
return model
@property
def A ( self ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = {
'''param_shapes''': (
(self.renderer_dim, 9_3),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 1_2,
'''background''': (
0.1,
0.1,
0.1,
),
}
__lowercase = ShapERenderer(**snake_case_ )
return model
def A ( self ) -> Dict:
'''simple docstring'''
__lowercase = self.dummy_prior
__lowercase = self.dummy_text_encoder
__lowercase = self.dummy_tokenizer
__lowercase = self.dummy_renderer
__lowercase = HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=1_0_2_4 , prediction_type='''sample''' , use_karras_sigmas=snake_case_ , clip_sample=snake_case_ , clip_sample_range=1.0 , )
__lowercase = {
'''prior''': prior,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def A ( self , snake_case_ , snake_case_=0 ) -> int:
'''simple docstring'''
if str(snake_case_ ).startswith('''mps''' ):
__lowercase = torch.manual_seed(snake_case_ )
else:
__lowercase = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
__lowercase = {
'''prompt''': '''horse''',
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 3_2,
'''output_type''': '''np''',
}
return inputs
def A ( self ) -> Optional[Any]:
'''simple docstring'''
__lowercase = '''cpu'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**snake_case_ )
__lowercase = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
__lowercase = pipe(**self.get_dummy_inputs(snake_case_ ) )
__lowercase = output.images[0]
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (2_0, 3_2, 3_2, 3)
__lowercase = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A ( self ) -> Optional[int]:
'''simple docstring'''
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def A ( self ) -> Tuple:
'''simple docstring'''
__lowercase = torch_device == '''cpu'''
__lowercase = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=snake_case_ , relax_max_difference=snake_case_ , )
def A ( self ) -> Tuple:
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**snake_case_ )
__lowercase = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
__lowercase = 1
__lowercase = 2
__lowercase = self.get_dummy_inputs(snake_case_ )
for key in inputs.keys():
if key in self.batch_params:
__lowercase = batch_size * [inputs[key]]
__lowercase = pipe(**snake_case_ , num_images_per_prompt=snake_case_ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def A ( self ) -> Dict:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self ) -> int:
'''simple docstring'''
__lowercase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_np_out.npy''' )
__lowercase = ShapEPipeline.from_pretrained('''openai/shap-e''' )
__lowercase = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
__lowercase = torch.Generator(device=snake_case_ ).manual_seed(0 )
__lowercase = pipe(
'''a shark''' , generator=snake_case_ , guidance_scale=1_5.0 , num_inference_steps=6_4 , frame_size=6_4 , output_type='''np''' , ).images[0]
assert images.shape == (2_0, 6_4, 6_4, 3)
assert_mean_pixel_difference(snake_case_ , snake_case_ )
| 527
|
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'''{price_plus_tax(100, 0.25) = }''')
print(f'''{price_plus_tax(125.50, 0.05) = }''')
| 527
| 1
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class _lowerCamelCase :
'''simple docstring'''
A_ : Tuple = XGLMConfig
A_ : str = {}
A_ : Any = """gelu"""
def __init__( self : Union[str, Any] , _A : List[str] , _A : Dict=14 , _A : Any=7 , _A : Any=True , _A : Tuple=True , _A : Union[str, Any]=True , _A : Any=99 , _A : Optional[Any]=32 , _A : Union[str, Any]=2 , _A : Union[str, Any]=4 , _A : List[Any]=37 , _A : Tuple="gelu" , _A : Dict=0.1 , _A : Optional[int]=0.1 , _A : List[Any]=512 , _A : Tuple=0.02 , ) -> str:
__magic_name__ : Any = parent
__magic_name__ : Dict = batch_size
__magic_name__ : Dict = seq_length
__magic_name__ : int = is_training
__magic_name__ : List[Any] = use_input_mask
__magic_name__ : Tuple = use_labels
__magic_name__ : Optional[int] = vocab_size
__magic_name__ : Union[str, Any] = d_model
__magic_name__ : str = num_hidden_layers
__magic_name__ : Optional[Any] = num_attention_heads
__magic_name__ : Any = ffn_dim
__magic_name__ : List[str] = activation_function
__magic_name__ : Optional[Any] = activation_dropout
__magic_name__ : List[Any] = attention_dropout
__magic_name__ : Optional[Any] = max_position_embeddings
__magic_name__ : Union[str, Any] = initializer_range
__magic_name__ : Optional[Any] = None
__magic_name__ : str = 0
__magic_name__ : Optional[Any] = 2
__magic_name__ : int = 1
def __lowerCAmelCase ( self : Any ) -> Union[str, Any]:
return XGLMConfig.from_pretrained('facebook/xglm-564M' )
def __lowerCAmelCase ( self : Optional[Any] ) -> str:
__magic_name__ : Tuple = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
__magic_name__ : int = None
if self.use_input_mask:
__magic_name__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
__magic_name__ : List[Any] = self.get_config()
__magic_name__ : Optional[int] = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def __lowerCAmelCase ( self : Dict ) -> Optional[Any]:
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=_A , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=_A , )
def __lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
__magic_name__ : List[Any] = self.prepare_config_and_inputs()
(
(
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) ,
) : Tuple = config_and_inputs
__magic_name__ : Optional[int] = {
'input_ids': input_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_tf
class _lowerCamelCase ( lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
A_ : int = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
A_ : Optional[Any] = (TFXGLMForCausalLM,) if is_tf_available() else ()
A_ : Union[str, Any] = (
{"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
)
A_ : Dict = False
A_ : Union[str, Any] = False
A_ : Tuple = False
def __lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
__magic_name__ : Optional[Any] = TFXGLMModelTester(self )
__magic_name__ : Dict = ConfigTester(self , config_class=_A , n_embd=37 )
def __lowerCAmelCase ( self : Any ) -> Dict:
self.config_tester.run_common_tests()
@slow
def __lowerCAmelCase ( self : Any ) -> int:
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ : Union[str, Any] = TFXGLMModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
def __lowerCAmelCase ( self : Union[str, Any] ) -> List[str]:
super().test_resize_token_embeddings()
@require_tf
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowerCAmelCase ( self : str , _A : Optional[int]=True ) -> Dict:
__magic_name__ : int = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
__magic_name__ : str = tf.convert_to_tensor([[2, 268, 9865]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
__magic_name__ : str = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
# fmt: on
__magic_name__ : Union[str, Any] = model.generate(_A , do_sample=_A , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , _A )
@slow
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
__magic_name__ : Tuple = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
__magic_name__ : List[Any] = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
tf.random.set_seed(0 )
__magic_name__ : Optional[int] = tokenizer('Today is a nice day and' , return_tensors='tf' )
__magic_name__ : Optional[int] = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(':/CPU:0' ):
__magic_name__ : Tuple = model.generate(_A , do_sample=_A , seed=[7, 0] )
__magic_name__ : List[Any] = tokenizer.decode(output_ids[0] , skip_special_tokens=_A )
__magic_name__ : str = (
'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
)
self.assertEqual(_A , _A )
@slow
def __lowerCAmelCase ( self : int ) -> str:
__magic_name__ : int = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
__magic_name__ : Optional[Any] = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
__magic_name__ : Dict = 'left'
# use different length sentences to test batching
__magic_name__ : str = [
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When',
'Hello, my dog is a little',
]
__magic_name__ : int = tokenizer(_A , return_tensors='tf' , padding=_A )
__magic_name__ : Dict = inputs['input_ids']
__magic_name__ : Any = model.generate(input_ids=_A , attention_mask=inputs['attention_mask'] , max_new_tokens=12 )
__magic_name__ : Union[str, Any] = tokenizer(sentences[0] , return_tensors='tf' ).input_ids
__magic_name__ : Dict = model.generate(input_ids=_A , max_new_tokens=12 )
__magic_name__ : Union[str, Any] = tokenizer(sentences[1] , return_tensors='tf' ).input_ids
__magic_name__ : int = model.generate(input_ids=_A , max_new_tokens=12 )
__magic_name__ : Dict = tokenizer.batch_decode(_A , skip_special_tokens=_A )
__magic_name__ : List[str] = tokenizer.decode(output_non_padded[0] , skip_special_tokens=_A )
__magic_name__ : str = tokenizer.decode(output_padded[0] , skip_special_tokens=_A )
__magic_name__ : List[str] = [
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
'a single',
'Hello, my dog is a little bit of a shy one, but he is very friendly',
]
self.assertListEqual(_A , _A )
self.assertListEqual(_A , [non_padded_sentence, padded_sentence] )
| 561
|
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : str ):
"""simple docstring"""
__magic_name__ : str = 0
# if input_string is "aba" than new_input_string become "a|b|a"
__magic_name__ : Optional[Any] = ''
__magic_name__ : Optional[int] = ''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(lowerCAmelCase ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
__magic_name__ , __magic_name__ : str = 0, 0
# length[i] shows the length of palindromic substring with center i
__magic_name__ : Dict = [1 for i in range(len(lowerCAmelCase ) )]
# for each character in new_string find corresponding palindromic string
__magic_name__ : Tuple = 0
for j in range(len(lowerCAmelCase ) ):
__magic_name__ : Dict = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(lowerCAmelCase )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
__magic_name__ : Union[str, Any] = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
__magic_name__ : Union[str, Any] = j - k + 1 # noqa: E741
__magic_name__ : Any = j + k - 1
# update max_length and start position
if max_length < length[j]:
__magic_name__ : Tuple = length[j]
__magic_name__ : Tuple = j
# create that string
__magic_name__ : Tuple = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 561
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Optional[Any] = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
"""processing_git""": ["""GitProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
"""GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GitForCausalLM""",
"""GitModel""",
"""GitPreTrainedModel""",
"""GitVisionModel""",
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 709
|
def __lowerCamelCase ( ) -> int:
"""simple docstring"""
return 1
def __lowerCamelCase ( __a :int ) -> int:
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def __lowerCamelCase ( __a :int ) -> int:
"""simple docstring"""
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(__a )
def __lowerCamelCase ( __a :int ) -> int:
"""simple docstring"""
return 0 if x < 0 else ten_pence(x - 1_0 ) + five_pence(__a )
def __lowerCamelCase ( __a :int ) -> int:
"""simple docstring"""
return 0 if x < 0 else twenty_pence(x - 2_0 ) + ten_pence(__a )
def __lowerCamelCase ( __a :int ) -> int:
"""simple docstring"""
return 0 if x < 0 else fifty_pence(x - 5_0 ) + twenty_pence(__a )
def __lowerCamelCase ( __a :int ) -> int:
"""simple docstring"""
return 0 if x < 0 else one_pound(x - 1_0_0 ) + fifty_pence(__a )
def __lowerCamelCase ( __a :int ) -> int:
"""simple docstring"""
return 0 if x < 0 else two_pound(x - 2_0_0 ) + one_pound(__a )
def __lowerCamelCase ( __a :int = 2_0_0 ) -> int:
"""simple docstring"""
return two_pound(__a )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 247
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": ["""XLMTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XLMForMultipleChoice""",
"""XLMForQuestionAnswering""",
"""XLMForQuestionAnsweringSimple""",
"""XLMForSequenceClassification""",
"""XLMForTokenClassification""",
"""XLMModel""",
"""XLMPreTrainedModel""",
"""XLMWithLMHeadModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXLMForMultipleChoice""",
"""TFXLMForQuestionAnsweringSimple""",
"""TFXLMForSequenceClassification""",
"""TFXLMForTokenClassification""",
"""TFXLMMainLayer""",
"""TFXLMModel""",
"""TFXLMPreTrainedModel""",
"""TFXLMWithLMHeadModel""",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5
|
"""simple docstring"""
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
__snake_case = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> dict[str, str]:
"""simple docstring"""
__snake_case = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__snake_case = remove_duplicates(key.upper() )
__snake_case = len(SCREAMING_SNAKE_CASE )
# First fill cipher with key characters
__snake_case = {alphabet[i]: char for i, char in enumerate(SCREAMING_SNAKE_CASE )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(SCREAMING_SNAKE_CASE ) , 26 ):
__snake_case = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__snake_case = alphabet[i - offset]
__snake_case = char
return cipher_alphabet
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
return "".join(cipher_map.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for ch in message.upper() )
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
__snake_case = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for ch in message.upper() )
def __UpperCamelCase ( ) -> None:
"""simple docstring"""
__snake_case = input("Enter message to encode or decode: " ).strip()
__snake_case = input("Enter keyword: " ).strip()
__snake_case = input("Encipher or decipher? E/D:" ).strip()[0].lower()
try:
__snake_case = {"e": encipher, "d": decipher}[option]
except KeyError:
raise KeyError("invalid input option" )
__snake_case = create_cipher_map(SCREAMING_SNAKE_CASE )
print(func(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 163
| 0
|
"""simple docstring"""
import os
import pytest
from attr import dataclass
lowerCAmelCase_ = '''us-east-1''' # defaults region
@dataclass
class _snake_case :
"""simple docstring"""
a = 42
a = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
a = {
"task_name": "mnli",
"per_device_train_batch_size": 16,
"per_device_eval_batch_size": 16,
"do_train": True,
"do_eval": True,
"do_predict": True,
"output_dir": "/opt/ml/model",
"overwrite_output_dir": True,
"max_steps": 5_00,
"save_steps": 55_00,
}
a = {**hyperparameters, "max_steps": 10_00}
@property
def _lowerCAmelCase ( self : List[str]):
"""simple docstring"""
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def _lowerCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
return f"""{self.framework}-transfromers-test"""
@property
def _lowerCAmelCase ( self : List[Any]):
"""simple docstring"""
return f"""./tests/sagemaker/scripts/{self.framework}"""
@property
def _lowerCAmelCase ( self : Dict):
"""simple docstring"""
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope="""class""" )
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> str:
_SCREAMING_SNAKE_CASE : int = SageMakerTestEnvironment(framework=request.cls.framework )
| 635
|
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = ['''model.decoder.embed_positions.weights''']
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> Optional[int]:
if "emb" in name:
_SCREAMING_SNAKE_CASE : List[Any] = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
_SCREAMING_SNAKE_CASE : List[str] = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
_SCREAMING_SNAKE_CASE : Union[str, Any] = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
_SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
_SCREAMING_SNAKE_CASE : Union[str, Any] = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
_SCREAMING_SNAKE_CASE : int = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
_SCREAMING_SNAKE_CASE : Dict = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
_SCREAMING_SNAKE_CASE : Dict = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
_SCREAMING_SNAKE_CASE : Tuple = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
_SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
_SCREAMING_SNAKE_CASE : str = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> Tuple[Dict, Dict]:
_SCREAMING_SNAKE_CASE : str = list(state_dict.keys() )
_SCREAMING_SNAKE_CASE : Tuple = {}
for key in keys:
_SCREAMING_SNAKE_CASE : Dict = state_dict.pop(__SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE : int = rename_keys(__SCREAMING_SNAKE_CASE )
if "in_proj_weight" in key:
# split fused qkv proj
_SCREAMING_SNAKE_CASE : str = val[:hidden_size, :]
_SCREAMING_SNAKE_CASE : Any = val[hidden_size : 2 * hidden_size, :]
_SCREAMING_SNAKE_CASE : Optional[Any] = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
_SCREAMING_SNAKE_CASE : int = val
else:
_SCREAMING_SNAKE_CASE : Dict = val
return state_dict, enc_dec_proj_state_dict
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> MusicgenDecoderConfig:
if checkpoint == "small":
# default config values
_SCREAMING_SNAKE_CASE : Optional[Any] = 1_024
_SCREAMING_SNAKE_CASE : str = 24
_SCREAMING_SNAKE_CASE : Any = 16
elif checkpoint == "medium":
_SCREAMING_SNAKE_CASE : Dict = 1_536
_SCREAMING_SNAKE_CASE : Union[str, Any] = 48
_SCREAMING_SNAKE_CASE : Optional[Any] = 24
elif checkpoint == "large":
_SCREAMING_SNAKE_CASE : List[Any] = 2_048
_SCREAMING_SNAKE_CASE : Optional[int] = 48
_SCREAMING_SNAKE_CASE : str = 32
else:
raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""" )
_SCREAMING_SNAKE_CASE : Optional[Any] = MusicgenDecoderConfig(
hidden_size=__SCREAMING_SNAKE_CASE , ffn_dim=hidden_size * 4 , num_hidden_layers=__SCREAMING_SNAKE_CASE , num_attention_heads=__SCREAMING_SNAKE_CASE , )
return config
@torch.no_grad()
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE="cpu" )-> str:
_SCREAMING_SNAKE_CASE : str = MusicGen.get_pretrained(__SCREAMING_SNAKE_CASE , device=__SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE : List[str] = decoder_config_from_checkpoint(__SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE : List[Any] = fairseq_model.lm.state_dict()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[str] = rename_state_dict(
__SCREAMING_SNAKE_CASE , hidden_size=decoder_config.hidden_size )
_SCREAMING_SNAKE_CASE : Tuple = TaEncoderModel.from_pretrained("""t5-base""" )
_SCREAMING_SNAKE_CASE : List[Any] = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
_SCREAMING_SNAKE_CASE : str = MusicgenForCausalLM(__SCREAMING_SNAKE_CASE ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : str = decoder.load_state_dict(__SCREAMING_SNAKE_CASE , strict=__SCREAMING_SNAKE_CASE )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(__SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ) > 0:
raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}""" )
if len(__SCREAMING_SNAKE_CASE ) > 0:
raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
_SCREAMING_SNAKE_CASE : Dict = MusicgenForConditionalGeneration(text_encoder=__SCREAMING_SNAKE_CASE , audio_encoder=__SCREAMING_SNAKE_CASE , decoder=__SCREAMING_SNAKE_CASE )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(__SCREAMING_SNAKE_CASE )
# check we can do a forward pass
_SCREAMING_SNAKE_CASE : Optional[Any] = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
_SCREAMING_SNAKE_CASE : Dict = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
_SCREAMING_SNAKE_CASE : Optional[int] = model(input_ids=__SCREAMING_SNAKE_CASE , decoder_input_ids=__SCREAMING_SNAKE_CASE ).logits
if logits.shape != (8, 1, 2_048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
_SCREAMING_SNAKE_CASE : Optional[int] = AutoTokenizer.from_pretrained("""t5-base""" )
_SCREAMING_SNAKE_CASE : List[str] = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
_SCREAMING_SNAKE_CASE : Optional[int] = MusicgenProcessor(feature_extractor=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE )
# set the appropriate bos/pad token ids
_SCREAMING_SNAKE_CASE : Optional[Any] = 2_048
_SCREAMING_SNAKE_CASE : List[Any] = 2_048
# set other default generation config params
_SCREAMING_SNAKE_CASE : Any = int(30 * audio_encoder.config.frame_rate )
_SCREAMING_SNAKE_CASE : Tuple = True
_SCREAMING_SNAKE_CASE : int = 3.0
if pytorch_dump_folder is not None:
Path(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
processor.save_pretrained(__SCREAMING_SNAKE_CASE )
if repo_id:
logger.info(F"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(__SCREAMING_SNAKE_CASE )
processor.push_to_hub(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint''',
default='''small''',
type=str,
help='''Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.''',
)
parser.add_argument(
'''--pytorch_dump_folder''',
required=True,
default=None,
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
parser.add_argument(
'''--device''', default='''cpu''', type=str, help='''Torch device to run the conversion, either cpu or cuda.'''
)
lowerCAmelCase_ = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 635
| 1
|
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
__A =2_5_0_0_0_4
__A =2_5_0_0_2_0
@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = MBartTokenizer
lowerCAmelCase__ = MBartTokenizerFast
lowerCAmelCase__ = True
lowerCAmelCase__ = True
def SCREAMING_SNAKE_CASE_( self ) -> Any:
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase_ = MBartTokenizer(lowercase , keep_accents=lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE_( self ) -> Any:
lowerCamelCase_ = MBartTokenizer(lowercase , keep_accents=lowercase )
lowerCamelCase_ = tokenizer.tokenize("This is a test" )
self.assertListEqual(lowercase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowerCamelCase_ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
lowercase , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(lowercase )
self.assertListEqual(
lowercase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCamelCase_ = tokenizer.convert_ids_to_tokens(lowercase )
self.assertListEqual(
lowercase , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def SCREAMING_SNAKE_CASE_( self ) -> Any:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowerCamelCase_ = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(lowercase , **lowercase )
lowerCamelCase_ = self.tokenizer_class.from_pretrained(lowercase , **lowercase )
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(lowercase )
lowerCamelCase_ = tokenizer_p.save_pretrained(lowercase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
lowerCamelCase_ = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(lowercase , lowercase )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(lowercase )
lowerCamelCase_ = tokenizer_p.from_pretrained(lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase , lowercase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowercase )
# Save tokenizer rust, legacy_format=True
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(lowercase , legacy_format=lowercase )
lowerCamelCase_ = tokenizer_p.save_pretrained(lowercase )
# Checks it save with the same files
self.assertSequenceEqual(lowercase , lowercase )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(lowercase )
lowerCamelCase_ = tokenizer_p.from_pretrained(lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase , lowercase ) )
shutil.rmtree(lowercase )
# Save tokenizer rust, legacy_format=False
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(lowercase , legacy_format=lowercase )
lowerCamelCase_ = tokenizer_p.save_pretrained(lowercase )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(lowercase )
lowerCamelCase_ = tokenizer_p.from_pretrained(lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase , lowercase ) )
shutil.rmtree(lowercase )
@require_torch
@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
lowerCAmelCase__ = 'facebook/mbart-large-en-ro'
lowerCAmelCase__ = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
lowerCAmelCase__ = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
lowerCAmelCase__ = [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
@classmethod
def SCREAMING_SNAKE_CASE_( cls ) -> Any:
lowerCamelCase_ = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO" )
lowerCamelCase_ = 1
return cls
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] , 250001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] , 250004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] , 250020 )
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
lowerCamelCase_ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
self.assertIn(lowercase , self.tokenizer.all_special_ids )
lowerCamelCase_ = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
lowerCamelCase_ = self.tokenizer.decode(lowercase , skip_special_tokens=lowercase )
lowerCamelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowercase )
self.assertEqual(lowercase , lowercase )
self.assertNotIn(self.tokenizer.eos_token , lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , lowercase )
lowerCamelCase_ = 10
lowerCamelCase_ = self.tokenizer(lowercase , max_length=lowercase , truncation=lowercase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , lowercase )
self.assertEqual(len(lowercase ) , lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [250026, 250001] )
def SCREAMING_SNAKE_CASE_( self ) -> int:
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowercase )
lowerCamelCase_ = MBartTokenizer.from_pretrained(lowercase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowercase )
@require_torch
def SCREAMING_SNAKE_CASE_( self ) -> int:
lowerCamelCase_ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=lowercase , return_tensors="pt" )
lowerCamelCase_ = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
lowerCamelCase_ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowercase , truncation=lowercase , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
lowerCamelCase_ = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
lowerCamelCase_ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowercase )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
lowerCamelCase_ = self.tokenizer(self.src_text , padding=lowercase , truncation=lowercase , max_length=3 , return_tensors="pt" )
lowerCamelCase_ = self.tokenizer(
text_target=self.tgt_text , padding=lowercase , truncation=lowercase , max_length=10 , return_tensors="pt" )
lowerCamelCase_ = targets["input_ids"]
lowerCamelCase_ = shift_tokens_right(lowercase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
lowerCamelCase_ = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR" )
self.assertEqual(
nested_simplify(lowercase ) , {
# A, test, EOS, en_XX
"input_ids": [[62, 3034, 2, 250004]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 250001,
} , )
| 463
|
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__A ='''http://www.mocksite.com/file1.txt'''
__A ='''"text": ["foo", "foo"]'''
__A ='''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = 2_00
lowerCAmelCase__ = {'Content-Length': '100'}
lowerCAmelCase__ = {}
def SCREAMING_SNAKE_CASE_( self , **lowercase ) -> int:
return [bytes(lowercase , "utf-8" )]
def lowerCamelCase_ ( *lowerCamelCase__ , **lowerCamelCase__ ):
return MockResponse()
@pytest.mark.parametrize("urls_type" , [str, list, dict] )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
import requests
monkeypatch.setattr(lowerCamelCase__ , "request" , lowerCamelCase__ )
lowerCamelCase_ = URL
if issubclass(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = url
elif issubclass(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = [url]
elif issubclass(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = {"train": url}
lowerCamelCase_ = "dummy"
lowerCamelCase_ = "downloads"
lowerCamelCase_ = tmp_path
lowerCamelCase_ = DownloadConfig(
cache_dir=os.path.join(lowerCamelCase__ , lowerCamelCase__ ) , use_etag=lowerCamelCase__ , )
lowerCamelCase_ = DownloadManager(dataset_name=lowerCamelCase__ , download_config=lowerCamelCase__ )
lowerCamelCase_ = dl_manager.download(lowerCamelCase__ )
lowerCamelCase_ = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = [downloaded_paths]
lowerCamelCase_ = [urls]
elif isinstance(lowerCamelCase__ , lowerCamelCase__ ):
assert "train" in downloaded_paths.keys()
lowerCamelCase_ = downloaded_paths.values()
lowerCamelCase_ = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(lowerCamelCase__ , lowerCamelCase__ ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
lowerCamelCase_ = Path(lowerCamelCase__ )
lowerCamelCase_ = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
lowerCamelCase_ = downloaded_path.read_text()
assert content == CONTENT
lowerCamelCase_ = downloaded_path.with_suffix(".json" )
assert metadata_downloaded_path.exists()
lowerCamelCase_ = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize("paths_type" , [str, list, dict] )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = str(lowerCamelCase__ )
if issubclass(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = filename
elif issubclass(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = [filename]
elif issubclass(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = {"train": filename}
lowerCamelCase_ = "dummy"
lowerCamelCase_ = xz_file.parent
lowerCamelCase_ = "extracted"
lowerCamelCase_ = DownloadConfig(
cache_dir=lowerCamelCase__ , use_etag=lowerCamelCase__ , )
lowerCamelCase_ = DownloadManager(dataset_name=lowerCamelCase__ , download_config=lowerCamelCase__ )
lowerCamelCase_ = dl_manager.extract(lowerCamelCase__ )
lowerCamelCase_ = paths
for extracted_paths in [extracted_paths]:
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = [extracted_paths]
lowerCamelCase_ = [paths]
elif isinstance(lowerCamelCase__ , lowerCamelCase__ ):
assert "train" in extracted_paths.keys()
lowerCamelCase_ = extracted_paths.values()
lowerCamelCase_ = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(lowerCamelCase__ , lowerCamelCase__ ):
assert extracted_path == dl_manager.extracted_paths[input_path]
lowerCamelCase_ = Path(lowerCamelCase__ )
lowerCamelCase_ = extracted_path.parts
assert parts[-1] == hash_url_to_filename(lowerCamelCase__ , etag=lowerCamelCase__ )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
lowerCamelCase_ = extracted_path.read_text()
lowerCamelCase_ = text_file.read_text()
assert extracted_file_content == expected_file_content
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
assert path.endswith(".jsonl" )
for num_items, line in enumerate(lowerCamelCase__ , start=1 ):
lowerCamelCase_ = json.loads(line.decode("utf-8" ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize("archive_jsonl" , ["tar_jsonl_path", "zip_jsonl_path"] )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = request.getfixturevalue(lowerCamelCase__ )
lowerCamelCase_ = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(lowerCamelCase__ ) , start=1 ):
_test_jsonl(lowerCamelCase__ , lowerCamelCase__ )
assert num_jsonl == 2
@pytest.mark.parametrize("archive_nested_jsonl" , ["tar_nested_jsonl_path", "zip_nested_jsonl_path"] )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = request.getfixturevalue(lowerCamelCase__ )
lowerCamelCase_ = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(lowerCamelCase__ ) , start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(lowerCamelCase__ ) , start=1 ):
_test_jsonl(lowerCamelCase__ , lowerCamelCase__ )
assert num_tar == 1
assert num_jsonl == 2
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(lowerCamelCase__ ) , start=1 ):
assert os.path.basename(lowerCamelCase__ ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 463
| 1
|
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a_ :Any = re.compile(r'\b(a|an|the)\b', re.UNICODE)
a_ :List[str] = None
def a ( ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = 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=A__ , 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=A__ , 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 ( A__ ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = bool(qa['''answers''']['''text'''] )
return qid_to_has_ans
def a ( A__ ) -> List[str]:
'''simple docstring'''
def remove_articles(A__ ):
return ARTICLES_REGEX.sub(''' ''' , A__ )
def white_space_fix(A__ ):
return " ".join(text.split() )
def remove_punc(A__ ):
SCREAMING_SNAKE_CASE__ : Any = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(A__ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(A__ ) ) ) )
def a ( A__ ) -> str:
'''simple docstring'''
if not s:
return []
return normalize_answer(A__ ).split()
def a ( A__ , A__ ) -> Tuple:
'''simple docstring'''
return int(normalize_answer(A__ ) == normalize_answer(A__ ) )
def a ( A__ , A__ ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_tokens(A__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_tokens(A__ )
SCREAMING_SNAKE_CASE__ : str = collections.Counter(A__ ) & collections.Counter(A__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = sum(common.values() )
if len(A__ ) == 0 or len(A__ ) == 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
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1.0 * num_same / len(A__ )
SCREAMING_SNAKE_CASE__ : List[Any] = 1.0 * num_same / len(A__ )
SCREAMING_SNAKE_CASE__ : List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def a ( A__ , A__ ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = {}
SCREAMING_SNAKE_CASE__ : Dict = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = qa['''id''']
SCREAMING_SNAKE_CASE__ : int = [t for t in qa['''answers''']['''text'''] if normalize_answer(A__ )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ['''''']
if qid not in preds:
print(f"""Missing prediction for {qid}""" )
continue
SCREAMING_SNAKE_CASE__ : Optional[int] = preds[qid]
# Take max over all gold answers
SCREAMING_SNAKE_CASE__ : List[str] = max(compute_exact(A__ , A__ ) for a in gold_answers )
SCREAMING_SNAKE_CASE__ : Optional[int] = max(compute_fa(A__ , A__ ) for a in gold_answers )
return exact_scores, fa_scores
def a ( A__ , A__ , A__ , A__ ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = {}
for qid, s in scores.items():
SCREAMING_SNAKE_CASE__ : Optional[Any] = na_probs[qid] > na_prob_thresh
if pred_na:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = float(not qid_to_has_ans[qid] )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = s
return new_scores
def a ( A__ , A__ , A__=None ) -> Union[str, Any]:
'''simple docstring'''
if not qid_list:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = len(A__ )
return collections.OrderedDict(
[
('''exact''', 1_0_0.0 * sum(exact_scores.values() ) / total),
('''f1''', 1_0_0.0 * sum(fa_scores.values() ) / total),
('''total''', total),
] )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = len(A__ )
return collections.OrderedDict(
[
('''exact''', 1_0_0.0 * sum(exact_scores[k] for k in qid_list ) / total),
('''f1''', 1_0_0.0 * sum(fa_scores[k] for k in qid_list ) / total),
('''total''', total),
] )
def a ( A__ , A__ , A__ ) -> Optional[Any]:
'''simple docstring'''
for k in new_eval:
SCREAMING_SNAKE_CASE__ : Optional[int] = new_eval[k]
def a ( A__ , A__ , A__ , A__ ) -> Union[str, Any]:
'''simple docstring'''
plt.step(A__ , A__ , color='''b''' , alpha=0.2 , where='''post''' )
plt.fill_between(A__ , A__ , 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(A__ )
plt.savefig(A__ )
plt.clf()
def a ( A__ , A__ , A__ , A__ , A__=None , A__=None ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = sorted(A__ , key=lambda A__ : na_probs[k] )
SCREAMING_SNAKE_CASE__ : int = 0.0
SCREAMING_SNAKE_CASE__ : int = 1.0
SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0
SCREAMING_SNAKE_CASE__ : Tuple = [1.0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [0.0]
SCREAMING_SNAKE_CASE__ : Any = 0.0
for i, qid in enumerate(A__ ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
SCREAMING_SNAKE_CASE__ : List[str] = true_pos / float(i + 1 )
SCREAMING_SNAKE_CASE__ : List[str] = true_pos / float(A__ )
if i == len(A__ ) - 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(A__ )
recalls.append(A__ )
if out_image:
plot_pr_curve(A__ , A__ , A__ , A__ )
return {"ap": 1_0_0.0 * avg_prec}
def a ( A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
'''simple docstring'''
if out_image_dir and not os.path.exists(A__ ):
os.makedirs(A__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
SCREAMING_SNAKE_CASE__ : List[str] = make_precision_recall_eval(
A__ , A__ , A__ , A__ , out_image=os.path.join(A__ , '''pr_exact.png''' ) , title='''Precision-Recall curve for Exact Match score''' , )
SCREAMING_SNAKE_CASE__ : List[Any] = make_precision_recall_eval(
A__ , A__ , A__ , A__ , out_image=os.path.join(A__ , '''pr_f1.png''' ) , title='''Precision-Recall curve for F1 score''' , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {k: float(A__ ) for k, v in qid_to_has_ans.items()}
SCREAMING_SNAKE_CASE__ : List[str] = make_precision_recall_eval(
A__ , A__ , A__ , A__ , out_image=os.path.join(A__ , '''pr_oracle.png''' ) , title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)''' , )
merge_eval(A__ , A__ , '''pr_exact''' )
merge_eval(A__ , A__ , '''pr_f1''' )
merge_eval(A__ , A__ , '''pr_oracle''' )
def a ( A__ , A__ , A__ , A__ ) -> Optional[int]:
'''simple docstring'''
if not qid_list:
return
SCREAMING_SNAKE_CASE__ : str = [na_probs[k] for k in qid_list]
SCREAMING_SNAKE_CASE__ : int = np.ones_like(A__ ) / float(len(A__ ) )
plt.hist(A__ , weights=A__ , bins=2_0 , 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(A__ , f"""na_prob_hist_{name}.png""" ) )
plt.clf()
def a ( A__ , A__ , A__ , A__ ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
SCREAMING_SNAKE_CASE__ : Any = num_no_ans
SCREAMING_SNAKE_CASE__ : Union[str, Any] = cur_score
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
SCREAMING_SNAKE_CASE__ : int = sorted(A__ , key=lambda A__ : na_probs[k] )
for i, qid in enumerate(A__ ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
SCREAMING_SNAKE_CASE__ : Tuple = scores[qid]
else:
if preds[qid]:
SCREAMING_SNAKE_CASE__ : Any = -1
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0
cur_score += diff
if cur_score > best_score:
SCREAMING_SNAKE_CASE__ : Tuple = cur_score
SCREAMING_SNAKE_CASE__ : Any = na_probs[qid]
return 1_0_0.0 * best_score / len(A__ ), best_thresh
def a ( A__ , A__ , A__ , A__ , A__ , A__ ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = find_best_thresh(A__ , A__ , A__ , A__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = find_best_thresh(A__ , A__ , A__ , A__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = best_exact
SCREAMING_SNAKE_CASE__ : Optional[Any] = exact_thresh
SCREAMING_SNAKE_CASE__ : str = best_fa
SCREAMING_SNAKE_CASE__ : Tuple = fa_thresh
def a ( ) -> List[Any]:
'''simple docstring'''
with open(OPTS.data_file ) as f:
SCREAMING_SNAKE_CASE__ : Tuple = json.load(A__ )
SCREAMING_SNAKE_CASE__ : List[Any] = dataset_json['''data''']
with open(OPTS.pred_file ) as f:
SCREAMING_SNAKE_CASE__ : Optional[int] = json.load(A__ )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = json.load(A__ )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = {k: 0.0 for k in preds}
SCREAMING_SNAKE_CASE__ : List[str] = make_qid_to_has_ans(A__ ) # maps qid to True/False
SCREAMING_SNAKE_CASE__ : Optional[Any] = [k for k, v in qid_to_has_ans.items() if v]
SCREAMING_SNAKE_CASE__ : List[Any] = [k for k, v in qid_to_has_ans.items() if not v]
SCREAMING_SNAKE_CASE__ : int = get_raw_scores(A__ , A__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = apply_no_ans_threshold(A__ , A__ , A__ , OPTS.na_prob_thresh )
SCREAMING_SNAKE_CASE__ : Dict = apply_no_ans_threshold(A__ , A__ , A__ , OPTS.na_prob_thresh )
SCREAMING_SNAKE_CASE__ : List[Any] = make_eval_dict(A__ , A__ )
if has_ans_qids:
SCREAMING_SNAKE_CASE__ : Optional[Any] = make_eval_dict(A__ , A__ , qid_list=A__ )
merge_eval(A__ , A__ , '''HasAns''' )
if no_ans_qids:
SCREAMING_SNAKE_CASE__ : int = make_eval_dict(A__ , A__ , qid_list=A__ )
merge_eval(A__ , A__ , '''NoAns''' )
if OPTS.na_prob_file:
find_all_best_thresh(A__ , A__ , A__ , A__ , A__ , A__ )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(A__ , A__ , A__ , A__ , A__ , OPTS.out_image_dir )
histogram_na_prob(A__ , A__ , OPTS.out_image_dir , '''hasAns''' )
histogram_na_prob(A__ , A__ , OPTS.out_image_dir , '''noAns''' )
if OPTS.out_file:
with open(OPTS.out_file , '''w''' ) as f:
json.dump(A__ , A__ )
else:
print(json.dumps(A__ , indent=2 ) )
if __name__ == "__main__":
a_ :Dict = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
main()
| 700
|
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowercase :
def __init__( self : List[str] , _lowercase : List[Any] , _lowercase : int=13 , _lowercase : Any=7 , _lowercase : Tuple=True , _lowercase : Union[str, Any]=True , _lowercase : List[str]=True , _lowercase : Optional[Any]=True , _lowercase : List[Any]=99 , _lowercase : List[str]=16 , _lowercase : List[Any]=36 , _lowercase : Any=6 , _lowercase : int=6 , _lowercase : str=6 , _lowercase : List[str]=37 , _lowercase : List[Any]="gelu" , _lowercase : Union[str, Any]=0.1 , _lowercase : List[str]=0.1 , _lowercase : List[Any]=5_12 , _lowercase : List[str]=16 , _lowercase : Optional[int]=2 , _lowercase : List[str]=0.02 , _lowercase : Union[str, Any]=3 , _lowercase : Optional[Any]=4 , _lowercase : Any=None , ):
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Any = batch_size
SCREAMING_SNAKE_CASE__ : List[Any] = seq_length
SCREAMING_SNAKE_CASE__ : str = is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_input_mask
SCREAMING_SNAKE_CASE__ : List[str] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : List[str] = use_labels
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_size
SCREAMING_SNAKE_CASE__ : int = embedding_size
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_hidden_groups
SCREAMING_SNAKE_CASE__ : str = num_attention_heads
SCREAMING_SNAKE_CASE__ : Union[str, Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] = type_vocab_size
SCREAMING_SNAKE_CASE__ : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Any = initializer_range
SCREAMING_SNAKE_CASE__ : str = num_labels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_choices
SCREAMING_SNAKE_CASE__ : Optional[int] = scope
def lowercase__ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : List[Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Optional[int] = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Dict = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : int = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase__ ( self : Any ):
return AlbertConfig(
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 , num_hidden_groups=self.num_hidden_groups , )
def lowercase__ ( self : Optional[int] , _lowercase : Tuple , _lowercase : List[str] , _lowercase : List[str] , _lowercase : List[Any] , _lowercase : Any , _lowercase : Optional[Any] , _lowercase : Any ):
SCREAMING_SNAKE_CASE__ : Dict = AlbertModel(config=_lowercase )
model.to(_lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase )
SCREAMING_SNAKE_CASE__ : str = model(_lowercase , token_type_ids=_lowercase )
SCREAMING_SNAKE_CASE__ : int = model(_lowercase )
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 : Optional[Any] , _lowercase : str , _lowercase : Dict , _lowercase : Any , _lowercase : Optional[Any] , _lowercase : Tuple , _lowercase : Optional[Any] , _lowercase : int ):
SCREAMING_SNAKE_CASE__ : Any = AlbertForPreTraining(config=_lowercase )
model.to(_lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(
_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase , sentence_order_label=_lowercase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def lowercase__ ( self : str , _lowercase : Tuple , _lowercase : Tuple , _lowercase : Optional[Any] , _lowercase : List[Any] , _lowercase : List[Any] , _lowercase : Optional[int] , _lowercase : Dict ):
SCREAMING_SNAKE_CASE__ : List[str] = AlbertForMaskedLM(config=_lowercase )
model.to(_lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : int = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase__ ( self : Optional[int] , _lowercase : List[str] , _lowercase : Optional[int] , _lowercase : Optional[int] , _lowercase : List[str] , _lowercase : Union[str, Any] , _lowercase : Any , _lowercase : str ):
SCREAMING_SNAKE_CASE__ : Any = AlbertForQuestionAnswering(config=_lowercase )
model.to(_lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Any = model(
_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , start_positions=_lowercase , end_positions=_lowercase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowercase__ ( self : Optional[Any] , _lowercase : str , _lowercase : Optional[Any] , _lowercase : Any , _lowercase : str , _lowercase : Optional[Any] , _lowercase : int , _lowercase : List[str] ):
SCREAMING_SNAKE_CASE__ : Any = self.num_labels
SCREAMING_SNAKE_CASE__ : Any = AlbertForSequenceClassification(_lowercase )
model.to(_lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : int = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase__ ( self : Dict , _lowercase : str , _lowercase : Tuple , _lowercase : int , _lowercase : List[Any] , _lowercase : List[str] , _lowercase : Tuple , _lowercase : List[str] ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.num_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] = AlbertForTokenClassification(config=_lowercase )
model.to(_lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : str = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase__ ( self : str , _lowercase : str , _lowercase : Union[str, Any] , _lowercase : str , _lowercase : List[Any] , _lowercase : int , _lowercase : List[str] , _lowercase : int ):
SCREAMING_SNAKE_CASE__ : Optional[int] = self.num_choices
SCREAMING_SNAKE_CASE__ : Dict = AlbertForMultipleChoice(config=_lowercase )
model.to(_lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Optional[int] = model(
_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowercase__ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE__ : Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class lowercase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowerCamelCase : Dict = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase : Tuple = (
{
'''feature-extraction''': AlbertModel,
'''fill-mask''': AlbertForMaskedLM,
'''question-answering''': AlbertForQuestionAnswering,
'''text-classification''': AlbertForSequenceClassification,
'''token-classification''': AlbertForTokenClassification,
'''zero-shot''': AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase : str = True
def lowercase__ ( self : Any , _lowercase : Any , _lowercase : Tuple , _lowercase : int=False ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = super()._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
if return_labels:
if model_class in get_values(_lowercase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_lowercase )
SCREAMING_SNAKE_CASE__ : Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_lowercase )
return inputs_dict
def lowercase__ ( self : str ):
SCREAMING_SNAKE_CASE__ : Dict = AlbertModelTester(self )
SCREAMING_SNAKE_CASE__ : str = ConfigTester(self , config_class=_lowercase , hidden_size=37 )
def lowercase__ ( self : Optional[int] ):
self.config_tester.run_common_tests()
def lowercase__ ( self : str ):
SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def lowercase__ ( self : str ):
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_lowercase )
def lowercase__ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowercase )
def lowercase__ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_lowercase )
def lowercase__ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowercase )
def lowercase__ ( self : str ):
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowercase )
def lowercase__ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = type
self.model_tester.create_and_check_model(*_lowercase )
@slow
def lowercase__ ( self : Union[str, Any] ):
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : List[Any] = AlbertModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
@require_torch
class lowercase ( unittest.TestCase ):
@slow
def lowercase__ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ : Any = AlbertModel.from_pretrained('''albert-base-v2''' )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Any = model(_lowercase , attention_mask=_lowercase )[0]
SCREAMING_SNAKE_CASE__ : Tuple = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , _lowercase )
SCREAMING_SNAKE_CASE__ : str = torch.tensor(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowercase , atol=1E-4 ) )
| 250
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class __SCREAMING_SNAKE_CASE ( UpperCamelCase):
"""simple docstring"""
__UpperCAmelCase = "canine"
def __init__( self , _UpperCAmelCase=768 , _UpperCAmelCase=12 , _UpperCAmelCase=12 , _UpperCAmelCase=3_072 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=16_384 , _UpperCAmelCase=16 , _UpperCAmelCase=0.02 , _UpperCAmelCase=1E-12 , _UpperCAmelCase=0 , _UpperCAmelCase=0xE000 , _UpperCAmelCase=0xE001 , _UpperCAmelCase=4 , _UpperCAmelCase=4 , _UpperCAmelCase=8 , _UpperCAmelCase=16_384 , _UpperCAmelCase=128 , **_UpperCAmelCase , ):
super().__init__(pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
__snake_case : Optional[Any] = max_position_embeddings
__snake_case : Tuple = hidden_size
__snake_case : Optional[int] = num_hidden_layers
__snake_case : int = num_attention_heads
__snake_case : List[str] = intermediate_size
__snake_case : Optional[int] = hidden_act
__snake_case : List[Any] = hidden_dropout_prob
__snake_case : Any = attention_probs_dropout_prob
__snake_case : List[Any] = initializer_range
__snake_case : Tuple = type_vocab_size
__snake_case : Tuple = layer_norm_eps
# Character config:
__snake_case : Any = downsampling_rate
__snake_case : str = upsampling_kernel_size
__snake_case : int = num_hash_functions
__snake_case : Dict = num_hash_buckets
__snake_case : List[Any] = local_transformer_stride
| 576
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"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:
_SCREAMING_SNAKE_CASE = [
"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
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 18
| 0
|
"""simple docstring"""
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed
lowercase__ = logging.getLogger(__name__)
def __magic_name__ ( _lowerCamelCase : Tuple=2 , _lowerCamelCase : Union[str, Any]=3 , _lowerCamelCase : Optional[int]=1_6 , _lowerCamelCase : int = 1_0 , _lowerCamelCase : int = 2 ):
def get_dataset(_lowerCamelCase : str ):
__a : Tuple = torch.randn(batch_size * n_batches , 1 )
return TensorDataset(_lowerCamelCase , a * x + b + 0.1 * torch.randn(batch_size * n_batches , 1 ) )
__a : List[str] = get_dataset(_lowerCamelCase )
__a : int = get_dataset(_lowerCamelCase )
__a : Tuple = DataLoader(_lowerCamelCase , shuffle=_lowerCamelCase , batch_size=_lowerCamelCase , num_workers=4 )
__a : str = DataLoader(_lowerCamelCase , shuffle=_lowerCamelCase , batch_size=_lowerCamelCase , num_workers=4 )
return (train_dataloader, valid_dataloader)
def __magic_name__ ( _lowerCamelCase : Tuple , _lowerCamelCase : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : Tuple , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[Any]=None ):
__a : Dict = []
for epoch in range(_lowerCamelCase ):
# Train quickly
model.train()
for batch in dataloader:
__a : Tuple = batch
__a : Union[str, Any] = model(_lowerCamelCase )
__a : int = torch.nn.functional.mse_loss(_lowerCamelCase , _lowerCamelCase )
accelerator.backward(_lowerCamelCase )
optimizer.step()
optimizer.zero_grad()
rands.append(random.random() ) # Introduce some randomness
if scheduler is not None:
scheduler.step()
return rands
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__(self ):
'''simple docstring'''
super().__init__()
__a : int = nn.Parameter(torch.randn(1 ) )
__a : Any = nn.Parameter(torch.randn(1 ) )
def lowerCAmelCase__(self , _lowercase ):
'''simple docstring'''
return x * self.a + self.b
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def lowerCAmelCase__(self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__a : Optional[int] = DummyModel()
__a : List[str] = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
__a : Optional[int] = dummy_dataloaders()
__a : List[str] = ProjectConfiguration(total_limit=1 , project_dir=_lowercase , automatic_checkpoint_naming=_lowercase )
# Train baseline
__a : str = Accelerator(project_config=_lowercase )
__a : Optional[int] = accelerator.prepare(
_lowercase , _lowercase , _lowercase , _lowercase )
# Save initial
accelerator.save_state()
# Save second state
accelerator.save_state()
self.assertEqual(len(os.listdir(accelerator.project_dir ) ) , 1 )
def lowerCAmelCase__(self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__a : Optional[int] = DummyModel()
__a : List[str] = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
__a : Dict = dummy_dataloaders()
# Train baseline
__a : Dict = Accelerator()
__a : Dict = accelerator.prepare(
_lowercase , _lowercase , _lowercase , _lowercase )
# Save initial
__a : Tuple = os.path.join(_lowercase , """initial""" )
accelerator.save_state(_lowercase )
(__a) : Tuple = model.a.item(), model.b.item()
__a : Optional[int] = optimizer.state_dict()
__a : Tuple = train(3 , _lowercase , _lowercase , _lowercase , _lowercase )
(__a) : List[Any] = model.a.item(), model.b.item()
__a : List[str] = optimizer.state_dict()
# Train partially
set_seed(42 )
__a : Tuple = DummyModel()
__a : Any = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
__a : int = dummy_dataloaders()
__a : Optional[Any] = Accelerator()
__a : List[Any] = accelerator.prepare(
_lowercase , _lowercase , _lowercase , _lowercase )
accelerator.load_state(_lowercase )
(__a) : Union[str, Any] = model.a.item(), model.b.item()
__a : List[Any] = optimizer.state_dict()
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
__a : Optional[Any] = train(2 , _lowercase , _lowercase , _lowercase , _lowercase )
# Save everything
__a : Optional[Any] = os.path.join(_lowercase , """checkpoint""" )
accelerator.save_state(_lowercase )
# Load everything back in and make sure all states work
accelerator.load_state(_lowercase )
test_rands += train(1 , _lowercase , _lowercase , _lowercase , _lowercase )
(__a) : str = model.a.item(), model.b.item()
__a : int = optimizer.state_dict()
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
def lowerCAmelCase__(self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__a : List[Any] = DummyModel()
__a : Any = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
__a : List[str] = dummy_dataloaders()
__a : Optional[Any] = ProjectConfiguration(automatic_checkpoint_naming=_lowercase )
# Train baseline
__a : List[str] = Accelerator(project_dir=_lowercase , project_config=_lowercase )
__a : Union[str, Any] = accelerator.prepare(
_lowercase , _lowercase , _lowercase , _lowercase )
# Save initial
accelerator.save_state()
(__a) : Dict = model.a.item(), model.b.item()
__a : Optional[Any] = optimizer.state_dict()
__a : str = train(3 , _lowercase , _lowercase , _lowercase , _lowercase )
(__a) : Union[str, Any] = model.a.item(), model.b.item()
__a : int = optimizer.state_dict()
# Train partially
set_seed(42 )
__a : Union[str, Any] = DummyModel()
__a : List[Any] = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
__a : List[Any] = dummy_dataloaders()
__a : int = ProjectConfiguration(iteration=1 , automatic_checkpoint_naming=_lowercase )
__a : str = Accelerator(project_dir=_lowercase , project_config=_lowercase )
__a : Dict = accelerator.prepare(
_lowercase , _lowercase , _lowercase , _lowercase )
accelerator.load_state(os.path.join(_lowercase , """checkpoints""" , """checkpoint_0""" ) )
(__a) : Optional[Any] = model.a.item(), model.b.item()
__a : Union[str, Any] = optimizer.state_dict()
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
__a : Dict = train(2 , _lowercase , _lowercase , _lowercase , _lowercase )
# Save everything
accelerator.save_state()
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(_lowercase , """checkpoints""" , """checkpoint_1""" ) )
test_rands += train(1 , _lowercase , _lowercase , _lowercase , _lowercase )
(__a) : int = model.a.item(), model.b.item()
__a : str = optimizer.state_dict()
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : str = torch.tensor([1, 2, 3] )
__a : Any = torch.tensor([2, 3, 4] )
__a : List[str] = DummyModel()
__a : Optional[Any] = torch.optim.Adam(net.parameters() )
__a : List[str] = Accelerator()
with self.assertRaises(_lowercase ) as ve:
accelerator.register_for_checkpointing(_lowercase , _lowercase , _lowercase , _lowercase )
__a : List[Any] = str(ve.exception )
self.assertTrue("""Item at index 0""" in message )
self.assertTrue("""Item at index 1""" in message )
self.assertFalse("""Item at index 2""" in message )
self.assertFalse("""Item at index 3""" in message )
def lowerCAmelCase__(self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__a : int = DummyModel()
__a : int = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
__a : Tuple = torch.optim.lr_scheduler.StepLR(_lowercase , step_size=1 , gamma=0.99 )
__a : Optional[Any] = dummy_dataloaders()
__a : Optional[int] = ProjectConfiguration(automatic_checkpoint_naming=_lowercase )
# Train baseline
__a : List[Any] = Accelerator(project_dir=_lowercase , project_config=_lowercase )
__a : Union[str, Any] = accelerator.prepare(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
# Save initial
accelerator.save_state()
__a : Optional[int] = scheduler.state_dict()
train(3 , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
self.assertNotEqual(_lowercase , scheduler.state_dict() )
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(_lowercase , """checkpoints""" , """checkpoint_0""" ) )
self.assertEqual(_lowercase , scheduler.state_dict() )
def lowerCAmelCase__(self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__a : Dict = DummyModel()
__a : Dict = ProjectConfiguration(automatic_checkpoint_naming=_lowercase , total_limit=2 )
# Train baseline
__a : Tuple = Accelerator(project_dir=_lowercase , project_config=_lowercase )
__a : str = accelerator.prepare(_lowercase )
# Save 3 states:
for _ in range(11 ):
accelerator.save_state()
self.assertTrue(not os.path.exists(os.path.join(_lowercase , """checkpoints""" , """checkpoint_0""" ) ) )
self.assertTrue(os.path.exists(os.path.join(_lowercase , """checkpoints""" , """checkpoint_9""" ) ) )
self.assertTrue(os.path.exists(os.path.join(_lowercase , """checkpoints""" , """checkpoint_10""" ) ) )
@require_cuda
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : List[Any] = ["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
execute_subprocess_async(_lowercase , env=os.environ.copy() )
if __name__ == "__main__":
lowercase__ = "/tmp/accelerate/state_checkpointing"
lowercase__ = DummyModel()
lowercase__ = torch.optim.Adam(params=model.parameters(), lr=1e-3)
lowercase__ = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
lowercase__ , lowercase__ = dummy_dataloaders()
lowercase__ = ProjectConfiguration(automatic_checkpoint_naming=True)
# Train baseline
lowercase__ = Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="no")
if accelerator.process_index == 0:
if os.path.exists(savedir):
shutil.rmtree(savedir)
os.makedirs(savedir)
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader, scheduler
)
lowercase__ , lowercase__ = accelerator.prepare(model, optimizer)
train(3, model, train_dataloader, optimizer, accelerator, scheduler)
# Check that the intial optimizer is loaded on the GPU
for group in optimizer.param_groups:
lowercase__ = group["params"][0].device
break
assert param_device.type == accelerator.device.type
lowercase__ = model.cpu()
accelerator.wait_for_everyone()
accelerator.save_state()
accelerator.wait_for_everyone()
# Check CPU state
accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="cpu")
for group in optimizer.param_groups:
lowercase__ = group["params"][0].device
break
assert (
param_device.type == torch.device("cpu").type
), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"
# Check device state
model.to(accelerator.device)
accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="on_device")
for group in optimizer.param_groups:
lowercase__ = group["params"][0].device
break
assert (
param_device.type == accelerator.device.type
), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"
# Check error
with pytest.raises(TypeError, match="Unsupported optimizer map location passed"):
accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="invalid")
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
shutil.rmtree(savedir)
accelerator.wait_for_everyone()
| 705
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
lowercase__ = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ErnieForCausalLM",
"ErnieForMaskedLM",
"ErnieForMultipleChoice",
"ErnieForNextSentencePrediction",
"ErnieForPreTraining",
"ErnieForQuestionAnswering",
"ErnieForSequenceClassification",
"ErnieForTokenClassification",
"ErnieModel",
"ErniePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 63
| 0
|
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def a_ ( _A , _A=False ) -> int:
"""simple docstring"""
snake_case__ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''module.blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''module.blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''module.blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((f'''module.blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''module.blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''module.blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''module.blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''module.blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''module.blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''module.blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
('module.cls_token', 'vit.embeddings.cls_token'),
('module.patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
('module.patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
('module.pos_embed', 'vit.embeddings.position_embeddings'),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('module.norm.weight', 'layernorm.weight'),
('module.norm.bias', 'layernorm.bias'),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
snake_case__ = [(pair[0], pair[1][4:]) if pair[1].startswith('vit' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('norm.weight', 'vit.layernorm.weight'),
('norm.bias', 'vit.layernorm.bias'),
('head.weight', 'classifier.weight'),
('head.bias', 'classifier.bias'),
] )
return rename_keys
def a_ ( _A , _A , _A=False ) -> List[Any]:
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
snake_case__ = ''
else:
snake_case__ = 'vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case__ = state_dict.pop(f'''module.blocks.{i}.attn.qkv.weight''' )
snake_case__ = state_dict.pop(f'''module.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case__ = in_proj_weight[
: config.hidden_size, :
]
snake_case__ = in_proj_bias[: config.hidden_size]
snake_case__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case__ = in_proj_weight[
-config.hidden_size :, :
]
snake_case__ = in_proj_bias[-config.hidden_size :]
def a_ ( _A ) -> List[Any]:
"""simple docstring"""
snake_case__ = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(_A , _A )
def a_ ( _A ) -> Tuple:
"""simple docstring"""
# projection head is used in the self-supervised pre-training in MSN,
# for downstream task it's not needed.
snake_case__ = [
'module.fc.fc1.weight',
'module.fc.fc1.bias',
'module.fc.bn1.weight',
'module.fc.bn1.bias',
'module.fc.bn1.running_mean',
'module.fc.bn1.running_var',
'module.fc.bn1.num_batches_tracked',
'module.fc.fc2.weight',
'module.fc.fc2.bias',
'module.fc.bn2.weight',
'module.fc.bn2.bias',
'module.fc.bn2.running_mean',
'module.fc.bn2.running_var',
'module.fc.bn2.num_batches_tracked',
'module.fc.fc3.weight',
'module.fc.fc3.bias',
]
for k in ignore_keys:
state_dict.pop(_A , _A )
def a_ ( _A , _A , _A ) -> Optional[Any]:
"""simple docstring"""
snake_case__ = dct.pop(_A )
snake_case__ = val
def a_ ( _A , _A ) -> str:
"""simple docstring"""
snake_case__ = ViTMSNConfig()
snake_case__ = 1000
snake_case__ = 'datasets/huggingface/label-files'
snake_case__ = 'imagenet-1k-id2label.json'
snake_case__ = json.load(open(hf_hub_download(_A , _A ) , 'r' ) )
snake_case__ = {int(_A ): v for k, v in idalabel.items()}
snake_case__ = idalabel
snake_case__ = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
snake_case__ = 384
snake_case__ = 1536
snake_case__ = 6
elif "l16" in checkpoint_url:
snake_case__ = 1024
snake_case__ = 4096
snake_case__ = 24
snake_case__ = 16
snake_case__ = 0.1
elif "b4" in checkpoint_url:
snake_case__ = 4
elif "l7" in checkpoint_url:
snake_case__ = 7
snake_case__ = 1024
snake_case__ = 4096
snake_case__ = 24
snake_case__ = 16
snake_case__ = 0.1
snake_case__ = ViTMSNModel(_A )
snake_case__ = torch.hub.load_state_dict_from_url(_A , map_location='cpu' )['target_encoder']
snake_case__ = ViTImageProcessor(size=config.image_size )
remove_projection_head(_A )
snake_case__ = create_rename_keys(_A , base_model=_A )
for src, dest in rename_keys:
rename_key(_A , _A , _A )
read_in_q_k_v(_A , _A , base_model=_A )
model.load_state_dict(_A )
model.eval()
snake_case__ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
snake_case__ = Image.open(requests.get(_A , stream=_A ).raw )
snake_case__ = ViTImageProcessor(
size=config.image_size , image_mean=_A , image_std=_A )
snake_case__ = image_processor(images=_A , return_tensors='pt' )
# forward pass
torch.manual_seed(2 )
snake_case__ = model(**_A )
snake_case__ = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
snake_case__ = torch.tensor([[-1.0915, -1.4876, -1.1809]] )
elif "b16" in checkpoint_url:
snake_case__ = torch.tensor([[14.2889, -18.9045, 11.7281]] )
elif "l16" in checkpoint_url:
snake_case__ = torch.tensor([[41.5028, -22.8681, 45.6475]] )
elif "b4" in checkpoint_url:
snake_case__ = torch.tensor([[-4.3868, 5.2932, -0.4137]] )
else:
snake_case__ = torch.tensor([[-0.1792, -0.6465, 2.4263]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , _A , atol=1e-4 )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_A )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(_A )
if __name__ == "__main__":
__UpperCamelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar""",
type=str,
help="""URL of the checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
__UpperCamelCase : Optional[Any] = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 328
|
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __SCREAMING_SNAKE_CASE( a_ , unittest.TestCase ):
_UpperCAmelCase = PhobertTokenizer
_UpperCAmelCase = False
def lowerCAmelCase_ ( self: Dict ) -> Any:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case__ = ['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
snake_case__ = dict(zip(UpperCamelCase , range(len(UpperCamelCase ) ) ) )
snake_case__ = ['#version: 0.2', 'l à</w>']
snake_case__ = {'unk_token': '<unk>'}
snake_case__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
snake_case__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
for token in vocab_tokens:
fp.write(F'''{token} {vocab_tokens[token]}\n''' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(UpperCamelCase ) )
def lowerCAmelCase_ ( self: str , **UpperCamelCase: Dict ) -> int:
kwargs.update(self.special_tokens_map )
return PhobertTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase )
def lowerCAmelCase_ ( self: Dict , UpperCamelCase: Any ) -> Optional[int]:
snake_case__ = 'Tôi là VinAI Research'
snake_case__ = 'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
return input_text, output_text
def lowerCAmelCase_ ( self: Tuple ) -> Tuple:
snake_case__ = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case__ = 'Tôi là VinAI Research'
snake_case__ = 'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
snake_case__ = tokenizer.tokenize(UpperCamelCase )
print(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
snake_case__ = tokens + [tokenizer.unk_token]
snake_case__ = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase ) , UpperCamelCase )
| 328
| 1
|
def a_ (_lowerCAmelCase : int )-> int:
snake_case: List[Any] = abs(_lowerCAmelCase )
snake_case: Union[str, Any] = 0
while n > 0:
res += n % 10
n //= 10
return res
def a_ (_lowerCAmelCase : int )-> int:
snake_case: Tuple = abs(_lowerCAmelCase )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def a_ (_lowerCAmelCase : int )-> int:
return sum(int(_lowerCAmelCase ) for c in str(abs(_lowerCAmelCase ) ) )
def a_ ()-> None:
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(_lowerCAmelCase : Callable , _lowerCAmelCase : int ) -> None:
snake_case: Union[str, Any] = F"{func.__name__}({value})"
snake_case: List[str] = timeit(F"__main__.{call}" , setup="""import __main__""" )
print(F"{call:56} = {func(_lowerCAmelCase )} -- {timing:.4f} seconds" )
for value in (262144, 1125899906842624, 1267650600228229401496703205376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(_lowerCAmelCase , _lowerCAmelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 164
|
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
__lowerCAmelCase : List[str] = logging.get_logger(__name__)
__lowerCAmelCase : Union[str, Any] = {
'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class lowerCamelCase ( __snake_case ):
__lowerCamelCase = 'instructblip_vision_model'
def __init__( self , __lowerCamelCase=14_08 , __lowerCamelCase=61_44 , __lowerCamelCase=39 , __lowerCamelCase=16 , __lowerCamelCase=2_24 , __lowerCamelCase=14 , __lowerCamelCase="gelu" , __lowerCamelCase=1e-6 , __lowerCamelCase=0.0 , __lowerCamelCase=1e-10 , __lowerCamelCase=True , **__lowerCamelCase , ) -> Dict:
'''simple docstring'''
super().__init__(**__lowerCamelCase )
snake_case: Union[str, Any] = hidden_size
snake_case: List[str] = intermediate_size
snake_case: List[Any] = num_hidden_layers
snake_case: Optional[Any] = num_attention_heads
snake_case: str = patch_size
snake_case: Optional[int] = image_size
snake_case: str = initializer_range
snake_case: Tuple = attention_dropout
snake_case: List[str] = layer_norm_eps
snake_case: Dict = hidden_act
snake_case: Any = qkv_bias
@classmethod
def lowerCAmelCase_ ( cls , __lowerCamelCase , **__lowerCamelCase ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__lowerCamelCase )
snake_case , snake_case: Any = cls.get_config_dict(__lowerCamelCase , **__lowerCamelCase )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
snake_case: Dict = 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 lowerCamelCase ( __snake_case ):
__lowerCamelCase = 'instructblip_qformer'
def __init__( self , __lowerCamelCase=3_05_22 , __lowerCamelCase=7_68 , __lowerCamelCase=12 , __lowerCamelCase=12 , __lowerCamelCase=30_72 , __lowerCamelCase="gelu" , __lowerCamelCase=0.1 , __lowerCamelCase=0.1 , __lowerCamelCase=5_12 , __lowerCamelCase=0.02 , __lowerCamelCase=1e-12 , __lowerCamelCase=0 , __lowerCamelCase="absolute" , __lowerCamelCase=2 , __lowerCamelCase=14_08 , **__lowerCamelCase , ) -> str:
'''simple docstring'''
super().__init__(pad_token_id=__lowerCamelCase , **__lowerCamelCase )
snake_case: Optional[Any] = vocab_size
snake_case: Union[str, Any] = hidden_size
snake_case: str = num_hidden_layers
snake_case: Union[str, Any] = num_attention_heads
snake_case: List[str] = hidden_act
snake_case: Dict = intermediate_size
snake_case: List[Any] = hidden_dropout_prob
snake_case: Tuple = attention_probs_dropout_prob
snake_case: Tuple = max_position_embeddings
snake_case: Any = initializer_range
snake_case: List[str] = layer_norm_eps
snake_case: Dict = position_embedding_type
snake_case: Optional[Any] = cross_attention_frequency
snake_case: Optional[Any] = encoder_hidden_size
@classmethod
def lowerCAmelCase_ ( cls , __lowerCamelCase , **__lowerCamelCase ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__lowerCamelCase )
snake_case , snake_case: Union[str, Any] = cls.get_config_dict(__lowerCamelCase , **__lowerCamelCase )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
snake_case: List[Any] = 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(__lowerCamelCase , **__lowerCamelCase )
class lowerCamelCase ( __snake_case ):
__lowerCamelCase = 'instructblip'
__lowerCamelCase = True
def __init__( self , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=32 , **__lowerCamelCase ) -> List[Any]:
'''simple docstring'''
super().__init__(**__lowerCamelCase )
if vision_config is None:
snake_case: Any = {}
logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""" )
if qformer_config is None:
snake_case: List[Any] = {}
logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""" )
if text_config is None:
snake_case: List[Any] = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
snake_case: int = InstructBlipVisionConfig(**__lowerCamelCase )
snake_case: int = InstructBlipQFormerConfig(**__lowerCamelCase )
snake_case: int = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
snake_case: Optional[int] = CONFIG_MAPPING[text_model_type](**__lowerCamelCase )
snake_case: Optional[int] = self.text_config.tie_word_embeddings
snake_case: Any = self.text_config.is_encoder_decoder
snake_case: List[str] = num_query_tokens
snake_case: Any = self.vision_config.hidden_size
snake_case: Any = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
snake_case: Any = 1.0
snake_case: List[str] = 0.02
@classmethod
def lowerCAmelCase_ ( cls , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase , ) -> List[Any]:
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__lowerCamelCase , )
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
snake_case: str = copy.deepcopy(self.__dict__ )
snake_case: Any = self.vision_config.to_dict()
snake_case: Dict = self.qformer_config.to_dict()
snake_case: List[str] = self.text_config.to_dict()
snake_case: List[Any] = self.__class__.model_type
return output
| 164
| 1
|
"""simple docstring"""
from __future__ import annotations
def A_ ( __lowercase , __lowercase , __lowercase , __lowercase ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
UpperCamelCase_ , UpperCamelCase_ : int =array[indexa], array[indexa]
def A_ ( __lowercase , __lowercase , __lowercase , __lowercase ):
if length > 1:
UpperCamelCase_ : str =int(length / 2 )
for i in range(__lowercase , low + middle ):
comp_and_swap(__lowercase , __lowercase , i + middle , __lowercase )
bitonic_merge(__lowercase , __lowercase , __lowercase , __lowercase )
bitonic_merge(__lowercase , low + middle , __lowercase , __lowercase )
def A_ ( __lowercase , __lowercase , __lowercase , __lowercase ):
if length > 1:
UpperCamelCase_ : Union[str, Any] =int(length / 2 )
bitonic_sort(__lowercase , __lowercase , __lowercase , 1 )
bitonic_sort(__lowercase , low + middle , __lowercase , 0 )
bitonic_merge(__lowercase , __lowercase , __lowercase , __lowercase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = input('Enter numbers separated by a comma:\n').strip()
__SCREAMING_SNAKE_CASE = [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=', ')
| 357
|
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def A_ ( __lowercase , __lowercase , __lowercase = 1 , __lowercase = 1 , __lowercase = 1.0e4 , __lowercase = False , __lowercase = 1.0 , ):
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
UpperCamelCase_ : Optional[int] =float(embedding_dim // 2 )
UpperCamelCase_ : Optional[Any] =math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
UpperCamelCase_ : List[Any] =min_timescale * jnp.exp(jnp.arange(__lowercase , dtype=jnp.floataa ) * -log_timescale_increment )
UpperCamelCase_ : int =jnp.expand_dims(__lowercase , 1 ) * jnp.expand_dims(__lowercase , 0 )
# scale embeddings
UpperCamelCase_ : List[str] =scale * emb
if flip_sin_to_cos:
UpperCamelCase_ : Tuple =jnp.concatenate([jnp.cos(__lowercase ), jnp.sin(__lowercase )] , axis=1 )
else:
UpperCamelCase_ : Tuple =jnp.concatenate([jnp.sin(__lowercase ), jnp.cos(__lowercase )] , axis=1 )
UpperCamelCase_ : List[Any] =jnp.reshape(__lowercase , [jnp.shape(__lowercase )[0], embedding_dim] )
return signal
class a__ ( nn.Module ):
UpperCAmelCase__ = 32
UpperCAmelCase__ = jnp.floataa
@nn.compact
def __call__( self :Optional[Any] , _lowerCamelCase :List[str] ):
'''simple docstring'''
UpperCamelCase_ : Dict =nn.Dense(self.time_embed_dim , dtype=self.dtype , name='linear_1' )(_lowerCamelCase )
UpperCamelCase_ : Any =nn.silu(_lowerCamelCase )
UpperCamelCase_ : Tuple =nn.Dense(self.time_embed_dim , dtype=self.dtype , name='linear_2' )(_lowerCamelCase )
return temb
class a__ ( nn.Module ):
UpperCAmelCase__ = 32
UpperCAmelCase__ = False
UpperCAmelCase__ = 1
@nn.compact
def __call__( self :Union[str, Any] , _lowerCamelCase :Dict ):
'''simple docstring'''
return get_sinusoidal_embeddings(
_lowerCamelCase , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 357
| 1
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class lowercase ( unittest.TestCase):
'''simple docstring'''
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
SCREAMING_SNAKE_CASE : List[Any] = get_activation('gelu' )
self.assertTrue(torch.allclose(gelu_python(snake_case ) , torch_builtin(snake_case ) ) )
self.assertFalse(torch.allclose(gelu_python(snake_case ) , gelu_new(snake_case ) ) )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
SCREAMING_SNAKE_CASE : Tuple = get_activation('gelu' )
SCREAMING_SNAKE_CASE : Optional[int] = get_activation('gelu_10' )
SCREAMING_SNAKE_CASE : int = torch_builtin(snake_case )
SCREAMING_SNAKE_CASE : Any = geluaa(snake_case )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(snake_case ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
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(snake_case ):
get_activation('bogus' )
with self.assertRaises(snake_case ):
get_activation(snake_case )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = get_activation('gelu' )
SCREAMING_SNAKE_CASE : Optional[int] = 1
SCREAMING_SNAKE_CASE : Optional[int] = get_activation('gelu' )
self.assertEqual(acta.a , 1 )
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE : List[str] = acta.a
| 700
|
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
_lowerCamelCase : Dict = logging.get_logger(__name__)
@dataclass
class lowercase :
'''simple docstring'''
UpperCAmelCase : str = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys())})
UpperCAmelCase : str = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
UpperCAmelCase : int = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
UpperCAmelCase : bool = field(
default=SCREAMING_SNAKE_CASE_ , metadata={'help': 'Overwrite the cached training and evaluation sets'})
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.task_name.lower()
class lowercase ( SCREAMING_SNAKE_CASE_):
'''simple docstring'''
UpperCAmelCase : List[Any] = 'train'
UpperCAmelCase : Optional[Any] = 'dev'
UpperCAmelCase : Optional[int] = 'test'
class lowercase ( SCREAMING_SNAKE_CASE_):
'''simple docstring'''
UpperCAmelCase : GlueDataTrainingArguments
UpperCAmelCase : str
UpperCAmelCase : List[InputFeatures]
def __init__( self : Union[str, Any] , snake_case : GlueDataTrainingArguments , snake_case : PreTrainedTokenizerBase , snake_case : Optional[int] = None , snake_case : Union[str, Split] = Split.train , snake_case : Optional[str] = None , ):
'''simple docstring'''
warnings.warn(
'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
'library. You can have a look at this example script for pointers: '
'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py' , snake_case , )
SCREAMING_SNAKE_CASE : Tuple = args
SCREAMING_SNAKE_CASE : int = glue_processors[args.task_name]()
SCREAMING_SNAKE_CASE : str = glue_output_modes[args.task_name]
if isinstance(snake_case , snake_case ):
try:
SCREAMING_SNAKE_CASE : Any = Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name' )
# Load data features from cache or dataset file
SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}''' , )
SCREAMING_SNAKE_CASE : Any = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = label_list[2], label_list[1]
SCREAMING_SNAKE_CASE : List[Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
SCREAMING_SNAKE_CASE : Union[str, Any] = cached_features_file + '.lock'
with FileLock(snake_case ):
if os.path.exists(snake_case ) and not args.overwrite_cache:
SCREAMING_SNAKE_CASE : Optional[int] = time.time()
SCREAMING_SNAKE_CASE : int = torch.load(snake_case )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
else:
logger.info(f'''Creating features from dataset file at {args.data_dir}''' )
if mode == Split.dev:
SCREAMING_SNAKE_CASE : str = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
SCREAMING_SNAKE_CASE : Dict = self.processor.get_test_examples(args.data_dir )
else:
SCREAMING_SNAKE_CASE : str = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = examples[:limit_length]
SCREAMING_SNAKE_CASE : Optional[Any] = glue_convert_examples_to_features(
snake_case , snake_case , max_length=args.max_seq_length , label_list=snake_case , output_mode=self.output_mode , )
SCREAMING_SNAKE_CASE : Tuple = time.time()
torch.save(self.features , snake_case )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self : int ):
'''simple docstring'''
return len(self.features )
def __getitem__( self : Dict , snake_case : Optional[int] ):
'''simple docstring'''
return self.features[i]
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return self.label_list
| 308
| 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
from ..auto import CONFIG_MAPPING
UpperCamelCase =logging.get_logger(__name__)
UpperCamelCase ={
"microsoft/table-transformer-detection": (
"https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
),
}
class A ( _a ):
"""simple docstring"""
__a : Optional[int] = 'table-transformer'
__a : List[str] = ['past_key_values']
__a : List[Any] = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __lowerCAmelCase=True , __lowerCAmelCase=None , __lowerCAmelCase=3 , __lowerCAmelCase=1_00 , __lowerCAmelCase=6 , __lowerCAmelCase=20_48 , __lowerCAmelCase=8 , __lowerCAmelCase=6 , __lowerCAmelCase=20_48 , __lowerCAmelCase=8 , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.0 , __lowerCAmelCase=True , __lowerCAmelCase="relu" , __lowerCAmelCase=2_56 , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.02 , __lowerCAmelCase=1.0 , __lowerCAmelCase=False , __lowerCAmelCase="sine" , __lowerCAmelCase="resnet50" , __lowerCAmelCase=True , __lowerCAmelCase=False , __lowerCAmelCase=1 , __lowerCAmelCase=5 , __lowerCAmelCase=2 , __lowerCAmelCase=1 , __lowerCAmelCase=1 , __lowerCAmelCase=5 , __lowerCAmelCase=2 , __lowerCAmelCase=0.1 , **__lowerCAmelCase , ):
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCamelCase_ : int = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
UpperCamelCase_ : int = backbone_config.get("""model_type""" )
UpperCamelCase_ : int = CONFIG_MAPPING[backbone_model_type]
UpperCamelCase_ : Tuple = config_class.from_dict(__SCREAMING_SNAKE_CASE )
# set timm attributes to None
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ : Union[str, Any] = None, None, None
UpperCamelCase_ : Dict = use_timm_backbone
UpperCamelCase_ : Dict = backbone_config
UpperCamelCase_ : int = num_channels
UpperCamelCase_ : Optional[Any] = num_queries
UpperCamelCase_ : Any = d_model
UpperCamelCase_ : Dict = encoder_ffn_dim
UpperCamelCase_ : str = encoder_layers
UpperCamelCase_ : Tuple = encoder_attention_heads
UpperCamelCase_ : Tuple = decoder_ffn_dim
UpperCamelCase_ : Optional[Any] = decoder_layers
UpperCamelCase_ : Dict = decoder_attention_heads
UpperCamelCase_ : int = dropout
UpperCamelCase_ : Optional[int] = attention_dropout
UpperCamelCase_ : Dict = activation_dropout
UpperCamelCase_ : Optional[int] = activation_function
UpperCamelCase_ : Any = init_std
UpperCamelCase_ : List[str] = init_xavier_std
UpperCamelCase_ : Tuple = encoder_layerdrop
UpperCamelCase_ : str = decoder_layerdrop
UpperCamelCase_ : str = encoder_layers
UpperCamelCase_ : List[Any] = auxiliary_loss
UpperCamelCase_ : Any = position_embedding_type
UpperCamelCase_ : str = backbone
UpperCamelCase_ : List[Any] = use_pretrained_backbone
UpperCamelCase_ : int = dilation
# Hungarian matcher
UpperCamelCase_ : List[str] = class_cost
UpperCamelCase_ : Tuple = bbox_cost
UpperCamelCase_ : List[str] = giou_cost
# Loss coefficients
UpperCamelCase_ : List[Any] = mask_loss_coefficient
UpperCamelCase_ : Dict = dice_loss_coefficient
UpperCamelCase_ : str = bbox_loss_coefficient
UpperCamelCase_ : List[Any] = giou_loss_coefficient
UpperCamelCase_ : List[Any] = eos_coefficient
super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
@property
def _UpperCAmelCase ( self ):
return self.encoder_attention_heads
@property
def _UpperCAmelCase ( self ):
return self.d_model
class A ( _a ):
"""simple docstring"""
__a : Dict = version.parse('''1.11''' )
@property
def _UpperCAmelCase ( self ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _UpperCAmelCase ( self ):
return 1E-5
@property
def _UpperCAmelCase ( self ):
return 12
| 208
|
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self : Optional[int] ):
UpperCAmelCase = TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )
UpperCAmelCase = tf.convert_to_tensor(
[[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]] ,dtype=tf.intaa ,) # J'aime le camembert !"
UpperCAmelCase = model(__SCREAMING_SNAKE_CASE )["last_hidden_state"]
UpperCAmelCase = tf.TensorShape((1, 1_0, 7_6_8) )
self.assertEqual(output.shape ,__SCREAMING_SNAKE_CASE )
# compare the actual values for a slice.
UpperCAmelCase = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] ,dtype=tf.floataa ,)
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() ,expected_slice.numpy() ,atol=1e-4 ) )
| 333
| 0
|
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class snake_case :
"""simple docstring"""
def __init__( self, _lowercase, _lowercase ) -> Dict:
SCREAMING_SNAKE_CASE_ = question_encoder
SCREAMING_SNAKE_CASE_ = generator
SCREAMING_SNAKE_CASE_ = self.question_encoder
def a__ ( self, _lowercase ) -> int:
if os.path.isfile(_lowercase ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(_lowercase, exist_ok=_lowercase )
SCREAMING_SNAKE_CASE_ = os.path.join(_lowercase, 'question_encoder_tokenizer' )
SCREAMING_SNAKE_CASE_ = os.path.join(_lowercase, 'generator_tokenizer' )
self.question_encoder.save_pretrained(_lowercase )
self.generator.save_pretrained(_lowercase )
@classmethod
def a__ ( cls, _lowercase, **_lowercase ) -> List[str]:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
SCREAMING_SNAKE_CASE_ = kwargs.pop('config', _lowercase )
if config is None:
SCREAMING_SNAKE_CASE_ = RagConfig.from_pretrained(_lowercase )
SCREAMING_SNAKE_CASE_ = AutoTokenizer.from_pretrained(
_lowercase, config=config.question_encoder, subfolder='question_encoder_tokenizer' )
SCREAMING_SNAKE_CASE_ = AutoTokenizer.from_pretrained(
_lowercase, config=config.generator, subfolder='generator_tokenizer' )
return cls(question_encoder=_lowercase, generator=_lowercase )
def __call__( self, *_lowercase, **_lowercase ) -> Dict:
return self.current_tokenizer(*_lowercase, **_lowercase )
def a__ ( self, *_lowercase, **_lowercase ) -> Any:
return self.generator.batch_decode(*_lowercase, **_lowercase )
def a__ ( self, *_lowercase, **_lowercase ) -> Optional[Any]:
return self.generator.decode(*_lowercase, **_lowercase )
def a__ ( self ) -> int:
SCREAMING_SNAKE_CASE_ = self.question_encoder
def a__ ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.generator
def a__ ( self, _lowercase, _lowercase = None, _lowercase = None, _lowercase = None, _lowercase = "longest", _lowercase = None, _lowercase = True, **_lowercase, ) -> BatchEncoding:
warnings.warn(
'`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
'details', _lowercase, )
if max_length is None:
SCREAMING_SNAKE_CASE_ = self.current_tokenizer.model_max_length
SCREAMING_SNAKE_CASE_ = self(
_lowercase, add_special_tokens=_lowercase, return_tensors=_lowercase, max_length=_lowercase, padding=_lowercase, truncation=_lowercase, **_lowercase, )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
SCREAMING_SNAKE_CASE_ = self.current_tokenizer.model_max_length
SCREAMING_SNAKE_CASE_ = self(
text_target=_lowercase, add_special_tokens=_lowercase, return_tensors=_lowercase, padding=_lowercase, max_length=_lowercase, truncation=_lowercase, **_lowercase, )
SCREAMING_SNAKE_CASE_ = labels['input_ids']
return model_inputs
| 710
|
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : int = OrderedDict(
[
("audio-spectrogram-transformer", "ASTFeatureExtractor"),
("beit", "BeitFeatureExtractor"),
("chinese_clip", "ChineseCLIPFeatureExtractor"),
("clap", "ClapFeatureExtractor"),
("clip", "CLIPFeatureExtractor"),
("clipseg", "ViTFeatureExtractor"),
("conditional_detr", "ConditionalDetrFeatureExtractor"),
("convnext", "ConvNextFeatureExtractor"),
("cvt", "ConvNextFeatureExtractor"),
("data2vec-audio", "Wav2Vec2FeatureExtractor"),
("data2vec-vision", "BeitFeatureExtractor"),
("deformable_detr", "DeformableDetrFeatureExtractor"),
("deit", "DeiTFeatureExtractor"),
("detr", "DetrFeatureExtractor"),
("dinat", "ViTFeatureExtractor"),
("donut-swin", "DonutFeatureExtractor"),
("dpt", "DPTFeatureExtractor"),
("encodec", "EncodecFeatureExtractor"),
("flava", "FlavaFeatureExtractor"),
("glpn", "GLPNFeatureExtractor"),
("groupvit", "CLIPFeatureExtractor"),
("hubert", "Wav2Vec2FeatureExtractor"),
("imagegpt", "ImageGPTFeatureExtractor"),
("layoutlmv2", "LayoutLMv2FeatureExtractor"),
("layoutlmv3", "LayoutLMv3FeatureExtractor"),
("levit", "LevitFeatureExtractor"),
("maskformer", "MaskFormerFeatureExtractor"),
("mctct", "MCTCTFeatureExtractor"),
("mobilenet_v1", "MobileNetV1FeatureExtractor"),
("mobilenet_v2", "MobileNetV2FeatureExtractor"),
("mobilevit", "MobileViTFeatureExtractor"),
("nat", "ViTFeatureExtractor"),
("owlvit", "OwlViTFeatureExtractor"),
("perceiver", "PerceiverFeatureExtractor"),
("poolformer", "PoolFormerFeatureExtractor"),
("regnet", "ConvNextFeatureExtractor"),
("resnet", "ConvNextFeatureExtractor"),
("segformer", "SegformerFeatureExtractor"),
("sew", "Wav2Vec2FeatureExtractor"),
("sew-d", "Wav2Vec2FeatureExtractor"),
("speech_to_text", "Speech2TextFeatureExtractor"),
("speecht5", "SpeechT5FeatureExtractor"),
("swiftformer", "ViTFeatureExtractor"),
("swin", "ViTFeatureExtractor"),
("swinv2", "ViTFeatureExtractor"),
("table-transformer", "DetrFeatureExtractor"),
("timesformer", "VideoMAEFeatureExtractor"),
("tvlt", "TvltFeatureExtractor"),
("unispeech", "Wav2Vec2FeatureExtractor"),
("unispeech-sat", "Wav2Vec2FeatureExtractor"),
("van", "ConvNextFeatureExtractor"),
("videomae", "VideoMAEFeatureExtractor"),
("vilt", "ViltFeatureExtractor"),
("vit", "ViTFeatureExtractor"),
("vit_mae", "ViTFeatureExtractor"),
("vit_msn", "ViTFeatureExtractor"),
("wav2vec2", "Wav2Vec2FeatureExtractor"),
("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
("wavlm", "Wav2Vec2FeatureExtractor"),
("whisper", "WhisperFeatureExtractor"),
("xclip", "CLIPFeatureExtractor"),
("yolos", "YolosFeatureExtractor"),
]
)
SCREAMING_SNAKE_CASE : str = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def _UpperCamelCase ( lowerCAmelCase__: str ) -> Tuple:
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
SCREAMING_SNAKE_CASE_ = model_type_to_module_name(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ = importlib.import_module(F""".{module_name}""" ,'transformers.models' )
try:
return getattr(lowerCAmelCase__ ,lowerCAmelCase__ )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(lowerCAmelCase__ ,'__name__' ,lowerCAmelCase__ ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
SCREAMING_SNAKE_CASE_ = importlib.import_module('transformers' )
if hasattr(lowerCAmelCase__ ,lowerCAmelCase__ ):
return getattr(lowerCAmelCase__ ,lowerCAmelCase__ )
return None
def _UpperCamelCase ( lowerCAmelCase__: Union[str, os.PathLike] ,lowerCAmelCase__: Optional[Union[str, os.PathLike]] = None ,lowerCAmelCase__: bool = False ,lowerCAmelCase__: bool = False ,lowerCAmelCase__: Optional[Dict[str, str]] = None ,lowerCAmelCase__: Optional[Union[bool, str]] = None ,lowerCAmelCase__: Optional[str] = None ,lowerCAmelCase__: bool = False ,**lowerCAmelCase__: int ,) -> str:
SCREAMING_SNAKE_CASE_ = get_file_from_repo(
lowerCAmelCase__ ,lowerCAmelCase__ ,cache_dir=lowerCAmelCase__ ,force_download=lowerCAmelCase__ ,resume_download=lowerCAmelCase__ ,proxies=lowerCAmelCase__ ,use_auth_token=lowerCAmelCase__ ,revision=lowerCAmelCase__ ,local_files_only=lowerCAmelCase__ ,)
if resolved_config_file is None:
logger.info(
'Could not locate the feature extractor configuration file, will try to use the model config instead.' )
return {}
with open(lowerCAmelCase__ ,encoding='utf-8' ) as reader:
return json.load(lowerCAmelCase__ )
class snake_case :
"""simple docstring"""
def __init__( self ) -> str:
raise EnvironmentError(
'AutoFeatureExtractor is designed to be instantiated '
'using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.' )
@classmethod
@replace_list_option_in_docstrings(_lowercase )
def a__ ( cls, _lowercase, **_lowercase ) -> List[str]:
SCREAMING_SNAKE_CASE_ = kwargs.pop('config', _lowercase )
SCREAMING_SNAKE_CASE_ = kwargs.pop('trust_remote_code', _lowercase )
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = FeatureExtractionMixin.get_feature_extractor_dict(_lowercase, **_lowercase )
SCREAMING_SNAKE_CASE_ = config_dict.get('feature_extractor_type', _lowercase )
SCREAMING_SNAKE_CASE_ = None
if "AutoFeatureExtractor" in config_dict.get('auto_map', {} ):
SCREAMING_SNAKE_CASE_ = config_dict['auto_map']['AutoFeatureExtractor']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(_lowercase, _lowercase ):
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(_lowercase, **_lowercase )
# It could be in `config.feature_extractor_type``
SCREAMING_SNAKE_CASE_ = getattr(_lowercase, 'feature_extractor_type', _lowercase )
if hasattr(_lowercase, 'auto_map' ) and "AutoFeatureExtractor" in config.auto_map:
SCREAMING_SNAKE_CASE_ = config.auto_map['AutoFeatureExtractor']
if feature_extractor_class is not None:
SCREAMING_SNAKE_CASE_ = feature_extractor_class_from_name(_lowercase )
SCREAMING_SNAKE_CASE_ = feature_extractor_auto_map is not None
SCREAMING_SNAKE_CASE_ = feature_extractor_class is not None or type(_lowercase ) in FEATURE_EXTRACTOR_MAPPING
SCREAMING_SNAKE_CASE_ = resolve_trust_remote_code(
_lowercase, _lowercase, _lowercase, _lowercase )
if has_remote_code and trust_remote_code:
SCREAMING_SNAKE_CASE_ = get_class_from_dynamic_module(
_lowercase, _lowercase, **_lowercase )
SCREAMING_SNAKE_CASE_ = kwargs.pop('code_revision', _lowercase )
if os.path.isdir(_lowercase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(_lowercase, **_lowercase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(_lowercase, **_lowercase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(_lowercase ) in FEATURE_EXTRACTOR_MAPPING:
SCREAMING_SNAKE_CASE_ = FEATURE_EXTRACTOR_MAPPING[type(_lowercase )]
return feature_extractor_class.from_dict(_lowercase, **_lowercase )
raise ValueError(
f"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
f"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
f"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}""" )
@staticmethod
def a__ ( _lowercase, _lowercase ) -> Tuple:
FEATURE_EXTRACTOR_MAPPING.register(_lowercase, _lowercase )
| 238
| 0
|
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_A = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25,
"""L""": 4.03,
"""C""": 2.78,
"""U""": 2.76,
"""M""": 2.41,
"""W""": 2.36,
"""F""": 2.23,
"""G""": 2.02,
"""Y""": 1.97,
"""P""": 1.93,
"""B""": 1.29,
"""V""": 0.98,
"""K""": 0.77,
"""J""": 0.15,
"""X""": 0.15,
"""Q""": 0.10,
"""Z""": 0.07,
}
_A = """ETAOINSHRDLCUMWFGYPBVKJXQZ"""
_A = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def lowercase_ ( __UpperCAmelCase ) -> dict[str, int]:
lowerCAmelCase__ : str = {letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def lowercase_ ( __UpperCAmelCase ) -> str:
return x[0]
def lowercase_ ( __UpperCAmelCase ) -> str:
lowerCAmelCase__ : Optional[int] = get_letter_count(__UpperCAmelCase )
lowerCAmelCase__ : dict[int, list[str]] = {
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(__UpperCAmelCase )
lowerCAmelCase__ : dict[int, str] = {}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=__UpperCAmelCase )
lowerCAmelCase__ : List[str] = """""".join(freq_to_letter[freq] )
lowerCAmelCase__ : List[Any] = list(freq_to_letter_str.items() )
freq_pairs.sort(key=__UpperCAmelCase , reverse=__UpperCAmelCase )
lowerCAmelCase__ : list[str] = [freq_pair[1] for freq_pair in freq_pairs]
return "".join(__UpperCAmelCase )
def lowercase_ ( __UpperCAmelCase ) -> int:
lowerCAmelCase__ : List[Any] = get_frequency_order(__UpperCAmelCase )
lowerCAmelCase__ : List[str] = 0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 299
|
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
_A = get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase ( a_ , unittest.TestCase ):
_lowerCamelCase :List[str] = SpeechTaTokenizer
_lowerCamelCase :Union[str, Any] = False
_lowerCamelCase :Optional[Any] = True
def _lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ : Tuple = SpeechTaTokenizer(UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = AddedToken("""<mask>""" , lstrip=UpperCamelCase , rstrip=UpperCamelCase )
lowerCAmelCase__ : Dict = mask_token
tokenizer.add_special_tokens({"""mask_token""": mask_token} )
tokenizer.add_tokens(["""<ctc_blank>"""] )
tokenizer.save_pretrained(self.tmpdirname )
def _lowerCAmelCase ( self : List[Any] , UpperCamelCase : str ) -> Any:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = """this is a test"""
lowerCAmelCase__ : List[Any] = """this is a test"""
return input_text, output_text
def _lowerCAmelCase ( self : Tuple , UpperCamelCase : str , UpperCamelCase : Tuple=False , UpperCamelCase : Union[str, Any]=20 , UpperCamelCase : Union[str, Any]=5 ) -> int:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = self.get_input_output_texts(UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = tokenizer.decode(UpperCamelCase , clean_up_tokenization_spaces=UpperCamelCase )
return text, ids
def _lowerCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : str = """<pad>"""
lowerCAmelCase__ : List[str] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase ) , UpperCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase ) , UpperCamelCase )
def _lowerCAmelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-4] , """œ""" )
self.assertEqual(vocab_keys[-2] , """<mask>""" )
self.assertEqual(vocab_keys[-1] , """<ctc_blank>""" )
self.assertEqual(len(UpperCamelCase ) , 81 )
def _lowerCAmelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def _lowerCAmelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : int = self.get_tokenizers(do_lower_case=UpperCamelCase )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
lowerCAmelCase__ : Dict = tokenizer.vocab_size
lowerCAmelCase__ : Tuple = len(UpperCamelCase )
self.assertNotEqual(UpperCamelCase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
lowerCAmelCase__ : Optional[int] = ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
lowerCAmelCase__ : Tuple = tokenizer.add_tokens(UpperCamelCase )
lowerCAmelCase__ : Optional[int] = tokenizer.vocab_size
lowerCAmelCase__ : List[str] = len(UpperCamelCase )
self.assertNotEqual(UpperCamelCase , 0 )
self.assertEqual(UpperCamelCase , UpperCamelCase )
self.assertEqual(UpperCamelCase , len(UpperCamelCase ) )
self.assertEqual(UpperCamelCase , all_size + len(UpperCamelCase ) )
lowerCAmelCase__ : Optional[Any] = tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l""" , add_special_tokens=UpperCamelCase )
self.assertGreaterEqual(len(UpperCamelCase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
lowerCAmelCase__ : int = {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
lowerCAmelCase__ : Tuple = tokenizer.add_special_tokens(UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = tokenizer.vocab_size
lowerCAmelCase__ : Dict = len(UpperCamelCase )
self.assertNotEqual(UpperCamelCase , 0 )
self.assertEqual(UpperCamelCase , UpperCamelCase )
self.assertEqual(UpperCamelCase , len(UpperCamelCase ) )
self.assertEqual(UpperCamelCase , all_size_a + len(UpperCamelCase ) )
lowerCAmelCase__ : List[Any] = tokenizer.encode(
""">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l""" , add_special_tokens=UpperCamelCase )
self.assertGreaterEqual(len(UpperCamelCase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
pass
def _lowerCAmelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
pass
def _lowerCAmelCase ( self : Dict ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : List[Any] = self.get_tokenizer()
lowerCAmelCase__ : Optional[int] = tokenizer.tokenize("""This is a test""" )
# fmt: off
self.assertListEqual(UpperCamelCase , [SPIECE_UNDERLINE, """T""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """a""", SPIECE_UNDERLINE, """t""", """e""", """s""", """t"""] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
lowerCAmelCase__ : Any = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCamelCase , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """92000""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] )
lowerCAmelCase__ : Dict = tokenizer.convert_tokens_to_ids(UpperCamelCase )
# fmt: off
self.assertListEqual(UpperCamelCase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
lowerCAmelCase__ : Union[str, Any] = tokenizer.convert_ids_to_tokens(UpperCamelCase )
self.assertListEqual(
UpperCamelCase , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """<unk>""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] )
@slow
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
# Use custom sequence because this tokenizer does not handle numbers.
lowerCAmelCase__ : Union[str, Any] = [
"""Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
"""general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
"""Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained """
"""models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
"""BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
"""conditioning on both left and right context in all layers.""",
"""The quick brown fox jumps over the lazy dog.""",
]
# fmt: off
lowerCAmelCase__ : Union[str, Any] = {
"""input_ids""": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
"""attention_mask""": [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCamelCase , model_name="""microsoft/speecht5_asr""" , revision="""c5ef64c71905caeccde0e4462ef3f9077224c524""" , sequences=UpperCamelCase , )
| 299
| 1
|
'''simple docstring'''
import torch
def a__ ( ) -> Dict:
if torch.cuda.is_available():
UpperCAmelCase__ : int = torch.cuda.device_count()
else:
UpperCAmelCase__ : int = 0
print(F"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__main__":
main()
| 721
|
'''simple docstring'''
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCamelCase__ = logging.get_logger(__name__)
class lowerCamelCase_ ( enum.Enum ):
lowerCAmelCase__ = 0
lowerCAmelCase__ = 1
@add_end_docstrings(__a )
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = 'generated'
def __init__( self : Optional[Any] , *_A : int , **_A : Union[str, Any] ):
'''simple docstring'''
super().__init__(*_A , **_A )
self.check_model_type(
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )
def lowercase_ ( self : List[Any] , _A : Any=None , _A : Union[str, Any]=None , _A : str=None , _A : List[Any]=None , _A : List[str]=None , _A : Tuple=None , **_A : List[Any] , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = {}
if truncation is not None:
UpperCAmelCase__ : Union[str, Any] = truncation
UpperCAmelCase__ : Optional[int] = generate_kwargs
UpperCAmelCase__ : Union[str, Any] = {}
if return_tensors is not None and return_type is None:
UpperCAmelCase__ : Tuple = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
if return_type is not None:
UpperCAmelCase__ : Dict = return_type
if clean_up_tokenization_spaces is not None:
UpperCAmelCase__ : Tuple = clean_up_tokenization_spaces
if stop_sequence is not None:
UpperCAmelCase__ : Tuple = self.tokenizer.encode(_A , add_special_tokens=_A )
if len(_A ) > 1:
warnings.warn(
'''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
''' the stop sequence will be used as the stop sequence string in the interim.''' )
UpperCAmelCase__ : List[str] = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def lowercase_ ( self : Union[str, Any] , _A : int , _A : int , _A : int ):
'''simple docstring'''
return True
def lowercase_ ( self : List[str] , *_A : Tuple , _A : Any ):
'''simple docstring'''
UpperCAmelCase__ : str = self.model.config.prefix if self.model.config.prefix is not None else ''''''
if isinstance(args[0] , _A ):
if self.tokenizer.pad_token_id is None:
raise ValueError('''Please make sure that the tokenizer has a pad_token_id when using a batch input''' )
UpperCAmelCase__ : int = ([prefix + arg for arg in args[0]],)
UpperCAmelCase__ : int = True
elif isinstance(args[0] , _A ):
UpperCAmelCase__ : str = (prefix + args[0],)
UpperCAmelCase__ : str = False
else:
raise ValueError(
f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`""" )
UpperCAmelCase__ : Dict = self.tokenizer(*_A , padding=_A , truncation=_A , return_tensors=self.framework )
# This is produced by tokenizers but is an invalid generate kwargs
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
return inputs
def __call__( self : Union[str, Any] , *_A : Dict , **_A : str ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = super().__call__(*_A , **_A )
if (
isinstance(args[0] , _A )
and all(isinstance(_A , _A ) for el in args[0] )
and all(len(_A ) == 1 for res in result )
):
return [res[0] for res in result]
return result
def lowercase_ ( self : Tuple , _A : List[Any] , _A : Tuple=TruncationStrategy.DO_NOT_TRUNCATE , **_A : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self._parse_and_tokenize(_A , truncation=_A , **_A )
return inputs
def lowercase_ ( self : int , _A : Any , **_A : Tuple ):
'''simple docstring'''
if self.framework == "pt":
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = model_inputs['''input_ids'''].shape
elif self.framework == "tf":
UpperCAmelCase__ , UpperCAmelCase__ : Dict = tf.shape(model_inputs['''input_ids'''] ).numpy()
UpperCAmelCase__ : Any = generate_kwargs.get('''min_length''' , self.model.config.min_length )
UpperCAmelCase__ : Any = generate_kwargs.get('''max_length''' , self.model.config.max_length )
self.check_inputs(_A , generate_kwargs['''min_length'''] , generate_kwargs['''max_length'''] )
UpperCAmelCase__ : List[str] = self.model.generate(**_A , **_A )
UpperCAmelCase__ : Optional[int] = output_ids.shape[0]
if self.framework == "pt":
UpperCAmelCase__ : Dict = output_ids.reshape(_A , out_b // in_b , *output_ids.shape[1:] )
elif self.framework == "tf":
UpperCAmelCase__ : Any = tf.reshape(_A , (in_b, out_b // in_b, *output_ids.shape[1:]) )
return {"output_ids": output_ids}
def lowercase_ ( self : Optional[int] , _A : List[str] , _A : Optional[int]=ReturnType.TEXT , _A : Any=False ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = []
for output_ids in model_outputs["output_ids"][0]:
if return_type == ReturnType.TENSORS:
UpperCAmelCase__ : Any = {f"""{self.return_name}_token_ids""": output_ids}
elif return_type == ReturnType.TEXT:
UpperCAmelCase__ : str = {
f"""{self.return_name}_text""": self.tokenizer.decode(
_A , skip_special_tokens=_A , clean_up_tokenization_spaces=_A , )
}
records.append(_A )
return records
@add_end_docstrings(__a )
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = 'summary'
def __call__( self : Any , *_A : int , **_A : str ):
'''simple docstring'''
return super().__call__(*_A , **_A )
def lowercase_ ( self : List[str] , _A : int , _A : int , _A : int ):
'''simple docstring'''
if max_length < min_length:
logger.warning(f"""Your min_length={min_length} must be inferior than your max_length={max_length}.""" )
if input_length < max_length:
logger.warning(
f"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
'''a summarization task, where outputs shorter than the input are typically wanted, you might '''
f"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})""" )
@add_end_docstrings(__a )
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = 'translation'
def lowercase_ ( self : Tuple , _A : int , _A : int , _A : int ):
'''simple docstring'''
if input_length > 0.9 * max_length:
logger.warning(
f"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
'''increasing your max_length manually, e.g. translator(\'...\', max_length=400)''' )
return True
def lowercase_ ( self : List[str] , *_A : Tuple , _A : Any=TruncationStrategy.DO_NOT_TRUNCATE , _A : List[Any]=None , _A : Any=None ):
'''simple docstring'''
if getattr(self.tokenizer , '''_build_translation_inputs''' , _A ):
return self.tokenizer._build_translation_inputs(
*_A , return_tensors=self.framework , truncation=_A , src_lang=_A , tgt_lang=_A )
else:
return super()._parse_and_tokenize(*_A , truncation=_A )
def lowercase_ ( self : str , _A : Tuple=None , _A : Union[str, Any]=None , **_A : Tuple ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = super()._sanitize_parameters(**_A )
if src_lang is not None:
UpperCAmelCase__ : List[str] = src_lang
if tgt_lang is not None:
UpperCAmelCase__ : int = tgt_lang
if src_lang is None and tgt_lang is None:
# Backward compatibility, direct arguments use is preferred.
UpperCAmelCase__ : List[Any] = kwargs.get('''task''' , self.task )
UpperCAmelCase__ : Optional[Any] = task.split('''_''' )
if task and len(_A ) == 4:
# translation, XX, to YY
UpperCAmelCase__ : str = items[1]
UpperCAmelCase__ : Any = items[3]
return preprocess_params, forward_params, postprocess_params
def __call__( self : Optional[int] , *_A : Optional[int] , **_A : str ):
'''simple docstring'''
return super().__call__(*_A , **_A )
| 312
| 0
|
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None ) -> str:
if version.parse(hfh.__version__ ).release < version.parse('0.11.0' ).release:
# old versions of hfh don't url-encode the file path
SCREAMING_SNAKE_CASE_ : Any = quote(_UpperCamelCase )
return hfh.hf_hub_url(_UpperCamelCase , _UpperCamelCase , repo_type='dataset' , revision=_UpperCamelCase )
| 345
|
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def lowercase__( )-> Optional[int]:
"""simple docstring"""
raise RuntimeError("CUDA out of memory." )
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self ):
super().__init__()
_UpperCamelCase = nn.Linear(3 , 4 )
_UpperCamelCase = nn.BatchNormad(4 )
_UpperCamelCase = nn.Linear(4 , 5 )
def a ( self , A_ ):
return self.lineara(self.batchnorm(self.lineara(A_ ) ) )
class A_ ( unittest.TestCase ):
'''simple docstring'''
def a ( self ):
_UpperCamelCase = []
@find_executable_batch_size(starting_batch_size=1_28 )
def mock_training_loop_function(A_ ):
nonlocal batch_sizes
batch_sizes.append(A_ )
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(A_ , [1_28, 64, 32, 16, 8] )
def a ( self ):
_UpperCamelCase = []
@find_executable_batch_size(starting_batch_size=1_28 )
def mock_training_loop_function(A_ , A_ ):
nonlocal batch_sizes
batch_sizes.append(A_ )
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
_UpperCamelCase , _UpperCamelCase = mock_training_loop_function("hello" )
self.assertListEqual(A_ , [1_28, 64, 32, 16, 8] )
self.assertListEqual([bs, arga] , [8, "hello"] )
def a ( self ):
@find_executable_batch_size(starting_batch_size=0 )
def mock_training_loop_function(A_ ):
pass
with self.assertRaises(A_ ) as cm:
mock_training_loop_function()
self.assertIn("No executable batch size found, reached zero." , cm.exception.args[0] )
def a ( self ):
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(A_ ):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(A_ ) as cm:
mock_training_loop_function()
self.assertIn("No executable batch size found, reached zero." , cm.exception.args[0] )
def a ( self ):
@find_executable_batch_size(starting_batch_size=1_28 )
def mock_training_loop_function(A_ , A_ , A_ ):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(A_ ) as cm:
mock_training_loop_function(1_28 , "hello" , "world" )
self.assertIn("Batch size was passed into `f`" , cm.exception.args[0] )
self.assertIn("`f(arg1='hello', arg2='world')" , cm.exception.args[0] )
def a ( self ):
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(A_ ):
raise ValueError("Oops, we had an error!" )
with self.assertRaises(A_ ) as cm:
mock_training_loop_function()
self.assertIn("Oops, we had an error!" , cm.exception.args[0] )
@require_cuda
def a ( self ):
_UpperCamelCase = torch.cuda.memory_allocated()
_UpperCamelCase = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , A_ )
_UpperCamelCase = release_memory(A_ )
self.assertEqual(torch.cuda.memory_allocated() , A_ )
| 138
| 0
|
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __snake_case ( ):
snake_case_ = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
snake_case_ = parser.add_subparsers(help="diffusers-cli command helpers" )
# Register commands
EnvironmentCommand.register_subcommand(lowercase )
# Let's go
snake_case_ = parser.parse_args()
if not hasattr(lowercase , "func" ):
parser.print_help()
exit(1 )
# Run
snake_case_ = args.func(lowercase )
service.run()
if __name__ == "__main__":
main()
| 420
|
'''simple docstring'''
from manim import *
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def _lowercase ( self ):
snake_case_ = Rectangle(height=0.5 , width=0.5 )
snake_case_ = Rectangle(height=0.25 , width=0.25 )
snake_case_ = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
snake_case_ = [mem.copy() for i in range(6 )]
snake_case_ = [mem.copy() for i in range(6 )]
snake_case_ = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
snake_case_ = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
snake_case_ = VGroup(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
snake_case_ = Text("CPU" , font_size=24 )
snake_case_ = Group(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0.5 , aligned_edge=UpperCAmelCase_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(UpperCAmelCase_ )
snake_case_ = [mem.copy() for i in range(4 )]
snake_case_ = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
snake_case_ = Text("GPU" , font_size=24 )
snake_case_ = Group(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0.5 , aligned_edge=UpperCAmelCase_ )
gpu.move_to([-1, -1, 0] )
self.add(UpperCAmelCase_ )
snake_case_ = [mem.copy() for i in range(6 )]
snake_case_ = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
snake_case_ = Text("Model" , font_size=24 )
snake_case_ = Group(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0.5 , aligned_edge=UpperCAmelCase_ )
model.move_to([3, -1.0, 0] )
self.add(UpperCAmelCase_ )
snake_case_ = []
snake_case_ = []
snake_case_ = []
for i, rect in enumerate(UpperCAmelCase_ ):
rect.set_stroke(UpperCAmelCase_ )
snake_case_ = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(UpperCAmelCase_ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=UpperCAmelCase_ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=UpperCAmelCase_ , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=UpperCAmelCase_ , buff=0.0 )
self.add(UpperCAmelCase_ )
model_cpu_arr.append(UpperCAmelCase_ )
self.add(*UpperCAmelCase_ , *UpperCAmelCase_ , *UpperCAmelCase_ )
snake_case_ = [mem.copy() for i in range(6 )]
snake_case_ = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
snake_case_ = Text("Loaded Checkpoint" , font_size=24 )
snake_case_ = Group(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0.5 , aligned_edge=UpperCAmelCase_ )
checkpoint.move_to([3, 0.5, 0] )
self.add(UpperCAmelCase_ )
snake_case_ = []
snake_case_ = []
for i, rect in enumerate(UpperCAmelCase_ ):
snake_case_ = fill.copy().set_fill(UpperCAmelCase_ , opacity=0.7 )
target.move_to(UpperCAmelCase_ )
ckpt_arr.append(UpperCAmelCase_ )
snake_case_ = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(UpperCAmelCase_ )
self.add(*UpperCAmelCase_ , *UpperCAmelCase_ )
snake_case_ = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
snake_case_ = MarkupText(
f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = MarkupText(
f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(UpperCAmelCase_ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(UpperCAmelCase_ )
snake_case_ = MarkupText(
f'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
snake_case_ = [meta_mem.copy() for i in range(6 )]
snake_case_ = [meta_mem.copy() for i in range(6 )]
snake_case_ = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
snake_case_ = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
snake_case_ = VGroup(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
snake_case_ = Text("Disk" , font_size=24 )
snake_case_ = Group(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0.5 , aligned_edge=UpperCAmelCase_ )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(UpperCAmelCase_ , run_time=3 ) , Write(UpperCAmelCase_ , run_time=1 ) , Create(UpperCAmelCase_ , run_time=1 ) )
snake_case_ = []
for i, rect in enumerate(UpperCAmelCase_ ):
snake_case_ = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(UpperCAmelCase_ , run_time=1.5 ) )
self.play(*UpperCAmelCase_ )
self.play(FadeOut(UpperCAmelCase_ ) )
snake_case_ = MarkupText(f'''Then, the checkpoint is removed from memory\nthrough garbage collection.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(UpperCAmelCase_ , run_time=3 ) )
self.play(
FadeOut(UpperCAmelCase_ , UpperCAmelCase_ , *UpperCAmelCase_ , *UpperCAmelCase_ ) , )
self.wait()
| 420
| 1
|
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __A ( a_ :BertModel , a_ :str , a_ :str) -> str:
__a : List[str] = ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')
__a : Any = (
('''layer.''', '''layer_'''),
('''word_embeddings.weight''', '''word_embeddings'''),
('''position_embeddings.weight''', '''position_embeddings'''),
('''token_type_embeddings.weight''', '''token_type_embeddings'''),
('''.''', '''/'''),
('''LayerNorm/weight''', '''LayerNorm/gamma'''),
('''LayerNorm/bias''', '''LayerNorm/beta'''),
('''weight''', '''kernel'''),
)
if not os.path.isdir(a_):
os.makedirs(a_)
__a : List[Any] = model.state_dict()
def to_tf_var_name(a_ :str):
for patt, repl in iter(a_):
__a : int = name.replace(a_ , a_)
return F"""bert/{name}"""
def create_tf_var(a_ :np.ndarray , a_ :str , a_ :tf.Session):
__a : int = tf.dtypes.as_dtype(tensor.dtype)
__a : Optional[int] = tf.get_variable(dtype=a_ , shape=tensor.shape , name=a_ , initializer=tf.zeros_initializer())
session.run(tf.variables_initializer([tf_var]))
session.run(a_)
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
__a : Any = to_tf_var_name(a_)
__a : Tuple = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose):
__a : List[Any] = torch_tensor.T
__a : Optional[Any] = create_tf_var(tensor=a_ , name=a_ , session=a_)
tf.keras.backend.set_value(a_ , a_)
__a : int = session.run(a_)
print(F"""Successfully created {tf_name}: {np.allclose(a_ , a_)}""")
__a : Tuple = tf.train.Saver(tf.trainable_variables())
saver.save(a_ , os.path.join(a_ , model_name.replace('''-''' , '''_''') + '''.ckpt'''))
def __A ( a_ :int=None) -> str:
__a : str = argparse.ArgumentParser()
parser.add_argument('''--model_name''' , type=a_ , required=a_ , help='''model name e.g. bert-base-uncased''')
parser.add_argument(
'''--cache_dir''' , type=a_ , default=a_ , required=a_ , help='''Directory containing pytorch model''')
parser.add_argument('''--pytorch_model_path''' , type=a_ , required=a_ , help='''/path/to/<pytorch-model-name>.bin''')
parser.add_argument('''--tf_cache_dir''' , type=a_ , required=a_ , help='''Directory in which to save tensorflow model''')
__a : Optional[Any] = parser.parse_args(a_)
__a : Optional[Any] = 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=a_ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name)
if __name__ == "__main__":
main()
| 52
|
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def UpperCamelCase ( ) ->Optional[int]:
_lowerCamelCase : int = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
_lowerCamelCase : Union[str, Any] = parser.add_subparsers(help='''diffusers-cli command helpers''' )
# Register commands
EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE_ )
# Let's go
_lowerCamelCase : List[Any] = parser.parse_args()
if not hasattr(SCREAMING_SNAKE_CASE_ , '''func''' ):
parser.print_help()
exit(1 )
# Run
_lowerCamelCase : List[str] = args.func(SCREAMING_SNAKE_CASE_ )
service.run()
if __name__ == "__main__":
main()
| 434
| 0
|
from collections.abc import Generator
def _UpperCAmelCase ( ):
snake_case__ , snake_case__ = 0, 1
while True:
snake_case__ , snake_case__ = b, a + b
yield b
def _UpperCAmelCase ( a : int = 1000 ):
snake_case__ = 1
snake_case__ = fibonacci_generator()
while len(str(next(a ) ) ) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 99
|
import comet # From: unbabel-comet
import torch
import datasets
a__ = datasets.logging.get_logger(__name__)
a__ = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel's Participation in the WMT20 Metrics Shared Task},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
month = {November},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
pages = {909--918},
}
@inproceedings{rei-etal-2020-comet,
title = \"{COMET}: A Neural Framework for {MT} Evaluation\",
author = \"Rei, Ricardo and
Stewart, Craig and
Farinha, Ana C and
Lavie, Alon\",
booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",
month = nov,
year = \"2020\",
address = \"Online\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",
pages = \"2685--2702\",
}
"""
a__ = """\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.
See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
"""
a__ = """
COMET score.
Args:
`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.
Returns:
`samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
`scores`: List of scores.
Examples:
>>> comet_metric = datasets.load_metric('comet')
>>> # comet_metric = load_metric('comet', 'wmt20-comet-da') # you can also choose which model to use
>>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]
>>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]
>>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]
>>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
>>> print([round(v, 2) for v in results[\"scores\"]])
[0.19, 0.92]
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://unbabel.github.io/COMET/html/index.html""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""sources""": datasets.Value("""string""" , id="""sequence"""),
"""predictions""": datasets.Value("""string""" , id="""sequence"""),
"""references""": datasets.Value("""string""" , id="""sequence"""),
}) , codebase_urls=["""https://github.com/Unbabel/COMET"""] , reference_urls=[
"""https://github.com/Unbabel/COMET""",
"""https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
"""http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
] , )
def __magic_name__ ( self : str , UpperCamelCase__ : Dict):
'''simple docstring'''
if self.config_name == "default":
snake_case__ = comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""))
else:
snake_case__ = comet.load_from_checkpoint(comet.download_model(self.config_name))
def __magic_name__ ( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[Any] , UpperCamelCase__ : int=None , UpperCamelCase__ : List[Any]=False):
'''simple docstring'''
if gpus is None:
snake_case__ = 1 if torch.cuda.is_available() else 0
snake_case__ = {"""src""": sources, """mt""": predictions, """ref""": references}
snake_case__ = [dict(zip(UpperCamelCase__ , UpperCamelCase__)) for t in zip(*data.values())]
snake_case__ , snake_case__ = self.scorer.predict(UpperCamelCase__ , gpus=UpperCamelCase__ , progress_bar=UpperCamelCase__)
return {"mean_score": mean_score, "scores": scores}
| 99
| 1
|
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
__lowerCamelCase = 1_00
__lowerCamelCase = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__lowerCamelCase = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def lowercase ( __UpperCamelCase ) -> set[int]:
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
__magic_name__ = set()
__magic_name__ = 42
__magic_name__ = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def lowercase ( __UpperCamelCase = 5000 ) -> int | None:
for number_to_partition in range(1 , __UpperCamelCase ):
if len(partition(__UpperCamelCase ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(f"""{solution() = }""")
| 490
|
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _lowercase ( __UpperCAmelCase ):
_lowerCamelCase = DistilBertTokenizer
_lowerCamelCase = DistilBertTokenizerFast
_lowerCamelCase = True
@slow
def lowerCAmelCase__ ( self ):
__magic_name__ = DistilBertTokenizer.from_pretrained('''distilbert-base-uncased''' )
__magic_name__ = tokenizer.encode('''sequence builders''' , add_special_tokens=UpperCamelCase_ )
__magic_name__ = tokenizer.encode('''multi-sequence build''' , add_special_tokens=UpperCamelCase_ )
__magic_name__ = tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ )
__magic_name__ = tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ , UpperCamelCase_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 490
| 1
|
from __future__ import annotations
import queue
class lowerCamelCase__ :
'''simple docstring'''
def __init__(self ,__lowerCamelCase ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : List[str] = data
lowerCAmelCase__ : Optional[int] = None
lowerCAmelCase__ : str = None
def lowerCAmelCase__ ( ):
'''simple docstring'''
print('''\n********Press N to stop entering at any point of time********\n''')
lowerCAmelCase__ : List[str] = input('''Enter the value of the root node: ''').strip().lower()
lowerCAmelCase__ : queue.Queue = queue.Queue()
lowerCAmelCase__ : Any = TreeNode(int(lowerCamelCase_))
q.put(lowerCamelCase_)
while not q.empty():
lowerCAmelCase__ : Any = q.get()
lowerCAmelCase__ : Union[str, Any] = f"""Enter the left node of {node_found.data}: """
lowerCAmelCase__ : Optional[int] = input(lowerCamelCase_).strip().lower() or '''n'''
if check == "n":
return tree_node
lowerCAmelCase__ : Optional[Any] = TreeNode(int(lowerCamelCase_))
lowerCAmelCase__ : Optional[Any] = left_node
q.put(lowerCamelCase_)
lowerCAmelCase__ : Tuple = f"""Enter the right node of {node_found.data}: """
lowerCAmelCase__ : Optional[Any] = input(lowerCamelCase_).strip().lower() or '''n'''
if check == "n":
return tree_node
lowerCAmelCase__ : Optional[int] = TreeNode(int(lowerCamelCase_))
lowerCAmelCase__ : Any = right_node
q.put(lowerCamelCase_)
raise
def lowerCAmelCase__ ( lowerCamelCase_ : TreeNode):
'''simple docstring'''
if not isinstance(lowerCamelCase_ ,lowerCamelCase_) or not node:
return
print(node.data ,end=''',''')
pre_order(node.left)
pre_order(node.right)
def lowerCAmelCase__ ( lowerCamelCase_ : TreeNode):
'''simple docstring'''
if not isinstance(lowerCamelCase_ ,lowerCamelCase_) or not node:
return
in_order(node.left)
print(node.data ,end=''',''')
in_order(node.right)
def lowerCAmelCase__ ( lowerCamelCase_ : TreeNode):
'''simple docstring'''
if not isinstance(lowerCamelCase_ ,lowerCamelCase_) or not node:
return
post_order(node.left)
post_order(node.right)
print(node.data ,end=''',''')
def lowerCAmelCase__ ( lowerCamelCase_ : TreeNode):
'''simple docstring'''
if not isinstance(lowerCamelCase_ ,lowerCamelCase_) or not node:
return
lowerCAmelCase__ : queue.Queue = queue.Queue()
q.put(lowerCamelCase_)
while not q.empty():
lowerCAmelCase__ : str = q.get()
print(node_dequeued.data ,end=''',''')
if node_dequeued.left:
q.put(node_dequeued.left)
if node_dequeued.right:
q.put(node_dequeued.right)
def lowerCAmelCase__ ( lowerCamelCase_ : TreeNode):
'''simple docstring'''
if not isinstance(lowerCamelCase_ ,lowerCamelCase_) or not node:
return
lowerCAmelCase__ : queue.Queue = queue.Queue()
q.put(lowerCamelCase_)
while not q.empty():
lowerCAmelCase__ : List[str] = []
while not q.empty():
lowerCAmelCase__ : Any = q.get()
print(node_dequeued.data ,end=''',''')
if node_dequeued.left:
list_.append(node_dequeued.left)
if node_dequeued.right:
list_.append(node_dequeued.right)
print()
for node in list_:
q.put(lowerCamelCase_)
def lowerCAmelCase__ ( lowerCamelCase_ : TreeNode):
'''simple docstring'''
if not isinstance(lowerCamelCase_ ,lowerCamelCase_) or not node:
return
lowerCAmelCase__ : list[TreeNode] = []
lowerCAmelCase__ : Optional[Any] = node
while n or stack:
while n: # start from root node, find its left child
print(n.data ,end=''',''')
stack.append(lowerCamelCase_)
lowerCAmelCase__ : Any = n.left
# end of while means current node doesn't have left child
lowerCAmelCase__ : Tuple = stack.pop()
# start to traverse its right child
lowerCAmelCase__ : Union[str, Any] = n.right
def lowerCAmelCase__ ( lowerCamelCase_ : TreeNode):
'''simple docstring'''
if not isinstance(lowerCamelCase_ ,lowerCamelCase_) or not node:
return
lowerCAmelCase__ : list[TreeNode] = []
lowerCAmelCase__ : Optional[int] = node
while n or stack:
while n:
stack.append(lowerCamelCase_)
lowerCAmelCase__ : List[str] = n.left
lowerCAmelCase__ : List[str] = stack.pop()
print(n.data ,end=''',''')
lowerCAmelCase__ : Optional[Any] = n.right
def lowerCAmelCase__ ( lowerCamelCase_ : TreeNode):
'''simple docstring'''
if not isinstance(lowerCamelCase_ ,lowerCamelCase_) or not node:
return
lowerCAmelCase__ , lowerCAmelCase__ : Optional[Any] = [], []
lowerCAmelCase__ : List[Any] = node
stacka.append(lowerCamelCase_)
while stacka: # to find the reversed order of post order, store it in stack2
lowerCAmelCase__ : Any = stacka.pop()
if n.left:
stacka.append(n.left)
if n.right:
stacka.append(n.right)
stacka.append(lowerCamelCase_)
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data ,end=''',''')
def lowerCAmelCase__ ( lowerCamelCase_ : str = "" ,lowerCamelCase_ : Any=50 ,lowerCamelCase_ : Any="*"):
'''simple docstring'''
if not s:
return "\n" + width * char
lowerCAmelCase__ , lowerCAmelCase__ : Dict = divmod(width - len(lowerCamelCase_) - 2 ,2)
return f"""{left * char} {s} {(left + extra) * char}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('Binary Tree Traversals'))
__snake_case : TreeNode =build_tree()
print(prompt('Pre Order Traversal'))
pre_order(node)
print(prompt() + '\n')
print(prompt('In Order Traversal'))
in_order(node)
print(prompt() + '\n')
print(prompt('Post Order Traversal'))
post_order(node)
print(prompt() + '\n')
print(prompt('Level Order Traversal'))
level_order(node)
print(prompt() + '\n')
print(prompt('Actual Level Order Traversal'))
level_order_actual(node)
print('*' * 5_0 + '\n')
print(prompt('Pre Order Traversal - Iteration Version'))
pre_order_iter(node)
print(prompt() + '\n')
print(prompt('In Order Traversal - Iteration Version'))
in_order_iter(node)
print(prompt() + '\n')
print(prompt('Post Order Traversal - Iteration Version'))
post_order_iter(node)
print(prompt())
| 90
|
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
__snake_case : List[str] ='tiny-wmt19-en-ru'
# Build
# borrowed from a test
__snake_case : Any =[
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
__snake_case : Union[str, Any] =dict(zip(vocab, range(len(vocab))))
__snake_case : List[Any] =['l o 123', 'lo w 1456', 'e r</w> 1789', '']
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : Optional[Any] =Path(tmpdirname)
__snake_case : int =build_dir / VOCAB_FILES_NAMES['src_vocab_file']
__snake_case : str =build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
__snake_case : Optional[int] =build_dir / VOCAB_FILES_NAMES['merges_file']
with open(src_vocab_file, 'w') as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, 'w') as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, 'w') as fp:
fp.write('\n'.join(merges))
__snake_case : Union[str, Any] =FSMTTokenizer(
langs=['en', 'ru'],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
__snake_case : Dict =FSMTConfig(
langs=['ru', 'en'],
src_vocab_size=1_0_0_0,
tgt_vocab_size=1_0_0_0,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
__snake_case : Optional[Any] =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")
# Test
__snake_case : Dict =tokenizer(['Making tiny model'], return_tensors='pt')
__snake_case : Dict =tiny_model(**batch)
print('test output:', len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(f"""Generated {mname_tiny}""")
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 90
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''VisionEncoderDecoderModel''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''TFVisionEncoderDecoderModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''FlaxVisionEncoderDecoderModel''']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 41
|
from __future__ import annotations
import math
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> list[int]:
"""simple docstring"""
if num <= 0:
_A = F"{num}: Invalid input, please enter a positive integer."
raise ValueError(_SCREAMING_SNAKE_CASE )
_A = [True] * (num + 1)
_A = []
_A = 2
_A = int(math.sqrt(_SCREAMING_SNAKE_CASE ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(_SCREAMING_SNAKE_CASE )
# Set multiples of start be False
for i in range(start * start , num + 1 , _SCREAMING_SNAKE_CASE ):
if sieve[i] is True:
_A = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(_SCREAMING_SNAKE_CASE )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("Enter a positive integer: ").strip())))
| 27
| 0
|
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__snake_case : Tuple = logging.get_logger(__name__)
__snake_case : Union[str, Any] = {"vocab_file": "spiece.model"}
__snake_case : Dict = {
"vocab_file": {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
),
}
}
__snake_case : List[Any] = {
"google/bigbird-roberta-base": 4096,
"google/bigbird-roberta-large": 4096,
"google/bigbird-base-trivia-itc": 4096,
}
class A ( a ):
__UpperCAmelCase : Dict = VOCAB_FILES_NAMES
__UpperCAmelCase : Any = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : Any = ["""input_ids""", """attention_mask"""]
__UpperCAmelCase : List[int] = []
def __init__( self , snake_case_ , snake_case_="<unk>" , snake_case_="<s>" , snake_case_="</s>" , snake_case_="<pad>" , snake_case_="[SEP]" , snake_case_="[MASK]" , snake_case_="[CLS]" , snake_case_ = None , **snake_case_ , ) -> None:
_a = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else bos_token
_a = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else eos_token
_a = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else unk_token
_a = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else pad_token
_a = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else cls_token
_a = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
_a = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else mask_token
_a = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , pad_token=snake_case_ , sep_token=snake_case_ , mask_token=snake_case_ , cls_token=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
_a = vocab_file
_a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case_ )
@property
def __lowerCAmelCase ( self ) -> Optional[Any]:
return self.sp_model.get_piece_size()
def __lowerCAmelCase ( self ) -> Optional[int]:
_a = {self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Tuple:
_a = self.__dict__.copy()
_a = None
return state
def __setstate__( self , snake_case_ ) -> Dict:
_a = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
_a = {}
_a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCAmelCase ( self , snake_case_ ) -> List[str]:
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def __lowerCAmelCase ( self , snake_case_ ) -> Tuple:
return self.sp_model.piece_to_id(snake_case_ )
def __lowerCAmelCase ( self , snake_case_ ) -> Optional[Any]:
_a = self.sp_model.IdToPiece(snake_case_ )
return token
def __lowerCAmelCase ( self , snake_case_ ) -> List[str]:
_a = []
_a = ""
_a = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(snake_case_ ) + token
_a = True
_a = []
else:
current_sub_tokens.append(snake_case_ )
_a = False
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def __lowerCAmelCase ( self , snake_case_ , snake_case_ = False , snake_case_ = None , snake_case_ = True , **snake_case_ , ) -> str:
_a = kwargs.pop("use_source_tokenizer" , snake_case_ )
_a = self.convert_ids_to_tokens(snake_case_ , skip_special_tokens=snake_case_ )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
_a = []
_a = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(snake_case_ ) )
_a = []
sub_texts.append(snake_case_ )
else:
current_sub_text.append(snake_case_ )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(snake_case_ ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
_a = re.sub(R" (\[(MASK|SEP)\])" , R"\1" , " ".join(snake_case_ ) )
else:
_a = "".join(snake_case_ )
_a = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
_a = self.clean_up_tokenization(snake_case_ )
return clean_text
else:
return text
def __lowerCAmelCase ( self , snake_case_ , snake_case_ = None ) -> Tuple[str]:
if not os.path.isdir(snake_case_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
_a = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case_ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case_ , "wb" ) as fi:
_a = self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
def __lowerCAmelCase ( self , snake_case_ , snake_case_ = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_a = [self.cls_token_id]
_a = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def __lowerCAmelCase ( self , snake_case_ , snake_case_ = None , snake_case_ = 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 None:
return [1] + ([0] * len(snake_case_ )) + [1]
return [1] + ([0] * len(snake_case_ )) + [1] + ([0] * len(snake_case_ )) + [1]
def __lowerCAmelCase ( self , snake_case_ , snake_case_ = None ) -> List[int]:
_a = [self.sep_token_id]
_a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
| 707
|
'''simple docstring'''
class A :
def __init__( self ) -> List[str]:
_a = 0
_a = 0
_a = {}
def __lowerCAmelCase ( self , snake_case_ ) -> int:
if vertex not in self.adjacency:
_a = {}
self.num_vertices += 1
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ ) -> Optional[int]:
self.add_vertex(snake_case_ )
self.add_vertex(snake_case_ )
if head == tail:
return
_a = weight
_a = weight
def __lowerCAmelCase ( self ) -> Union[str, Any]:
_a = self.get_edges()
for edge in edges:
_a , _a , _a = edge
edges.remove((tail, head, weight) )
for i in range(len(snake_case_ ) ):
_a = list(edges[i] )
edges.sort(key=lambda snake_case_ : e[2] )
for i in range(len(snake_case_ ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
_a = edges[i][2] + 1
for edge in edges:
_a , _a , _a = edge
_a = weight
_a = weight
def __str__( self ) -> Optional[int]:
_a = ""
for tail in self.adjacency:
for head in self.adjacency[tail]:
_a = self.adjacency[head][tail]
string += F'''{head} -> {tail} == {weight}\n'''
return string.rstrip("\n" )
def __lowerCAmelCase ( self ) -> Optional[Any]:
_a = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def __lowerCAmelCase ( self ) -> Any:
return self.adjacency.keys()
@staticmethod
def __lowerCAmelCase ( snake_case_=None , snake_case_=None ) -> Any:
_a = Graph()
if vertices is None:
_a = []
if edges is None:
_a = []
for vertex in vertices:
g.add_vertex(snake_case_ )
for edge in edges:
g.add_edge(*snake_case_ )
return g
class A :
def __init__( self ) -> Optional[int]:
_a = {}
_a = {}
def __len__( self ) -> List[Any]:
return len(self.parent )
def __lowerCAmelCase ( self , snake_case_ ) -> Optional[int]:
if item in self.parent:
return self.find(snake_case_ )
_a = item
_a = 0
return item
def __lowerCAmelCase ( self , snake_case_ ) -> Optional[Any]:
if item not in self.parent:
return self.make_set(snake_case_ )
if item != self.parent[item]:
_a = self.find(self.parent[item] )
return self.parent[item]
def __lowerCAmelCase ( self , snake_case_ , snake_case_ ) -> Optional[int]:
_a = self.find(snake_case_ )
_a = self.find(snake_case_ )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
_a = roota
return roota
if self.rank[roota] < self.rank[roota]:
_a = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
_a = roota
return roota
return None
@staticmethod
def __lowerCAmelCase ( snake_case_ ) -> Tuple:
_a = graph.num_vertices
_a = Graph.UnionFind()
_a = []
while num_components > 1:
_a = {}
for vertex in graph.get_vertices():
_a = -1
_a = graph.get_edges()
for edge in edges:
_a , _a , _a = edge
edges.remove((tail, head, weight) )
for edge in edges:
_a , _a , _a = edge
_a = union_find.find(snake_case_ )
_a = union_find.find(snake_case_ )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
_a = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
_a = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
_a , _a , _a = cheap_edge[vertex]
if union_find.find(snake_case_ ) != union_find.find(snake_case_ ):
union_find.union(snake_case_ , snake_case_ )
mst_edges.append(cheap_edge[vertex] )
_a = num_components - 1
_a = Graph.build(edges=snake_case_ )
return mst
| 691
| 0
|
def _SCREAMING_SNAKE_CASE ( snake_case ) -> bool:
_UpperCAmelCase = 0
for ch in input_str:
_UpperCAmelCase = ord(snake_case )
_UpperCAmelCase = pow(2 , snake_case )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 518
|
a = 8.3_144_598
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case ) -> float:
if temperature < 0:
raise Exception("""Temperature cannot be less than 0 K""" )
if molar_mass <= 0:
raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol""" )
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
a = 300
a = 28
a = rms_speed_of_molecule(temperature, molar_mass)
print(F'Vrms of Nitrogen gas at 300 K is {vrms} m/s')
| 518
| 1
|
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
lowercase_ = {
"""b0""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 224,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 240,
"""dropout_rate""": 0.2,
"""dw_padding""": [16],
},
"""b2""": {
"""hidden_dim""": 1_408,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 260,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 16],
},
"""b3""": {
"""hidden_dim""": 1_536,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 300,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 18],
},
"""b4""": {
"""hidden_dim""": 1_792,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 380,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2_048,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 456,
"""dropout_rate""": 0.4,
"""dw_padding""": [13, 27],
},
"""b6""": {
"""hidden_dim""": 2_304,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 528,
"""dropout_rate""": 0.5,
"""dw_padding""": [31],
},
"""b7""": {
"""hidden_dim""": 2_560,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 600,
"""dropout_rate""": 0.5,
"""dw_padding""": [18],
},
}
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> str:
lowercase__ = EfficientNetConfig()
lowercase__ = CONFIG_MAP[model_name]['hidden_dim']
lowercase__ = CONFIG_MAP[model_name]['width_coef']
lowercase__ = CONFIG_MAP[model_name]['depth_coef']
lowercase__ = CONFIG_MAP[model_name]['image_size']
lowercase__ = CONFIG_MAP[model_name]['dropout_rate']
lowercase__ = CONFIG_MAP[model_name]['dw_padding']
lowercase__ = 'huggingface/label-files'
lowercase__ = 'imagenet-1k-id2label.json'
lowercase__ = 1000
lowercase__ = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
lowercase__ = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
return config
def __UpperCamelCase () -> Tuple:
lowercase__ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowercase__ = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
lowercase__ = CONFIG_MAP[model_name]['image_size']
lowercase__ = EfficientNetImageProcessor(
size={'height': size, 'width': size} , image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3] , do_center_crop=_SCREAMING_SNAKE_CASE , )
return preprocessor
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Tuple:
lowercase__ = [v.split('_' )[0].split('block' )[1] for v in original_param_names if v.startswith('block' )]
lowercase__ = sorted(set(_SCREAMING_SNAKE_CASE ) )
lowercase__ = len(_SCREAMING_SNAKE_CASE )
lowercase__ = {b: str(_SCREAMING_SNAKE_CASE ) for b, i in zip(_SCREAMING_SNAKE_CASE , range(_SCREAMING_SNAKE_CASE ) )}
lowercase__ = []
rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight') )
rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight') )
rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias') )
rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean') )
rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var') )
for b in block_names:
lowercase__ = block_name_mapping[b]
rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""") )
rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""") )
rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""") )
rename_keys.append(
(F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""") )
rename_keys.append(
(F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""") )
rename_keys.append(
(F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""") )
rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""") )
rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""") )
rename_keys.append(
(F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""") )
rename_keys.append(
(F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""") )
rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""") )
rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""") )
rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""") )
rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""") )
rename_keys.append(
(F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""") )
rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""") )
rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""") )
rename_keys.append(
(F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""") )
rename_keys.append(
(F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""") )
rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight') )
rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight') )
rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias') )
rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean') )
rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var') )
lowercase__ = {}
for item in rename_keys:
if item[0] in original_param_names:
lowercase__ = 'efficientnet.' + item[1]
lowercase__ = 'classifier.weight'
lowercase__ = 'classifier.bias'
return key_mapping
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
for key, value in tf_params.items():
if "normalization" in key:
continue
lowercase__ = key_mapping[key]
if "_conv" in key and "kernel" in key:
lowercase__ = torch.from_numpy(_SCREAMING_SNAKE_CASE ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
lowercase__ = torch.from_numpy(_SCREAMING_SNAKE_CASE ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
lowercase__ = torch.from_numpy(np.transpose(_SCREAMING_SNAKE_CASE ) )
else:
lowercase__ = torch.from_numpy(_SCREAMING_SNAKE_CASE )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
lowercase__ = model_classes[model_name](
include_top=_SCREAMING_SNAKE_CASE , weights='imagenet' , input_tensor=_SCREAMING_SNAKE_CASE , input_shape=_SCREAMING_SNAKE_CASE , pooling=_SCREAMING_SNAKE_CASE , classes=1000 , classifier_activation='softmax' , )
lowercase__ = original_model.trainable_variables
lowercase__ = original_model.non_trainable_variables
lowercase__ = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
lowercase__ = param.numpy()
lowercase__ = list(tf_params.keys() )
# Load HuggingFace model
lowercase__ = get_efficientnet_config(_SCREAMING_SNAKE_CASE )
lowercase__ = EfficientNetForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
lowercase__ = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('Converting parameters...' )
lowercase__ = rename_keys(_SCREAMING_SNAKE_CASE )
replace_params(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Initialize preprocessor and preprocess input image
lowercase__ = convert_image_processor(_SCREAMING_SNAKE_CASE )
lowercase__ = preprocessor(images=prepare_img() , return_tensors='pt' )
# HF model inference
hf_model.eval()
with torch.no_grad():
lowercase__ = hf_model(**_SCREAMING_SNAKE_CASE )
lowercase__ = outputs.logits.detach().numpy()
# Original model inference
lowercase__ = False
lowercase__ = CONFIG_MAP[model_name]['image_size']
lowercase__ = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
lowercase__ = image.img_to_array(_SCREAMING_SNAKE_CASE )
lowercase__ = np.expand_dims(_SCREAMING_SNAKE_CASE , axis=0 )
lowercase__ = original_model.predict(_SCREAMING_SNAKE_CASE )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 ), "The predicted logits are not the same."
print('Model outputs match!' )
if save_model:
# Create folder to save model
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
os.mkdir(_SCREAMING_SNAKE_CASE )
# Save converted model and image processor
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
preprocessor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
# Push model and image processor to hub
print(F"""Pushing converted {model_name} to the hub...""" )
lowercase__ = F"""efficientnet-{model_name}"""
preprocessor.push_to_hub(_SCREAMING_SNAKE_CASE )
hf_model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
lowercase_ = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 703
|
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
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""facebook/deit-base-distilled-patch16-224""": (
"""https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
),
# See all DeiT models at https://huggingface.co/models?filter=deit
}
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : Any = 'deit'
def __init__( self : Any , a : Union[str, Any]=768 , a : Optional[Any]=12 , a : Union[str, Any]=12 , a : Optional[int]=3_072 , a : Optional[int]="gelu" , a : Optional[Any]=0.0 , a : List[Any]=0.0 , a : int=0.02 , a : List[str]=1E-1_2 , a : Optional[int]=224 , a : Tuple=16 , a : List[Any]=3 , a : List[str]=True , a : Any=16 , **a : Union[str, Any] , )-> int:
"""simple docstring"""
super().__init__(**a )
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = qkv_bias
lowercase__ = encoder_stride
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : List[Any] = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE_ ( self : int )-> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def SCREAMING_SNAKE_CASE_ ( self : Any )-> float:
"""simple docstring"""
return 1E-4
| 45
| 0
|
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class A:
'''simple docstring'''
def __init__( self : List[Any] , A_ : Dict , A_ : Union[str, Any]=13 , A_ : List[Any]=30 , A_ : Optional[Any]=2 , A_ : List[str]=3 , A_ : List[str]=True , A_ : Dict=True , A_ : List[Any]=32 , A_ : Any=2 , A_ : Any=4 , A_ : Optional[int]=37 , A_ : Dict="gelu" , A_ : List[Any]=0.1 , A_ : Optional[int]=0.1 , A_ : Union[str, Any]=10 , A_ : Optional[Any]=0.02 , A_ : List[Any]=3 , A_ : str=None , ) -> str:
"""simple docstring"""
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = image_size
lowerCamelCase_ = patch_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = type_sequence_label_size
lowerCamelCase_ = initializer_range
lowerCamelCase_ = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCamelCase_ = (image_size // patch_size) ** 2
lowerCamelCase_ = num_patches + 1
def a__ ( self : List[str] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase_ = self.get_config()
return config, pixel_values, labels
def a__ ( self : List[Any] ) -> Any:
"""simple docstring"""
return ViTConfig(
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=A_ , initializer_range=self.initializer_range , )
def a__ ( self : Any , A_ : int , A_ : int , A_ : int ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ = TFViTModel(config=A_ )
lowerCamelCase_ = model(A_ , training=A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
lowerCamelCase_ = self.image_size // 2
lowerCamelCase_ = pixel_values[:, :, :image_size, :image_size]
lowerCamelCase_ = model(A_ , interpolate_pos_encoding=A_ , training=A_ )
lowerCamelCase_ = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def a__ ( self : List[Any] , A_ : List[Any] , A_ : Any , A_ : Any ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ = self.type_sequence_label_size
lowerCamelCase_ = TFViTForImageClassification(A_ )
lowerCamelCase_ = model(A_ , labels=A_ , training=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
lowerCamelCase_ = self.image_size // 2
lowerCamelCase_ = pixel_values[:, :, :image_size, :image_size]
lowerCamelCase_ = model(A_ , interpolate_pos_encoding=A_ , training=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCamelCase_ = 1
lowerCamelCase_ = TFViTForImageClassification(A_ )
lowerCamelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase_ = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a__ ( self : List[Any] ) -> Any:
"""simple docstring"""
lowerCamelCase_ = self.prepare_config_and_inputs()
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = config_and_inputs
lowerCamelCase_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class A( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
UpperCamelCase = (
{'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
if is_tf_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def a__ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = TFViTModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=A_ , has_text_modality=A_ , hidden_size=37 )
def a__ ( self : int ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='ViT does not use inputs_embeds' )
def a__ ( self : List[str] ) -> Tuple:
"""simple docstring"""
pass
@unittest.skip(reason='ViT does not use inputs_embeds' )
def a__ ( self : int ) -> Optional[Any]:
"""simple docstring"""
pass
def a__ ( self : str ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(A_ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
lowerCamelCase_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A_ , tf.keras.layers.Layer ) )
def a__ ( self : List[Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(A_ )
lowerCamelCase_ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ = [*signature.parameters.keys()]
lowerCamelCase_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , A_ )
def a__ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def a__ ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A_ )
@slow
def a__ ( self : Any ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ = TFViTModel.from_pretrained('google/vit-base-patch16-224' )
self.assertIsNotNone(A_ )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowerCamelCase_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class A( unittest.TestCase ):
'''simple docstring'''
@cached_property
def a__ ( self : int ) -> Tuple:
"""simple docstring"""
return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224' ) if is_vision_available() else None
@slow
def a__ ( self : List[str] ) -> str:
"""simple docstring"""
lowerCamelCase_ = TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224' )
lowerCamelCase_ = self.default_image_processor
lowerCamelCase_ = prepare_img()
lowerCamelCase_ = image_processor(images=A_ , return_tensors='tf' )
# forward pass
lowerCamelCase_ = model(**A_ )
# verify the logits
lowerCamelCase_ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , A_ )
lowerCamelCase_ = tf.constant([-0.2744, 0.8215, -0.0836] )
tf.debugging.assert_near(outputs.logits[0, :3] , A_ , atol=1E-4 )
| 70
|
'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def _A ( A ,A ,A ,A ) -> List[Any]:
lowercase : Optional[int] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, nicht wahr?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
lowercase : Optional[int] = {
"wmt16-en-de-dist-12-1": [28.3, 27.52],
"wmt16-en-de-dist-6-1": [27.4, 27.11],
"wmt16-en-de-12-1": [26.9, 25.75],
}
lowercase : Dict = F'''{src_lang}-{tgt_lang}'''
lowercase : Union[str, Any] = F'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
All 3 models are available:
* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/{model_name}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
## Training data
Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
## Eval results
Here are the BLEU scores:
model | fairseq | transformers
-------|---------|----------
{model_name} | {scores[model_name][0]} | {scores[model_name][1]}
The score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
## Data Sources
- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)
### BibTeX entry and citation info
```
@misc{{kasai2020deep,
title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
year={{2020}},
eprint={{2006.10369}},
archivePrefix={{arXiv}},
primaryClass={{cs.CL}}
}}
```
'''
model_card_dir.mkdir(parents=A ,exist_ok=A )
lowercase : str = os.path.join(A ,"README.md" )
print(F'''Generating {path}''' )
with open(A ,"w" ,encoding="utf-8" ) as f:
f.write(A )
# make sure we are under the root of the project
lowerCAmelCase : Union[str, Any] = Path(__file__).resolve().parent.parent.parent
lowerCAmelCase : Union[str, Any] = repo_dir / """model_cards"""
for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
lowerCAmelCase : Optional[Any] = model_cards_dir / """allenai""" / model_name
write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)
| 372
| 0
|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class __lowerCAmelCase :
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=True , snake_case=99 , snake_case=64 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ) -> List[str]:
"""simple docstring"""
a__ : Dict = parent
a__ : Union[str, Any] = batch_size
a__ : str = seq_length
a__ : List[Any] = is_training
a__ : Any = use_input_mask
a__ : str = use_token_type_ids
a__ : Any = use_labels
a__ : List[Any] = vocab_size
a__ : Optional[Any] = hidden_size
a__ : Optional[int] = embedding_size
a__ : Dict = num_hidden_layers
a__ : str = num_attention_heads
a__ : Any = intermediate_size
a__ : Optional[int] = hidden_act
a__ : Any = hidden_dropout_prob
a__ : List[Any] = attention_probs_dropout_prob
a__ : Union[str, Any] = max_position_embeddings
a__ : int = type_vocab_size
a__ : List[Any] = type_sequence_label_size
a__ : Union[str, Any] = initializer_range
a__ : List[Any] = num_labels
a__ : Dict = num_choices
a__ : Tuple = scope
def _snake_case ( self ) -> Optional[int]:
"""simple docstring"""
a__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ : str = None
if self.use_input_mask:
a__ : Dict = random_attention_mask([self.batch_size, self.seq_length] )
a__ : Optional[int] = None
if self.use_token_type_ids:
a__ : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a__ : List[str] = None
a__ : List[str] = None
a__ : Optional[Any] = None
if self.use_labels:
a__ : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a__ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
a__ : Dict = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _snake_case ( self ) -> Optional[Any]:
"""simple docstring"""
return 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 , embedding_size=self.embedding_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 _snake_case ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> str:
"""simple docstring"""
a__ : Dict = MobileBertModel(config=snake_case )
model.to(snake_case )
model.eval()
a__ : Tuple = model(snake_case , attention_mask=snake_case , token_type_ids=snake_case )
a__ : List[Any] = model(snake_case , token_type_ids=snake_case )
a__ : Union[str, Any] = model(snake_case )
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 _snake_case ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> Any:
"""simple docstring"""
a__ : Optional[Any] = MobileBertForMaskedLM(config=snake_case )
model.to(snake_case )
model.eval()
a__ : Union[str, Any] = model(snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> List[str]:
"""simple docstring"""
a__ : Union[str, Any] = MobileBertForNextSentencePrediction(config=snake_case )
model.to(snake_case )
model.eval()
a__ : Tuple = model(
snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def _snake_case ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> str:
"""simple docstring"""
a__ : List[Any] = MobileBertForPreTraining(config=snake_case )
model.to(snake_case )
model.eval()
a__ : Any = model(
snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case , next_sentence_label=snake_case , )
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 _snake_case ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> Tuple:
"""simple docstring"""
a__ : int = MobileBertForQuestionAnswering(config=snake_case )
model.to(snake_case )
model.eval()
a__ : Any = model(
snake_case , attention_mask=snake_case , token_type_ids=snake_case , start_positions=snake_case , end_positions=snake_case , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _snake_case ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> List[Any]:
"""simple docstring"""
a__ : List[str] = self.num_labels
a__ : Optional[Any] = MobileBertForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
a__ : Dict = model(snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> Optional[Any]:
"""simple docstring"""
a__ : Optional[Any] = self.num_labels
a__ : List[Any] = MobileBertForTokenClassification(config=snake_case )
model.to(snake_case )
model.eval()
a__ : str = model(snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _snake_case ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> Dict:
"""simple docstring"""
a__ : Optional[int] = self.num_choices
a__ : Optional[Any] = MobileBertForMultipleChoice(config=snake_case )
model.to(snake_case )
model.eval()
a__ : List[str] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : List[str] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : Tuple = model(
snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _snake_case ( self ) -> int:
"""simple docstring"""
a__ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
) : List[str] = config_and_inputs
a__ : Optional[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _UpperCamelCase ,_UpperCamelCase ,unittest.TestCase ):
_UpperCamelCase : Any = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCamelCase : str = (
{
"""feature-extraction""": MobileBertModel,
"""fill-mask""": MobileBertForMaskedLM,
"""question-answering""": MobileBertForQuestionAnswering,
"""text-classification""": MobileBertForSequenceClassification,
"""token-classification""": MobileBertForTokenClassification,
"""zero-shot""": MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCamelCase : Optional[int] = True
def _snake_case ( self , snake_case , snake_case , snake_case=False ) -> str:
"""simple docstring"""
a__ : List[str] = super()._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
if return_labels:
if model_class in get_values(snake_case ):
a__ : Union[str, Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=snake_case )
a__ : Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case )
return inputs_dict
def _snake_case ( self ) -> List[Any]:
"""simple docstring"""
a__ : Union[str, Any] = MobileBertModelTester(self )
a__ : Optional[int] = ConfigTester(self , config_class=snake_case , hidden_size=37 )
def _snake_case ( self ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def _snake_case ( self ) -> Optional[int]:
"""simple docstring"""
a__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*snake_case )
def _snake_case ( self ) -> Any:
"""simple docstring"""
a__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case )
def _snake_case ( self ) -> Tuple:
"""simple docstring"""
a__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case )
def _snake_case ( self ) -> List[Any]:
"""simple docstring"""
a__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case )
def _snake_case ( self ) -> List[Any]:
"""simple docstring"""
a__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case )
def _snake_case ( self ) -> List[str]:
"""simple docstring"""
a__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case )
def _snake_case ( self ) -> List[Any]:
"""simple docstring"""
a__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case )
def _snake_case ( self ) -> List[str]:
"""simple docstring"""
a__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case )
def _A ( lowerCamelCase ):
return torch.tensor(
lowerCamelCase , dtype=torch.long , device=lowerCamelCase , )
SCREAMING_SNAKE_CASE__ : List[Any] = 1e-3
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def _snake_case ( self ) -> Tuple:
"""simple docstring"""
a__ : Optional[Any] = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(snake_case )
a__ : int = _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]] )
with torch.no_grad():
a__ : str = model(snake_case )[0]
a__ : List[Any] = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , snake_case )
a__ : Optional[int] = torch.tensor(
[
[
[-2.473_6526E07, 8.269_1656E04, 1.652_1838E05],
[-5.754_1704E-01, 3.905_6022E00, 4.401_1507E00],
[2.604_7359E00, 1.567_7652E00, -1.732_4188E-01],
]
] , device=snake_case , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
a__ : Optional[int] = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
a__ : Tuple = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 629
|
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
SCREAMING_SNAKE_CASE__ : Dict = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""
class __lowerCAmelCase ( unittest.TestCase ,_UpperCamelCase ):
def _snake_case ( self ) -> str:
"""simple docstring"""
a__ : Optional[int] = load_tool("text-question-answering" )
self.tool.setup()
a__ : Dict = load_tool("text-question-answering" , remote=snake_case )
def _snake_case ( self ) -> Dict:
"""simple docstring"""
a__ : Optional[Any] = self.tool(snake_case , "What did Hugging Face do in April 2021?" )
self.assertEqual(snake_case , "launched the BigScience Research Workshop" )
def _snake_case ( self ) -> Tuple:
"""simple docstring"""
a__ : List[Any] = self.remote_tool(snake_case , "What did Hugging Face do in April 2021?" )
self.assertEqual(snake_case , "launched the BigScience Research Workshop" )
def _snake_case ( self ) -> Any:
"""simple docstring"""
a__ : Any = self.tool(text=snake_case , question="What did Hugging Face do in April 2021?" )
self.assertEqual(snake_case , "launched the BigScience Research Workshop" )
def _snake_case ( self ) -> int:
"""simple docstring"""
a__ : List[str] = self.remote_tool(text=snake_case , question="What did Hugging Face do in April 2021?" )
self.assertEqual(snake_case , "launched the BigScience Research Workshop" )
| 629
| 1
|
"""simple docstring"""
def snake_case ( A__ ,A__ ):
UpperCAmelCase_ : List[str] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
UpperCAmelCase_ : Optional[int] = n - k
# Calculate C(n,k)
for i in range(A__ ):
result *= n - i
result //= i + 1
return result
def snake_case ( A__ ):
return binomial_coefficient(2 * node_count ,A__ ) // (node_count + 1)
def snake_case ( A__ ):
if n < 0:
raise ValueError("factorial() not defined for negative values" )
UpperCAmelCase_ : Union[str, Any] = 1
for i in range(1 ,n + 1 ):
result *= i
return result
def snake_case ( A__ ):
return catalan_number(A__ ) * factorial(A__ )
if __name__ == "__main__":
lowerCamelCase_ = int(input('''Enter the number of nodes: ''').strip() or 0)
if node_count <= 0:
raise ValueError('''We need some nodes to work with.''')
print(
f'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
f'binary trees and {catalan_number(node_count)} binary search trees.'
)
| 95
|
"""simple docstring"""
import requests
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: str , lowerCamelCase_: str ):
"""simple docstring"""
snake_case : List[str] = {"Content-Type": "application/json"}
snake_case : int = requests.post(lowerCamelCase_ , json={"text": message_body} , headers=lowerCamelCase_ )
if response.status_code != 2_0_0:
snake_case : str = (
"Request to slack returned an error "
f'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(lowerCamelCase_ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')
| 449
| 0
|
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
lowercase = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
lowercase = {
# fairseq:
'''wmt19-ru-en''': {'''length_penalty''': 1.1},
'''wmt19-en-ru''': {'''length_penalty''': 1.15},
'''wmt19-en-de''': {'''length_penalty''': 1.0},
'''wmt19-de-en''': {'''length_penalty''': 1.1},
# allenai:
'''wmt16-en-de-dist-12-1''': {'''length_penalty''': 0.6},
'''wmt16-en-de-dist-6-1''': {'''length_penalty''': 0.6},
'''wmt16-en-de-12-1''': {'''length_penalty''': 0.8},
'''wmt19-de-en-6-6-base''': {'''length_penalty''': 0.6},
'''wmt19-de-en-6-6-big''': {'''length_penalty''': 0.6},
}
# this remaps the different models to their organization names
lowercase = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
lowercase = '''facebook'''
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
lowercase = '''allenai'''
def UpperCAmelCase_ ( lowercase__ ):
'''simple docstring'''
a_ =dict((re.sub(r"@@$" , "" , lowerCamelCase__ ), v) if k.endswith("@@" ) else (re.sub(r"$" , "</w>" , lowerCamelCase__ ), v) for k, v in d.items() )
a_ ="<s> <pad> </s> <unk>".split()
# restore the special tokens
for k in keep_keys:
del da[F"""{k}</w>"""]
a_ =d[k] # restore
return da
def UpperCAmelCase_ ( lowercase__ , lowercase__ ):
'''simple docstring'''
assert os.path.exists(lowerCamelCase__ )
os.makedirs(lowerCamelCase__ , exist_ok=lowerCamelCase__ )
print(F"""Writing results to {pytorch_dump_folder_path}""" )
# handle various types of models
a_ =basename(lowerCamelCase__ )
a_ =dirname(lowerCamelCase__ )
a_ =fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
a_ =cls.hub_models()
a_ ={"bpe": "fastbpe", "tokenizer": "moses"}
a_ ="."
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F"""using checkpoint {checkpoint_file}""" )
a_ =hub_utils.from_pretrained(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , archive_map=lowerCamelCase__ , **lowerCamelCase__ )
a_ =vars(chkpt["args"]["model"] )
a_ =args["source_lang"]
a_ =args["target_lang"]
a_ =dirname(lowerCamelCase__ )
a_ =basename(lowerCamelCase__ )
# dicts
a_ =os.path.join(lowerCamelCase__ , F"""dict.{src_lang}.txt""" )
a_ =os.path.join(lowerCamelCase__ , F"""dict.{tgt_lang}.txt""" )
a_ =Dictionary.load(lowerCamelCase__ )
a_ =rewrite_dict_keys(src_dict.indices )
a_ =len(lowerCamelCase__ )
a_ =os.path.join(lowerCamelCase__ , "vocab-src.json" )
print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records""" )
with open(lowerCamelCase__ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(lowerCamelCase__ , ensure_ascii=lowerCamelCase__ , indent=lowerCamelCase__ ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
a_ =True
for k in src_vocab.keys():
if not k.islower():
a_ =False
break
a_ =Dictionary.load(lowerCamelCase__ )
a_ =rewrite_dict_keys(tgt_dict.indices )
a_ =len(lowerCamelCase__ )
a_ =os.path.join(lowerCamelCase__ , "vocab-tgt.json" )
print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records""" )
with open(lowerCamelCase__ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(lowerCamelCase__ , ensure_ascii=lowerCamelCase__ , indent=lowerCamelCase__ ) )
# merges_file (bpecodes)
a_ =os.path.join(lowerCamelCase__ , VOCAB_FILES_NAMES["merges_file"] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
a_ =os.path.join(lowerCamelCase__ , lowerCamelCase__ )
if os.path.exists(lowerCamelCase__ ):
break
with open(lowerCamelCase__ , encoding="utf-8" ) as fin:
a_ =fin.read()
a_ =re.sub(r" \d+$" , "" , lowerCamelCase__ , 0 , re.M ) # remove frequency number
print(F"""Generating {merges_file}""" )
with open(lowerCamelCase__ , "w" , encoding="utf-8" ) as fout:
fout.write(lowerCamelCase__ )
# model config
a_ =os.path.join(lowerCamelCase__ , "config.json" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F"""need to extend tokenizer to support bpe={args['bpe']}"""
assert args["tokenizer"] == "moses", F"""need to extend tokenizer to support bpe={args['tokenizer']}"""
a_ ={
"architectures": ["FSMTForConditionalGeneration"],
"model_type": "fsmt",
"activation_dropout": args["activation_dropout"],
"activation_function": "relu",
"attention_dropout": args["attention_dropout"],
"d_model": args["decoder_embed_dim"],
"dropout": args["dropout"],
"init_std": 0.02,
"max_position_embeddings": args["max_source_positions"],
"num_hidden_layers": args["encoder_layers"],
"src_vocab_size": src_vocab_size,
"tgt_vocab_size": tgt_vocab_size,
"langs": [src_lang, tgt_lang],
"encoder_attention_heads": args["encoder_attention_heads"],
"encoder_ffn_dim": args["encoder_ffn_embed_dim"],
"encoder_layerdrop": args["encoder_layerdrop"],
"encoder_layers": args["encoder_layers"],
"decoder_attention_heads": args["decoder_attention_heads"],
"decoder_ffn_dim": args["decoder_ffn_embed_dim"],
"decoder_layerdrop": args["decoder_layerdrop"],
"decoder_layers": args["decoder_layers"],
"bos_token_id": 0,
"pad_token_id": 1,
"eos_token_id": 2,
"is_encoder_decoder": True,
"scale_embedding": not args["no_scale_embedding"],
"tie_word_embeddings": args["share_all_embeddings"],
}
# good hparam defaults to start with
a_ =5
a_ =False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
a_ =best_score_hparams[model_dir]["length_penalty"]
else:
a_ =1.0
print(F"""Generating {fsmt_model_config_file}""" )
with open(lowerCamelCase__ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(lowerCamelCase__ , ensure_ascii=lowerCamelCase__ , indent=lowerCamelCase__ ) )
# tokenizer config
a_ =os.path.join(lowerCamelCase__ , lowerCamelCase__ )
a_ ={
"langs": [src_lang, tgt_lang],
"model_max_length": 1_0_2_4,
"do_lower_case": do_lower_case,
}
print(F"""Generating {fsmt_tokenizer_config_file}""" )
with open(lowerCamelCase__ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(lowerCamelCase__ , ensure_ascii=lowerCamelCase__ , indent=lowerCamelCase__ ) )
# model
a_ =chkpt["models"][0]
a_ =model.state_dict()
# rename keys to start with 'model.'
a_ =OrderedDict(("model." + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
a_ =[
"model.model",
"model.encoder.version",
"model.decoder.version",
"model.encoder_embed_tokens.weight",
"model.decoder_embed_tokens.weight",
"model.encoder.embed_positions._float_tensor",
"model.decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
model_state_dict.pop(lowerCamelCase__ , lowerCamelCase__ )
a_ =FSMTConfig.from_pretrained(lowerCamelCase__ )
a_ =FSMTForConditionalGeneration(lowerCamelCase__ )
# check that it loads ok
model_new.load_state_dict(lowerCamelCase__ , strict=lowerCamelCase__ )
# save
a_ =os.path.join(lowerCamelCase__ , lowerCamelCase__ )
print(F"""Generating {pytorch_weights_dump_path}""" )
torch.save(lowerCamelCase__ , lowerCamelCase__ )
print("Conversion is done!" )
print("\nLast step is to upload the files to s3" )
print(F"""cd {data_root}""" )
print(F"""transformers-cli upload {model_dir}""" )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--fsmt_checkpoint_path''',
default=None,
type=str,
required=True,
help=(
'''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
''' bpecodes, etc.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowercase = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 714
|
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((a_) , (a_)) =extended_euclid(lowercase__ , a % b )
a_ =a // b
return (y, x - k * y)
def UpperCAmelCase_ ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
((a_) , (a_)) =extended_euclid(lowercase__ , lowercase__ )
a_ =na * na
a_ =ra * x * na + ra * y * na
return (n % m + m) % m
def UpperCAmelCase_ ( lowercase__ , lowercase__ ):
'''simple docstring'''
((a_) , (a_)) =extended_euclid(lowercase__ , lowercase__ )
if b < 0:
a_ =(b % n + n) % n
return b
def UpperCAmelCase_ ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
a_ , a_ =invert_modulo(lowercase__ , lowercase__ ), invert_modulo(lowercase__ , lowercase__ )
a_ =na * na
a_ =ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name='''chinese_remainder_theorem''', verbose=True)
testmod(name='''chinese_remainder_theorem2''', verbose=True)
testmod(name='''invert_modulo''', verbose=True)
testmod(name='''extended_euclid''', verbose=True)
| 41
| 0
|
def _UpperCAmelCase ( A ):
'''simple docstring'''
UpperCAmelCase__ =[1]
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ =0, 0, 0
UpperCAmelCase__ =ugly_nums[ia] * 2
UpperCAmelCase__ =ugly_nums[ia] * 3
UpperCAmelCase__ =ugly_nums[ia] * 5
for _ in range(1 , A ):
UpperCAmelCase__ =min(A , A , A )
ugly_nums.append(A )
if next_num == next_a:
ia += 1
UpperCAmelCase__ =ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
UpperCAmelCase__ =ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
UpperCAmelCase__ =ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f"""{ugly_numbers(2_00) = }""")
| 625
|
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,
)
UpperCamelCase_ = {
'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:
UpperCamelCase_ = ['OwlViTFeatureExtractor']
UpperCamelCase_ = ['OwlViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
'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
UpperCamelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 625
| 1
|
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
raise Exception('requires fairseq >= 1.0.0a')
logging.set_verbosity_info()
a_ : Dict = logging.get_logger(__name__)
a_ : Optional[Any] = 'Hello world! cécé herlolip'
def _SCREAMING_SNAKE_CASE ( snake_case_ : str , snake_case_ : str , snake_case_ : bool ):
__magic_name__ = FairseqRobertaModel.from_pretrained(snake_case_ )
roberta.eval() # disable dropout
__magic_name__ = roberta.model.encoder.sentence_encoder
__magic_name__ = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings , hidden_size=roberta.cfg.model.encoder_embed_dim , num_hidden_layers=roberta.cfg.model.encoder_layers , num_attention_heads=roberta.cfg.model.encoder_attention_heads , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1E-5 , )
if classification_head:
__magic_name__ = roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
print('''Our RoBERTa config:''' , snake_case_ )
__magic_name__ = XLMRobertaXLForSequenceClassification(snake_case_ ) if classification_head else XLMRobertaXLForMaskedLM(snake_case_ )
model.eval()
# Now let's copy all the weights.
# Embeddings
__magic_name__ = roberta_sent_encoder.embed_tokens.weight
__magic_name__ = roberta_sent_encoder.embed_positions.weight
__magic_name__ = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them.
__magic_name__ = roberta_sent_encoder.layer_norm.weight
__magic_name__ = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
__magic_name__ = model.roberta.encoder.layer[i]
__magic_name__ = roberta_sent_encoder.layers[i]
__magic_name__ = layer.attention
__magic_name__ = roberta_layer.self_attn_layer_norm.weight
__magic_name__ = roberta_layer.self_attn_layer_norm.bias
# self attention
__magic_name__ = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
)
__magic_name__ = roberta_layer.self_attn.q_proj.weight
__magic_name__ = roberta_layer.self_attn.q_proj.bias
__magic_name__ = roberta_layer.self_attn.k_proj.weight
__magic_name__ = roberta_layer.self_attn.k_proj.bias
__magic_name__ = roberta_layer.self_attn.v_proj.weight
__magic_name__ = roberta_layer.self_attn.v_proj.bias
# self-attention output
__magic_name__ = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
__magic_name__ = roberta_layer.self_attn.out_proj.weight
__magic_name__ = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
__magic_name__ = roberta_layer.final_layer_norm.weight
__magic_name__ = roberta_layer.final_layer_norm.bias
# intermediate
__magic_name__ = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
__magic_name__ = roberta_layer.fca.weight
__magic_name__ = roberta_layer.fca.bias
# output
__magic_name__ = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
__magic_name__ = roberta_layer.fca.weight
__magic_name__ = roberta_layer.fca.bias
# end of layer
if classification_head:
__magic_name__ = roberta.model.classification_heads['''mnli'''].dense.weight
__magic_name__ = roberta.model.classification_heads['''mnli'''].dense.bias
__magic_name__ = roberta.model.classification_heads['''mnli'''].out_proj.weight
__magic_name__ = roberta.model.classification_heads['''mnli'''].out_proj.bias
else:
# LM Head
__magic_name__ = roberta.model.encoder.lm_head.dense.weight
__magic_name__ = roberta.model.encoder.lm_head.dense.bias
__magic_name__ = roberta.model.encoder.lm_head.layer_norm.weight
__magic_name__ = roberta.model.encoder.lm_head.layer_norm.bias
__magic_name__ = roberta.model.encoder.lm_head.weight
__magic_name__ = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
__magic_name__ = roberta.encode(snake_case_ ).unsqueeze(0 ) # batch of size 1
__magic_name__ = model(snake_case_ )[0]
if classification_head:
__magic_name__ = roberta.model.classification_heads['''mnli'''](roberta.extract_features(snake_case_ ) )
else:
__magic_name__ = roberta.model(snake_case_ )[0]
print(our_output.shape , their_output.shape )
__magic_name__ = torch.max(torch.abs(our_output - their_output ) ).item()
print(f'max_absolute_diff = {max_absolute_diff}' ) # ~ 1e-7
__magic_name__ = torch.allclose(snake_case_ , snake_case_ , atol=1E-3 )
print('''Do both models output the same tensors?''' , '''🔥''' if success else '''💩''' )
if not success:
raise Exception('''Something went wRoNg''' )
pathlib.Path(snake_case_ ).mkdir(parents=snake_case_ , exist_ok=snake_case_ )
print(f'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(snake_case_ )
if __name__ == "__main__":
a_ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--classification_head', action='store_true', help='Whether to convert a final classification head.'
)
a_ : List[Any] = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 678
|
def _SCREAMING_SNAKE_CASE ( snake_case_ : list[list[int]] , snake_case_ : int , snake_case_ : int , snake_case_ : set ):
__magic_name__ , __magic_name__ = len(snake_case_ ), len(grid[0] )
if (
min(snake_case_ , 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) )
__magic_name__ = 0
count += depth_first_search(snake_case_ , row + 1 , snake_case_ , snake_case_ )
count += depth_first_search(snake_case_ , row - 1 , snake_case_ , snake_case_ )
count += depth_first_search(snake_case_ , snake_case_ , col + 1 , snake_case_ )
count += depth_first_search(snake_case_ , snake_case_ , col - 1 , snake_case_ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 678
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpeechConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"""UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""UniSpeechForCTC""",
"""UniSpeechForPreTraining""",
"""UniSpeechForSequenceClassification""",
"""UniSpeechModel""",
"""UniSpeechPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 299
|
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowerCamelCase ( a_ ):
def __init__( self : Any , UpperCamelCase : Dict , UpperCamelCase : int=13 , UpperCamelCase : Any=7 , UpperCamelCase : Any=True , UpperCamelCase : Tuple=True , UpperCamelCase : List[str]=True , UpperCamelCase : Union[str, Any]=True , UpperCamelCase : List[Any]=True , UpperCamelCase : List[str]=False , UpperCamelCase : Tuple=False , UpperCamelCase : List[str]=False , UpperCamelCase : List[Any]=2 , UpperCamelCase : List[str]=99 , UpperCamelCase : Union[str, Any]=0 , UpperCamelCase : Union[str, Any]=32 , UpperCamelCase : str=5 , UpperCamelCase : int=4 , UpperCamelCase : Optional[Any]=0.1 , UpperCamelCase : List[str]=0.1 , UpperCamelCase : Any=5_12 , UpperCamelCase : Optional[Any]=12 , UpperCamelCase : Union[str, Any]=2 , UpperCamelCase : Optional[Any]=0.02 , UpperCamelCase : Any=3 , UpperCamelCase : Optional[Any]=4 , UpperCamelCase : int="last" , UpperCamelCase : Optional[Any]=None , UpperCamelCase : Any=None , ) -> int:
"""simple docstring"""
lowerCAmelCase__ : Any = parent
lowerCAmelCase__ : Dict = batch_size
lowerCAmelCase__ : Optional[int] = seq_length
lowerCAmelCase__ : Optional[Any] = is_training
lowerCAmelCase__ : int = use_input_lengths
lowerCAmelCase__ : Any = use_token_type_ids
lowerCAmelCase__ : Tuple = use_labels
lowerCAmelCase__ : Any = gelu_activation
lowerCAmelCase__ : Optional[int] = sinusoidal_embeddings
lowerCAmelCase__ : Union[str, Any] = causal
lowerCAmelCase__ : Union[str, Any] = asm
lowerCAmelCase__ : Optional[int] = n_langs
lowerCAmelCase__ : str = vocab_size
lowerCAmelCase__ : int = n_special
lowerCAmelCase__ : List[Any] = hidden_size
lowerCAmelCase__ : int = num_hidden_layers
lowerCAmelCase__ : Optional[int] = num_attention_heads
lowerCAmelCase__ : Optional[Any] = hidden_dropout_prob
lowerCAmelCase__ : Optional[int] = attention_probs_dropout_prob
lowerCAmelCase__ : Dict = max_position_embeddings
lowerCAmelCase__ : Dict = type_vocab_size
lowerCAmelCase__ : Any = type_sequence_label_size
lowerCAmelCase__ : str = initializer_range
lowerCAmelCase__ : List[Any] = num_labels
lowerCAmelCase__ : Any = num_choices
lowerCAmelCase__ : Optional[int] = summary_type
lowerCAmelCase__ : List[str] = use_proj
lowerCAmelCase__ : str = scope
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase__ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase__ : List[Any] = None
if self.use_input_lengths:
lowerCAmelCase__ : Dict = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
lowerCAmelCase__ : Optional[Any] = None
if self.use_token_type_ids:
lowerCAmelCase__ : int = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
lowerCAmelCase__ : List[Any] = None
lowerCAmelCase__ : str = None
lowerCAmelCase__ : str = None
if self.use_labels:
lowerCAmelCase__ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase__ : Optional[int] = ids_tensor([self.batch_size] , 2 ).float()
lowerCAmelCase__ : int = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase__ : Tuple = 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] ) -> str:
"""simple docstring"""
return FlaubertConfig(
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 , )
def _lowerCAmelCase ( self : Optional[int] , UpperCamelCase : List[str] , UpperCamelCase : int , UpperCamelCase : str , UpperCamelCase : List[str] , UpperCamelCase : Optional[Any] , UpperCamelCase : Any , UpperCamelCase : Optional[int] , UpperCamelCase : Tuple , UpperCamelCase : List[Any] , ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = FlaubertModel(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
lowerCAmelCase__ : List[str] = model(UpperCamelCase , lengths=UpperCamelCase , langs=UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = model(UpperCamelCase , langs=UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = model(UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : Dict , UpperCamelCase : str , UpperCamelCase : int , UpperCamelCase : Optional[int] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optional[int] , UpperCamelCase : Tuple , UpperCamelCase : int , UpperCamelCase : Any , UpperCamelCase : Union[str, Any] , ) -> Any:
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = FlaubertWithLMHeadModel(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
lowerCAmelCase__ : Tuple = model(UpperCamelCase , token_type_ids=UpperCamelCase , labels=UpperCamelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCAmelCase ( self : List[Any] , UpperCamelCase : Optional[int] , UpperCamelCase : List[str] , UpperCamelCase : str , UpperCamelCase : Tuple , UpperCamelCase : int , UpperCamelCase : List[str] , UpperCamelCase : Any , UpperCamelCase : List[str] , UpperCamelCase : int , ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = FlaubertForQuestionAnsweringSimple(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
lowerCAmelCase__ : int = model(UpperCamelCase )
lowerCAmelCase__ : Optional[int] = model(UpperCamelCase , start_positions=UpperCamelCase , end_positions=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 _lowerCAmelCase ( self : List[Any] , UpperCamelCase : int , UpperCamelCase : Optional[int] , UpperCamelCase : Optional[int] , UpperCamelCase : List[Any] , UpperCamelCase : Any , UpperCamelCase : Union[str, Any] , UpperCamelCase : Dict , UpperCamelCase : int , UpperCamelCase : Optional[Any] , ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = FlaubertForQuestionAnswering(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
lowerCAmelCase__ : Dict = model(UpperCamelCase )
lowerCAmelCase__ : List[str] = model(
UpperCamelCase , start_positions=UpperCamelCase , end_positions=UpperCamelCase , cls_index=UpperCamelCase , is_impossible=UpperCamelCase , p_mask=UpperCamelCase , )
lowerCAmelCase__ : Union[str, Any] = model(
UpperCamelCase , start_positions=UpperCamelCase , end_positions=UpperCamelCase , cls_index=UpperCamelCase , is_impossible=UpperCamelCase , )
((lowerCAmelCase__) , ) : Dict = result_with_labels.to_tuple()
lowerCAmelCase__ : Any = model(UpperCamelCase , start_positions=UpperCamelCase , end_positions=UpperCamelCase )
((lowerCAmelCase__) , ) : Optional[int] = 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 : List[Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : List[str] , UpperCamelCase : str , UpperCamelCase : int , UpperCamelCase : Optional[int] , UpperCamelCase : List[Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : Optional[Any] , ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : List[str] = FlaubertForSequenceClassification(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
lowerCAmelCase__ : int = model(UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = model(UpperCamelCase , labels=UpperCamelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _lowerCAmelCase ( self : Tuple , UpperCamelCase : List[str] , UpperCamelCase : Union[str, Any] , UpperCamelCase : str , UpperCamelCase : Optional[Any] , UpperCamelCase : List[str] , UpperCamelCase : Union[str, Any] , UpperCamelCase : int , UpperCamelCase : Union[str, Any] , UpperCamelCase : Dict , ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : Tuple = self.num_labels
lowerCAmelCase__ : Optional[Any] = FlaubertForTokenClassification(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
lowerCAmelCase__ : Dict = model(UpperCamelCase , attention_mask=UpperCamelCase , labels=UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowerCAmelCase ( self : Optional[Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : Tuple , UpperCamelCase : Optional[Any] , UpperCamelCase : int , UpperCamelCase : Any , UpperCamelCase : Any , UpperCamelCase : str , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optional[int] , ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ : int = self.num_choices
lowerCAmelCase__ : Tuple = FlaubertForMultipleChoice(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
lowerCAmelCase__ : List[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase__ : int = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase__ : str = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase__ : Any = model(
UpperCamelCase , attention_mask=UpperCamelCase , token_type_ids=UpperCamelCase , labels=UpperCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
(
(
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) ,
) : Dict = config_and_inputs
lowerCAmelCase__ : Union[str, Any] = {
"""input_ids""": input_ids,
"""token_type_ids""": token_type_ids,
"""lengths""": input_lengths,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_torch
class _lowerCamelCase ( a_ , a_ , unittest.TestCase ):
_lowerCamelCase :int = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
_lowerCamelCase :Optional[Any] = (
{
"feature-extraction": FlaubertModel,
"fill-mask": FlaubertWithLMHeadModel,
"question-answering": FlaubertForQuestionAnsweringSimple,
"text-classification": FlaubertForSequenceClassification,
"token-classification": FlaubertForTokenClassification,
"zero-shot": FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : List[str] , UpperCamelCase : int , UpperCamelCase : Optional[int] , UpperCamelCase : List[Any] , UpperCamelCase : List[str] ) -> Dict:
"""simple docstring"""
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 : Optional[Any] , UpperCamelCase : Tuple , UpperCamelCase : Dict , UpperCamelCase : int=False ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : str = super()._prepare_for_class(UpperCamelCase , UpperCamelCase , return_labels=UpperCamelCase )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
lowerCAmelCase__ : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCamelCase )
lowerCAmelCase__ : List[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCamelCase )
return inputs_dict
def _lowerCAmelCase ( self : Dict ) -> str:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = FlaubertModelTester(self )
lowerCAmelCase__ : List[Any] = ConfigTester(self , config_class=UpperCamelCase , emb_dim=37 )
def _lowerCAmelCase ( self : str ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowerCAmelCase ( self : Dict ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*UpperCamelCase )
def _lowerCAmelCase ( self : int ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*UpperCamelCase )
def _lowerCAmelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*UpperCamelCase )
def _lowerCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*UpperCamelCase )
def _lowerCAmelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*UpperCamelCase )
def _lowerCAmelCase ( self : int ) -> str:
"""simple docstring"""
lowerCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*UpperCamelCase )
def _lowerCAmelCase ( self : Dict ) -> str:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*UpperCamelCase )
@slow
def _lowerCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ : Tuple = FlaubertModel.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
lowerCAmelCase__ : str = True
lowerCAmelCase__ : Tuple = model_class(config=UpperCamelCase )
lowerCAmelCase__ : str = self._prepare_for_class(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Tuple = torch.jit.trace(
UpperCamelCase , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(UpperCamelCase , os.path.join(UpperCamelCase , """traced_model.pt""" ) )
lowerCAmelCase__ : Union[str, Any] = torch.jit.load(os.path.join(UpperCamelCase , """traced_model.pt""" ) , map_location=UpperCamelCase )
loaded(inputs_dict["""input_ids"""].to(UpperCamelCase ) , inputs_dict["""attention_mask"""].to(UpperCamelCase ) )
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
@slow
def _lowerCAmelCase ( self : Any ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ : List[Any] = FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased""" )
lowerCAmelCase__ : List[Any] = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
with torch.no_grad():
lowerCAmelCase__ : int = model(UpperCamelCase )[0]
lowerCAmelCase__ : Dict = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , UpperCamelCase )
lowerCAmelCase__ : Optional[int] = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCamelCase , atol=1E-4 ) )
| 299
| 1
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( a__ , unittest.TestCase ):
snake_case_ = KandinskyVaaPriorPipeline
snake_case_ = ["""prompt"""]
snake_case_ = ["""prompt""", """negative_prompt"""]
snake_case_ = [
"""num_images_per_prompt""",
"""generator""",
"""num_inference_steps""",
"""latents""",
"""negative_prompt""",
"""guidance_scale""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def _UpperCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
return 32
@property
def _UpperCamelCase ( self : Dict ):
'''simple docstring'''
return 32
@property
def _UpperCamelCase ( self : Optional[int] ):
'''simple docstring'''
return self.time_input_dim
@property
def _UpperCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def _UpperCamelCase ( self : Tuple ):
'''simple docstring'''
return 100
@property
def _UpperCamelCase ( self : int ):
'''simple docstring'''
A__ : Union[str, Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def _UpperCamelCase ( self : List[str] ):
'''simple docstring'''
torch.manual_seed(0 )
A__ : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(lowercase__ )
@property
def _UpperCamelCase ( self : Dict ):
'''simple docstring'''
torch.manual_seed(0 )
A__ : int = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 12,
'''embedding_dim''': self.text_embedder_hidden_size,
'''num_layers''': 1,
}
A__ : str = PriorTransformer(**lowercase__ )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
A__ : int = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def _UpperCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
A__ : List[str] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
A__ : List[str] = CLIPVisionModelWithProjection(lowercase__ )
return model
@property
def _UpperCamelCase ( self : Dict ):
'''simple docstring'''
A__ : int = CLIPImageProcessor(
crop_size=224 , do_center_crop=lowercase__ , do_normalize=lowercase__ , do_resize=lowercase__ , image_mean=[0.48145466, 0.4578275, 0.40821073] , image_std=[0.26862954, 0.26130258, 0.27577711] , resample=3 , size=224 , )
return image_processor
def _UpperCamelCase ( self : List[Any] ):
'''simple docstring'''
A__ : Optional[int] = self.dummy_prior
A__ : Dict = self.dummy_image_encoder
A__ : List[str] = self.dummy_text_encoder
A__ : Dict = self.dummy_tokenizer
A__ : Dict = self.dummy_image_processor
A__ : str = UnCLIPScheduler(
variance_type="""fixed_small_log""" , prediction_type="""sample""" , num_train_timesteps=1000 , clip_sample=lowercase__ , clip_sample_range=10.0 , )
A__ : Union[str, Any] = {
'''prior''': prior,
'''image_encoder''': image_encoder,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''scheduler''': scheduler,
'''image_processor''': image_processor,
}
return components
def _UpperCamelCase ( self : List[str] , snake_case : Tuple , snake_case : Optional[Any]=0 ):
'''simple docstring'''
if str(lowercase__ ).startswith("""mps""" ):
A__ : List[str] = torch.manual_seed(lowercase__ )
else:
A__ : Dict = torch.Generator(device=lowercase__ ).manual_seed(lowercase__ )
A__ : Any = {
'''prompt''': '''horse''',
'''generator''': generator,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def _UpperCamelCase ( self : int ):
'''simple docstring'''
A__ : Optional[int] = '''cpu'''
A__ : Dict = self.get_dummy_components()
A__ : Dict = self.pipeline_class(**lowercase__ )
A__ : Tuple = pipe.to(lowercase__ )
pipe.set_progress_bar_config(disable=lowercase__ )
A__ : Tuple = pipe(**self.get_dummy_inputs(lowercase__ ) )
A__ : Any = output.image_embeds
A__ : Union[str, Any] = pipe(
**self.get_dummy_inputs(lowercase__ ) , return_dict=lowercase__ , )[0]
A__ : str = image[0, -10:]
A__ : Any = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
A__ : List[Any] = np.array(
[-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def _UpperCamelCase ( self : Optional[int] ):
'''simple docstring'''
A__ : int = torch_device == '''cpu'''
A__ : List[str] = True
A__ : List[str] = False
self._test_inference_batch_single_identical(
test_max_difference=lowercase__ , relax_max_difference=lowercase__ , test_mean_pixel_difference=lowercase__ , )
@skip_mps
def _UpperCamelCase ( self : Any ):
'''simple docstring'''
A__ : int = torch_device == '''cpu'''
A__ : List[str] = False
self._test_attention_slicing_forward_pass(
test_max_difference=lowercase__ , test_mean_pixel_difference=lowercase__ , )
| 703
|
"""simple docstring"""
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A_ = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A_ = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F'{len(upper_files)} files contain uppercase characters:')
print('''\n'''.join(upper_files) + '''\n''')
A_ = [file for file in filepaths if ''' ''' in file]
if space_files:
print(F'{len(space_files)} files contain space characters:')
print('''\n'''.join(space_files) + '''\n''')
A_ = [file for file in filepaths if '''-''' in file]
if hyphen_files:
print(F'{len(hyphen_files)} files contain hyphen characters:')
print('''\n'''.join(hyphen_files) + '''\n''')
A_ = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F'{len(nodir_files)} files are not in a directory:')
print('''\n'''.join(nodir_files) + '''\n''')
A_ = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 498
| 0
|
"""simple docstring"""
import string
from math import logaa
def lowerCamelCase__ ( __snake_case, __snake_case ) -> int:
"""simple docstring"""
_UpperCamelCase = document.translate(
str.maketrans('''''', '''''', string.punctuation ) ).replace('''\n''', '''''' )
_UpperCamelCase = document_without_punctuation.split(''' ''' ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> tuple[int, int]:
"""simple docstring"""
_UpperCamelCase = corpus.lower().translate(
str.maketrans('''''', '''''', string.punctuation ) ) # strip all punctuation and replace it with ''
_UpperCamelCase = corpus_without_punctuation.split('''\n''' )
_UpperCamelCase = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__snake_case ))
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=False ) -> float:
"""simple docstring"""
if smoothing:
if n == 0:
raise ValueError('''log10(0) is undefined.''' )
return round(1 + logaa(n / (1 + df) ), 3 )
if df == 0:
raise ZeroDivisionError('''df must be > 0''' )
elif n == 0:
raise ValueError('''log10(0) is undefined.''' )
return round(logaa(n / df ), 3 )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> float:
"""simple docstring"""
return round(tf * idf, 3 )
| 19
|
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : List[str]=sys.maxsize):
'''simple docstring'''
snake_case__ = """bilinear"""
snake_case__ = max_size
snake_case__ = short_edge_length
def __call__( self : List[str] , UpperCamelCase__ : Tuple):
'''simple docstring'''
snake_case__ = []
for img in imgs:
snake_case__ , snake_case__ = img.shape[:2]
# later: provide list and randomly choose index for resize
snake_case__ = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1)
if size == 0:
return img
snake_case__ = size * 1.0 / min(UpperCamelCase__ , UpperCamelCase__)
if h < w:
snake_case__ , snake_case__ = size, scale * w
else:
snake_case__ , snake_case__ = scale * h, size
if max(UpperCamelCase__ , UpperCamelCase__) > self.max_size:
snake_case__ = self.max_size * 1.0 / max(UpperCamelCase__ , UpperCamelCase__)
snake_case__ = newh * scale
snake_case__ = neww * scale
snake_case__ = int(neww + 0.5)
snake_case__ = int(newh + 0.5)
if img.dtype == np.uinta:
snake_case__ = Image.fromarray(UpperCamelCase__)
snake_case__ = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR)
snake_case__ = np.asarray(UpperCamelCase__)
else:
snake_case__ = img.permute(2 , 0 , 1).unsqueeze(0) # 3, 0, 1) # hw(c) -> nchw
snake_case__ = nn.functional.interpolate(
UpperCamelCase__ , (newh, neww) , mode=self.interp_method , align_corners=UpperCamelCase__).squeeze(0)
img_augs.append(UpperCamelCase__)
return img_augs
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase__ : Optional[int]):
'''simple docstring'''
snake_case__ = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST)
snake_case__ = cfg.INPUT.FORMAT
snake_case__ = cfg.SIZE_DIVISIBILITY
snake_case__ = cfg.PAD_VALUE
snake_case__ = cfg.INPUT.MAX_SIZE_TEST
snake_case__ = cfg.MODEL.DEVICE
snake_case__ = torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD) , 1 , 1)
snake_case__ = torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD) , 1 , 1)
snake_case__ = lambda UpperCamelCase__: (x - self.pixel_mean) / self.pixel_std
def __magic_name__ ( self : Dict , UpperCamelCase__ : Dict):
'''simple docstring'''
snake_case__ = tuple(max(UpperCamelCase__) for s in zip(*[img.shape for img in images]))
snake_case__ = [im.shape[-2:] for im in images]
snake_case__ = [
nn.functional.pad(
UpperCamelCase__ , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(UpperCamelCase__ , UpperCamelCase__)
]
return torch.stack(UpperCamelCase__), torch.tensor(UpperCamelCase__)
def __call__( self : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : str=False):
'''simple docstring'''
with torch.no_grad():
if not isinstance(UpperCamelCase__ , UpperCamelCase__):
snake_case__ = [images]
if single_image:
assert len(UpperCamelCase__) == 1
for i in range(len(UpperCamelCase__)):
if isinstance(images[i] , torch.Tensor):
images.insert(UpperCamelCase__ , images.pop(UpperCamelCase__).to(self.device).float())
elif not isinstance(images[i] , torch.Tensor):
images.insert(
UpperCamelCase__ , torch.as_tensor(img_tensorize(images.pop(UpperCamelCase__) , input_format=self.input_format))
.to(self.device)
.float() , )
# resize smallest edge
snake_case__ = torch.tensor([im.shape[:2] for im in images])
snake_case__ = self.aug(UpperCamelCase__)
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
snake_case__ = [self.normalizer(UpperCamelCase__) for x in images]
# now pad them to do the following operations
snake_case__ , snake_case__ = self.pad(UpperCamelCase__)
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
snake_case__ = torch.true_divide(UpperCamelCase__ , UpperCamelCase__)
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def _UpperCAmelCase ( a : Optional[Any] , a : Any ):
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def _UpperCAmelCase ( a : Any , a : Tuple[int, int] ):
assert torch.isfinite(a ).all(), "Box tensor contains infinite or NaN!"
snake_case__ , snake_case__ = box_size
tensor[:, 0].clamp_(min=0 , max=a )
tensor[:, 1].clamp_(min=0 , max=a )
tensor[:, 2].clamp_(min=0 , max=a )
tensor[:, 3].clamp_(min=0 , max=a )
| 654
| 0
|
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : List[Any] , *_A : Any , **_A : int ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Tuple , *_A : Union[str, Any] , **_A : List[Any] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Optional[Any] , *_A : Optional[Any] , **_A : Union[str, Any] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : int , *_A : str , **_A : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Tuple , *_A : Union[str, Any] , **_A : List[Any] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Optional[Any] , *_A : Tuple , **_A : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : str , *_A : Union[str, Any] , **_A : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Tuple , *_A : str , **_A : Dict ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Tuple , *_A : Any , **_A : int ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Union[str, Any] , *_A : Tuple , **_A : Tuple ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Union[str, Any] , *_A : Optional[int] , **_A : List[str] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : int , *_A : Any , **_A : int ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : List[str] , *_A : Optional[Any] , **_A : List[Any] ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : int , *_A : Tuple , **_A : Dict ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : List[Any] , *_A : Optional[int] , **_A : Any ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Optional[Any] , *_A : Any , **_A : List[Any] ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Any , *_A : Optional[int] , **_A : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Optional[int] , *_A : Tuple , **_A : Tuple ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
| 708
|
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
def a__ ( snake_case , snake_case=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
('''cls_token''', '''vit.embeddings.cls_token'''),
('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
('''pos_embed''', '''vit.embeddings.position_embeddings'''),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__SCREAMING_SNAKE_CASE : Dict = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def a__ ( snake_case , snake_case , snake_case=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
__SCREAMING_SNAKE_CASE : Tuple = ''''''
else:
__SCREAMING_SNAKE_CASE : Dict = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__SCREAMING_SNAKE_CASE : List[Any] = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
__SCREAMING_SNAKE_CASE : Optional[int] = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
__SCREAMING_SNAKE_CASE : Tuple = in_proj_weight[
: config.hidden_size, :
]
__SCREAMING_SNAKE_CASE : List[Any] = in_proj_bias[: config.hidden_size]
__SCREAMING_SNAKE_CASE : Optional[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__SCREAMING_SNAKE_CASE : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__SCREAMING_SNAKE_CASE : List[str] = in_proj_weight[
-config.hidden_size :, :
]
__SCREAMING_SNAKE_CASE : Optional[int] = in_proj_bias[-config.hidden_size :]
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(snake_case , snake_case )
def a__ ( snake_case , snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = dct.pop(snake_case )
__SCREAMING_SNAKE_CASE : int = val
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
__SCREAMING_SNAKE_CASE : List[str] = Image.open(requests.get(snake_case , stream=snake_case ).raw )
return im
@torch.no_grad()
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = ViTConfig()
__SCREAMING_SNAKE_CASE : str = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
__SCREAMING_SNAKE_CASE : Tuple = True
__SCREAMING_SNAKE_CASE : Tuple = int(vit_name[-12:-10] )
__SCREAMING_SNAKE_CASE : Optional[Any] = int(vit_name[-9:-6] )
else:
__SCREAMING_SNAKE_CASE : Dict = 1_000
__SCREAMING_SNAKE_CASE : str = '''huggingface/label-files'''
__SCREAMING_SNAKE_CASE : Any = '''imagenet-1k-id2label.json'''
__SCREAMING_SNAKE_CASE : int = json.load(open(hf_hub_download(snake_case , snake_case , repo_type='''dataset''' ) , '''r''' ) )
__SCREAMING_SNAKE_CASE : List[Any] = {int(snake_case ): v for k, v in idalabel.items()}
__SCREAMING_SNAKE_CASE : Union[str, Any] = idalabel
__SCREAMING_SNAKE_CASE : Any = {v: k for k, v in idalabel.items()}
__SCREAMING_SNAKE_CASE : Any = int(vit_name[-6:-4] )
__SCREAMING_SNAKE_CASE : str = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith('''tiny''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = 192
__SCREAMING_SNAKE_CASE : Optional[Any] = 768
__SCREAMING_SNAKE_CASE : int = 12
__SCREAMING_SNAKE_CASE : Tuple = 3
elif vit_name[9:].startswith('''small''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = 384
__SCREAMING_SNAKE_CASE : List[Any] = 1_536
__SCREAMING_SNAKE_CASE : str = 12
__SCREAMING_SNAKE_CASE : Tuple = 6
else:
pass
else:
if vit_name[4:].startswith('''small''' ):
__SCREAMING_SNAKE_CASE : Optional[Any] = 768
__SCREAMING_SNAKE_CASE : Any = 2_304
__SCREAMING_SNAKE_CASE : Any = 8
__SCREAMING_SNAKE_CASE : Any = 8
elif vit_name[4:].startswith('''base''' ):
pass
elif vit_name[4:].startswith('''large''' ):
__SCREAMING_SNAKE_CASE : Tuple = 1_024
__SCREAMING_SNAKE_CASE : Tuple = 4_096
__SCREAMING_SNAKE_CASE : Any = 24
__SCREAMING_SNAKE_CASE : Optional[int] = 16
elif vit_name[4:].startswith('''huge''' ):
__SCREAMING_SNAKE_CASE : str = 1_280
__SCREAMING_SNAKE_CASE : Any = 5_120
__SCREAMING_SNAKE_CASE : Tuple = 32
__SCREAMING_SNAKE_CASE : str = 16
# load original model from timm
__SCREAMING_SNAKE_CASE : Dict = timm.create_model(snake_case , pretrained=snake_case )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
__SCREAMING_SNAKE_CASE : Tuple = timm_model.state_dict()
if base_model:
remove_classification_head_(snake_case )
__SCREAMING_SNAKE_CASE : Tuple = create_rename_keys(snake_case , snake_case )
for src, dest in rename_keys:
rename_key(snake_case , snake_case , snake_case )
read_in_q_k_v(snake_case , snake_case , snake_case )
# load HuggingFace model
if vit_name[-5:] == "in21k":
__SCREAMING_SNAKE_CASE : Dict = ViTModel(snake_case ).eval()
else:
__SCREAMING_SNAKE_CASE : List[Any] = ViTForImageClassification(snake_case ).eval()
model.load_state_dict(snake_case )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
__SCREAMING_SNAKE_CASE : List[str] = DeiTImageProcessor(size=config.image_size )
else:
__SCREAMING_SNAKE_CASE : List[Any] = ViTImageProcessor(size=config.image_size )
__SCREAMING_SNAKE_CASE : List[str] = image_processor(images=prepare_img() , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : Dict = encoding['''pixel_values''']
__SCREAMING_SNAKE_CASE : Optional[int] = model(snake_case )
if base_model:
__SCREAMING_SNAKE_CASE : int = timm_model.forward_features(snake_case )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(snake_case , outputs.pooler_output , atol=1E-3 )
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = timm_model(snake_case )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(snake_case , outputs.logits , atol=1E-3 )
Path(snake_case ).mkdir(exist_ok=snake_case )
print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(snake_case )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(snake_case )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--vit_name""",
default="""vit_base_patch16_224""",
type=str,
help="""Name of the ViT timm 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."""
)
lowercase_ = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 131
| 0
|
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class __A( a ):
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=False , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=64 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , _snake_case=2 , _snake_case=2 , _snake_case=2 , _snake_case=2 , _snake_case=4 , _snake_case=1 , ) -> Dict:
'''simple docstring'''
__a = parent
__a = batch_size
__a = seq_length
__a = is_training
__a = use_input_mask
__a = use_token_type_ids
__a = use_labels
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = type_vocab_size
__a = type_sequence_label_size
__a = initializer_range
__a = num_labels
__a = num_choices
__a = scope
__a = q_groups
__a = k_groups
__a = v_groups
__a = post_attention_groups
__a = intermediate_groups
__a = output_groups
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a = None
if self.use_input_mask:
__a = random_attention_mask([self.batch_size, self.seq_length] )
__a = None
__a = None
__a = None
if self.use_labels:
__a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a = ids_tensor([self.batch_size] , self.num_choices )
__a = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
return SqueezeBertConfig(
embedding_size=self.hidden_size , 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 , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> Union[str, Any]:
'''simple docstring'''
__a = SqueezeBertModel(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = model(_snake_case , _snake_case )
__a = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> str:
'''simple docstring'''
__a = SqueezeBertForMaskedLM(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = 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 SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = SqueezeBertForQuestionAnswering(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = model(
_snake_case , attention_mask=_snake_case , start_positions=_snake_case , end_positions=_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 SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> Any:
'''simple docstring'''
__a = self.num_labels
__a = SqueezeBertForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
__a = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> Tuple:
'''simple docstring'''
__a = self.num_labels
__a = SqueezeBertForTokenClassification(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = self.num_choices
__a = SqueezeBertForMultipleChoice(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a = model(
_snake_case , attention_mask=_snake_case , labels=_snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = self.prepare_config_and_inputs()
((__a) , (__a) , (__a) , (__a) , (__a) , (__a)) = config_and_inputs
__a = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A( a , a , unittest.TestCase ):
snake_case_ = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
snake_case_ = (
{
'''feature-extraction''': SqueezeBertModel,
'''fill-mask''': SqueezeBertForMaskedLM,
'''question-answering''': SqueezeBertForQuestionAnswering,
'''text-classification''': SqueezeBertForSequenceClassification,
'''token-classification''': SqueezeBertForTokenClassification,
'''zero-shot''': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = True
snake_case_ = False
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = SqueezeBertModelTester(self )
__a = ConfigTester(self , config_class=_snake_case , dim=37 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_snake_case )
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a = SqueezeBertModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@require_sentencepiece
@require_tokenizers
@require_torch
class __A( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli''' )
__a = torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]] )
__a = model(_snake_case )[0]
__a = torch.Size((1, 3) )
self.assertEqual(output.shape , _snake_case )
__a = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(_snake_case , _snake_case , atol=1E-4 ) )
| 219
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : List[str] = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Any = [
'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
'LiltForQuestionAnswering',
'LiltForSequenceClassification',
'LiltForTokenClassification',
'LiltModel',
'LiltPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
A : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 219
| 1
|
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
SCREAMING_SNAKE_CASE_ : str = 4
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (1 << p) - 1
for _ in range(p - 2 ):
SCREAMING_SNAKE_CASE_ : Optional[int] = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(1_1))
| 707
|
'''simple docstring'''
import re
import string
import numpy as np
import datasets
snake_case_ = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
snake_case_ = '\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results["exact_match"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 33.3\n\n'
snake_case_ = '\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,_KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE__ ( datasets.Metric ):
def __lowerCamelCase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , reference_urls=[] , )
def __lowerCamelCase ( self , lowercase__ , lowercase__ , lowercase__=None , lowercase__=False , lowercase__=False , lowercase__=False , ):
"""simple docstring"""
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
SCREAMING_SNAKE_CASE_ : Optional[Any] = np.array([re.sub(lowercase__ , "" , lowercase__ ) for x in predictions] )
SCREAMING_SNAKE_CASE_ : List[Any] = np.array([re.sub(lowercase__ , "" , lowercase__ ) for x in references] )
else:
SCREAMING_SNAKE_CASE_ : int = np.asarray(lowercase__ )
SCREAMING_SNAKE_CASE_ : Any = np.asarray(lowercase__ )
if ignore_case:
SCREAMING_SNAKE_CASE_ : Dict = np.char.lower(lowercase__ )
SCREAMING_SNAKE_CASE_ : List[Any] = np.char.lower(lowercase__ )
if ignore_punctuation:
SCREAMING_SNAKE_CASE_ : Optional[int] = string.punctuation.maketrans("" , "" , string.punctuation )
SCREAMING_SNAKE_CASE_ : int = np.char.translate(lowercase__ , table=lowercase__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = np.char.translate(lowercase__ , table=lowercase__ )
if ignore_numbers:
SCREAMING_SNAKE_CASE_ : Optional[int] = string.digits.maketrans("" , "" , string.digits )
SCREAMING_SNAKE_CASE_ : Dict = np.char.translate(lowercase__ , table=lowercase__ )
SCREAMING_SNAKE_CASE_ : int = np.char.translate(lowercase__ , table=lowercase__ )
SCREAMING_SNAKE_CASE_ : str = predictions == references
return {"exact_match": np.mean(lowercase__ ) * 100}
| 68
| 0
|
'''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
lowerCAmelCase_ : Tuple = get_tests_dir('''fixtures''')
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case_ (self ):
# A mock response for an HTTP head request to emulate server down
_UpperCAmelCase : List[str] = mock.Mock()
_UpperCAmelCase : List[str] = 5_0_0
_UpperCAmelCase : Union[str, Any] = {}
_UpperCAmelCase : int = HTTPError
_UpperCAmelCase : Dict = {}
# Download this model to make sure it's in the cache.
_UpperCAmelCase : str = 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:
_UpperCAmelCase : Union[str, Any] = ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit""" )
# This check we did call the fake head request
mock_head.assert_called()
def snake_case_ (self ):
# This test is for deprecated behavior and can be removed in v5
_UpperCAmelCase : int = ViTImageProcessor.from_pretrained(
"""https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json""" )
def snake_case_ (self ):
with self.assertRaises(lowerCAmelCase__ ):
# config is in subfolder, the following should not work without specifying the subfolder
_UpperCAmelCase : List[str] = AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants""" )
_UpperCAmelCase : Any = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/stable-diffusion-all-variants""" , subfolder="""feature_extractor""" )
self.assertIsNotNone(lowerCAmelCase__ )
@is_staging_test
class __lowerCAmelCase ( unittest.TestCase ):
@classmethod
def snake_case_ (cls ):
_UpperCAmelCase : Dict = TOKEN
HfFolder.save_token(lowerCAmelCase__ )
@classmethod
def snake_case_ (cls ):
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 snake_case_ (self ):
_UpperCAmelCase : List[str] = ViTImageProcessor.from_pretrained(lowerCAmelCase__ )
image_processor.push_to_hub("""test-image-processor""" , use_auth_token=self._token )
_UpperCAmelCase : Optional[Any] = 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 )
_UpperCAmelCase : Tuple = ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
def snake_case_ (self ):
_UpperCAmelCase : Optional[Any] = ViTImageProcessor.from_pretrained(lowerCAmelCase__ )
image_processor.push_to_hub("""valid_org/test-image-processor""" , use_auth_token=self._token )
_UpperCAmelCase : 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 )
_UpperCAmelCase : str = ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org""" )
for k, v in image_processor.__dict__.items():
self.assertEqual(lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
def snake_case_ (self ):
CustomImageProcessor.register_for_auto_class()
_UpperCAmelCase : Union[str, 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"""} , )
_UpperCAmelCase : List[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""" )
| 414
|
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowerCAmelCase ( __a ):
def __init__(self , lowerCAmelCase__ , lowerCAmelCase__ ):
super().__init__()
# make sure scheduler can always be converted to DDIM
_UpperCAmelCase : Any = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ )
@torch.no_grad()
def __call__(self , lowerCAmelCase__ = 1 , lowerCAmelCase__ = None , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = 5_0 , lowerCAmelCase__ = None , lowerCAmelCase__ = "pil" , lowerCAmelCase__ = True , ):
# Sample gaussian noise to begin loop
if isinstance(self.unet.config.sample_size , lowerCAmelCase__ ):
_UpperCAmelCase : Tuple = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
_UpperCAmelCase : Tuple = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and len(lowerCAmelCase__ ) != batch_size:
raise ValueError(
F"You have passed a list of generators of length {len(lowerCAmelCase__ )}, but requested an effective batch"
F" size of {batch_size}. Make sure the batch size matches the length of the generators." )
_UpperCAmelCase : List[str] = randn_tensor(lowerCAmelCase__ , generator=lowerCAmelCase__ , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(lowerCAmelCase__ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
_UpperCAmelCase : Any = self.unet(lowerCAmelCase__ , lowerCAmelCase__ ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
_UpperCAmelCase : Dict = self.scheduler.step(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , eta=lowerCAmelCase__ , use_clipped_model_output=lowerCAmelCase__ , generator=lowerCAmelCase__ ).prev_sample
_UpperCAmelCase : Tuple = (image / 2 + 0.5).clamp(0 , 1 )
_UpperCAmelCase : str = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase : int = self.numpy_to_pil(lowerCAmelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCAmelCase__ )
| 414
| 1
|
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[Any] ={
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
),
}
class _lowercase ( _lowercase ):
a = "van"
def __init__( self: int , UpperCamelCase__: Tuple=224 , UpperCamelCase__: Optional[int]=3 , UpperCamelCase__: str=[7, 3, 3, 3] , UpperCamelCase__: List[Any]=[4, 2, 2, 2] , UpperCamelCase__: int=[64, 128, 320, 512] , UpperCamelCase__: Dict=[3, 3, 12, 3] , UpperCamelCase__: Union[str, Any]=[8, 8, 4, 4] , UpperCamelCase__: Dict="gelu" , UpperCamelCase__: Any=0.02 , UpperCamelCase__: List[Any]=1e-6 , UpperCamelCase__: Optional[Any]=1e-2 , UpperCamelCase__: Optional[Any]=0.0 , UpperCamelCase__: List[Any]=0.0 , **UpperCamelCase__: Optional[Any] , ):
super().__init__(**UpperCamelCase__ )
lowerCamelCase__ : Union[str, Any] = image_size
lowerCamelCase__ : Optional[int] = num_channels
lowerCamelCase__ : Any = patch_sizes
lowerCamelCase__ : Union[str, Any] = strides
lowerCamelCase__ : List[str] = hidden_sizes
lowerCamelCase__ : Tuple = depths
lowerCamelCase__ : str = mlp_ratios
lowerCamelCase__ : Dict = hidden_act
lowerCamelCase__ : str = initializer_range
lowerCamelCase__ : Tuple = layer_norm_eps
lowerCamelCase__ : Union[str, Any] = layer_scale_init_value
lowerCamelCase__ : Tuple = drop_path_rate
lowerCamelCase__ : Optional[Any] = dropout_rate
| 700
|
'''simple docstring'''
# 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
_A : Dict ='''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)
| 631
| 0
|
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ):
"""simple docstring"""
_lowerCamelCase : int = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("Only one argument must be 0" )
elif power < 0:
raise ValueError(
"Power cannot be negative in any electrical/electronics system" )
elif voltage == 0:
return result("voltage" , power / current )
elif current == 0:
return result("current" , power / voltage )
elif power == 0:
return result("power" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44
|
'''simple docstring'''
class UpperCAmelCase__ :
def __init__( self : Any,__A : Any,__A : Any,__A : Any ):
_lowerCamelCase : List[Any] = name
_lowerCamelCase : Union[str, Any] = value
_lowerCamelCase : str = weight
def __repr__( self : Any ):
return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
def lowerCamelCase_ ( self : Optional[int] ):
return self.value
def lowerCamelCase_ ( self : Any ):
return self.name
def lowerCamelCase_ ( self : List[Any] ):
return self.weight
def lowerCamelCase_ ( self : str ):
return self.value / self.weight
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = []
for i in range(len(_lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = sorted(_lowerCAmelCase , key=_lowerCAmelCase , reverse=_lowerCAmelCase )
_lowerCamelCase : Optional[int] = []
_lowerCamelCase , _lowerCamelCase : Optional[int] = 0.0, 0.0
for i in range(len(_lowerCAmelCase ) ):
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 A_ ( ):
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44
| 1
|
import numpy as np
class UpperCamelCase:
def __init__( self : Optional[int] ) -> Tuple:
'''simple docstring'''
__snake_case = (0, 0)
__snake_case = None
__snake_case = 0
__snake_case = 0
__snake_case = 0
def __eq__( self : str , SCREAMING_SNAKE_CASE : str ) -> str:
'''simple docstring'''
return self.position == cell.position
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
print(self.position )
class UpperCamelCase:
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : List[str]=(5, 5) ) -> List[Any]:
'''simple docstring'''
__snake_case = np.zeros(__UpperCamelCase )
__snake_case = world_size[0]
__snake_case = world_size[1]
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
print(self.w )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , SCREAMING_SNAKE_CASE : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
__snake_case = cell.position[0]
__snake_case = cell.position[1]
__snake_case = []
for n in neughbour_cord:
__snake_case = current_x + n[0]
__snake_case = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
__snake_case = Cell()
__snake_case = (x, y)
__snake_case = cell
neighbours.append(__UpperCamelCase )
return neighbours
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
__snake_case = []
__snake_case = []
_open.append(UpperCAmelCase__ )
while _open:
__snake_case = np.argmin([n.f for n in _open] )
__snake_case = _open[min_f]
_closed.append(_open.pop(UpperCAmelCase__ ) )
if current == goal:
break
for n in world.get_neigbours(UpperCAmelCase__ ):
for c in _closed:
if c == n:
continue
__snake_case = current.g + 1
__snake_case , __snake_case = n.position
__snake_case , __snake_case = goal.position
__snake_case = (ya - ya) ** 2 + (xa - xa) ** 2
__snake_case = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(UpperCAmelCase__ )
__snake_case = []
while current.parent is not None:
path.append(current.position )
__snake_case = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
A : Any = Gridworld()
# Start position and goal
A : List[str] = Cell()
A : List[Any] = (0, 0)
A : Optional[Any] = Cell()
A : Optional[Any] = (4, 4)
print(f'''path from {start.position} to {goal.position}''')
A : Tuple = astar(world, start, goal)
# Just for visual reasons.
for i in s:
A : Optional[int] = 1
print(world.w)
| 715
|
class UpperCamelCase:
def __init__( self : Any ) -> Any:
'''simple docstring'''
__snake_case = 0
__snake_case = 0
__snake_case = {}
def SCREAMING_SNAKE_CASE_ ( self : Dict , SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
if vertex not in self.adjacency:
__snake_case = {}
self.num_vertices += 1
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
'''simple docstring'''
self.add_vertex(SCREAMING_SNAKE_CASE )
self.add_vertex(SCREAMING_SNAKE_CASE )
if head == tail:
return
__snake_case = weight
__snake_case = weight
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
__snake_case = self.get_edges()
for edge in edges:
__snake_case , __snake_case , __snake_case = edge
edges.remove((tail, head, weight) )
for i in range(len(SCREAMING_SNAKE_CASE ) ):
__snake_case = list(edges[i] )
edges.sort(key=lambda SCREAMING_SNAKE_CASE : e[2] )
for i in range(len(SCREAMING_SNAKE_CASE ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
__snake_case = edges[i][2] + 1
for edge in edges:
__snake_case , __snake_case , __snake_case = edge
__snake_case = weight
__snake_case = weight
def __str__( self : Tuple ) -> List[Any]:
'''simple docstring'''
__snake_case = ""
for tail in self.adjacency:
for head in self.adjacency[tail]:
__snake_case = self.adjacency[head][tail]
string += f'''{head} -> {tail} == {weight}\n'''
return string.rstrip("\n" )
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
__snake_case = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def SCREAMING_SNAKE_CASE_ ( self : Any ) -> List[Any]:
'''simple docstring'''
return self.adjacency.keys()
@staticmethod
def SCREAMING_SNAKE_CASE_ ( SCREAMING_SNAKE_CASE : Any=None , SCREAMING_SNAKE_CASE : List[Any]=None ) -> int:
'''simple docstring'''
__snake_case = Graph()
if vertices is None:
__snake_case = []
if edges is None:
__snake_case = []
for vertex in vertices:
g.add_vertex(SCREAMING_SNAKE_CASE )
for edge in edges:
g.add_edge(*SCREAMING_SNAKE_CASE )
return g
class UpperCamelCase:
def __init__( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case = {}
__snake_case = {}
def __len__( self : List[str] ) -> Dict:
'''simple docstring'''
return len(self.parent )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , SCREAMING_SNAKE_CASE : int ) -> List[str]:
'''simple docstring'''
if item in self.parent:
return self.find(SCREAMING_SNAKE_CASE )
__snake_case = item
__snake_case = 0
return item
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , SCREAMING_SNAKE_CASE : List[str] ) -> Any:
'''simple docstring'''
if item not in self.parent:
return self.make_set(SCREAMING_SNAKE_CASE )
if item != self.parent[item]:
__snake_case = self.find(self.parent[item] )
return self.parent[item]
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Dict:
'''simple docstring'''
__snake_case = self.find(SCREAMING_SNAKE_CASE )
__snake_case = self.find(SCREAMING_SNAKE_CASE )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
__snake_case = roota
return roota
if self.rank[roota] < self.rank[roota]:
__snake_case = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
__snake_case = roota
return roota
return None
@staticmethod
def SCREAMING_SNAKE_CASE_ ( SCREAMING_SNAKE_CASE : str ) -> Any:
'''simple docstring'''
__snake_case = graph.num_vertices
__snake_case = Graph.UnionFind()
__snake_case = []
while num_components > 1:
__snake_case = {}
for vertex in graph.get_vertices():
__snake_case = -1
__snake_case = graph.get_edges()
for edge in edges:
__snake_case , __snake_case , __snake_case = edge
edges.remove((tail, head, weight) )
for edge in edges:
__snake_case , __snake_case , __snake_case = edge
__snake_case = union_find.find(SCREAMING_SNAKE_CASE )
__snake_case = union_find.find(SCREAMING_SNAKE_CASE )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
__snake_case = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
__snake_case = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
__snake_case , __snake_case , __snake_case = cheap_edge[vertex]
if union_find.find(SCREAMING_SNAKE_CASE ) != union_find.find(SCREAMING_SNAKE_CASE ):
union_find.union(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
mst_edges.append(cheap_edge[vertex] )
__snake_case = num_components - 1
__snake_case = Graph.build(edges=SCREAMING_SNAKE_CASE )
return mst
| 473
| 0
|
from string import ascii_lowercase, ascii_uppercase
def __a ( A__ : str ):
if not sentence:
return ""
SCREAMING_SNAKE_CASE = dict(zip(A__ , A__ ) )
return lower_to_upper.get(sentence[0] , sentence[0] ) + sentence[1:]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 16
|
'''simple docstring'''
SCREAMING_SNAKE_CASE = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Exponent of the factor(meter)
SCREAMING_SNAKE_CASE = {
"""m""": 0,
"""km""": 3,
"""Mm""": 6,
"""Gm""": 9,
"""Tm""": 1_2,
"""Pm""": 1_5,
"""Em""": 1_8,
"""Zm""": 2_1,
"""Ym""": 2_4,
}
def snake_case_ ( lowercase__ , lowercase__ , lowercase__ ):
UpperCAmelCase__ : str = from_type.lower().strip("s" )
UpperCAmelCase__ : Any = to_type.lower().strip("s" )
UpperCAmelCase__ : Union[str, Any] = UNIT_SYMBOL.get(lowercase__ , lowercase__ )
UpperCAmelCase__ : Any = UNIT_SYMBOL.get(lowercase__ , lowercase__ )
if from_sanitized not in METRIC_CONVERSION:
UpperCAmelCase__ : str = (
F"""Invalid 'from_type' value: {from_type!r}.\n"""
F"""Conversion abbreviations are: {", ".join(lowercase__ )}"""
)
raise ValueError(lowercase__ )
if to_sanitized not in METRIC_CONVERSION:
UpperCAmelCase__ : List[str] = (
F"""Invalid 'to_type' value: {to_type!r}.\n"""
F"""Conversion abbreviations are: {", ".join(lowercase__ )}"""
)
raise ValueError(lowercase__ )
UpperCAmelCase__ : Optional[int] = METRIC_CONVERSION[from_sanitized]
UpperCAmelCase__ : str = METRIC_CONVERSION[to_sanitized]
UpperCAmelCase__ : Tuple = 1
if from_exponent > to_exponent:
UpperCAmelCase__ : Tuple = from_exponent - to_exponent
else:
UpperCAmelCase__ : Optional[int] = -(to_exponent - from_exponent)
return value * pow(1_0 , lowercase__ )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 199
| 0
|
'''simple docstring'''
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ) -> Dict:
"""simple docstring"""
if index == r:
for j in range(snake_case__ ):
print(data[j] ,end=""" """ )
print(""" """ )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
_SCREAMING_SNAKE_CASE = arr[i]
combination_util(snake_case__ ,snake_case__ ,snake_case__ ,index + 1 ,snake_case__ ,i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(snake_case__ ,snake_case__ ,snake_case__ ,0 ,snake_case__ ,0 )
if __name__ == "__main__":
# Driver code to check the function above
UpperCamelCase = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 719
|
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class __UpperCAmelCase :
def __init__( self: Optional[Any] , UpperCAmelCase_: Tuple , UpperCAmelCase_: str=99 , UpperCAmelCase_: Optional[int]=13 , UpperCAmelCase_: Dict=16 , UpperCAmelCase_: Union[str, Any]=7 , UpperCAmelCase_: int=True , UpperCAmelCase_: int=True , UpperCAmelCase_: Any=True , UpperCAmelCase_: List[Any]=False , UpperCAmelCase_: str=True , UpperCAmelCase_: Optional[Any]=2 , UpperCAmelCase_: int=32 , UpperCAmelCase_: Union[str, Any]=4 , UpperCAmelCase_: List[Any]=4 , UpperCAmelCase_: Optional[int]=30 , UpperCAmelCase_: Dict=0 , UpperCAmelCase_: List[str]=1 , UpperCAmelCase_: Optional[Any]=2 , UpperCAmelCase_: Union[str, Any]=None , ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = decoder_seq_length
# For common tests
_SCREAMING_SNAKE_CASE = self.decoder_seq_length
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_attention_mask
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = d_model
_SCREAMING_SNAKE_CASE = d_model
_SCREAMING_SNAKE_CASE = decoder_layers
_SCREAMING_SNAKE_CASE = decoder_layers
_SCREAMING_SNAKE_CASE = decoder_ffn_dim
_SCREAMING_SNAKE_CASE = decoder_attention_heads
_SCREAMING_SNAKE_CASE = decoder_attention_heads
_SCREAMING_SNAKE_CASE = eos_token_id
_SCREAMING_SNAKE_CASE = bos_token_id
_SCREAMING_SNAKE_CASE = pad_token_id
_SCREAMING_SNAKE_CASE = decoder_start_token_id
_SCREAMING_SNAKE_CASE = use_cache
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = decoder_seq_length
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 1
def UpperCamelCase ( self: Optional[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = None
if self.use_attention_mask:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = TrOCRConfig(
vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )
return (config, input_ids, attention_mask, lm_labels)
def UpperCamelCase ( self: Tuple , UpperCAmelCase_: Tuple , UpperCAmelCase_: List[str] , UpperCAmelCase_: Union[str, Any] , UpperCAmelCase_: Optional[Any] , ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = TrOCRDecoder(config=UpperCAmelCase_ ).to(UpperCAmelCase_ ).eval()
_SCREAMING_SNAKE_CASE = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase_ , use_cache=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase_ , use_cache=UpperCAmelCase_ )
self.parent.assertTrue(len(UpperCAmelCase_ ) == len(UpperCAmelCase_ ) )
self.parent.assertTrue(len(UpperCAmelCase_ ) == len(UpperCAmelCase_ ) + 1 )
_SCREAMING_SNAKE_CASE = outputs["""past_key_values"""]
# create hypothetical next token and extent to next_input_ids
_SCREAMING_SNAKE_CASE = ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
_SCREAMING_SNAKE_CASE = torch.cat([input_ids, next_tokens] , dim=-1 )
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase_ )["""last_hidden_state"""]
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase_ , past_key_values=UpperCAmelCase_ )["""last_hidden_state"""]
# select random slice
_SCREAMING_SNAKE_CASE = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_SCREAMING_SNAKE_CASE = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
_SCREAMING_SNAKE_CASE = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(UpperCAmelCase_ , UpperCAmelCase_ , atol=1E-3 )
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_torch
class __UpperCAmelCase (_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,unittest.TestCase ):
__snake_case : Optional[Any] = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
__snake_case : Optional[Any] = (TrOCRForCausalLM,) if is_torch_available() else ()
__snake_case : Tuple = {"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
__snake_case : str = True
__snake_case : List[str] = False
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = TrOCRStandaloneDecoderModelTester(self , is_training=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=UpperCAmelCase_ )
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
pass
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
pass
def UpperCamelCase ( self: str ):
'''simple docstring'''
pass
def UpperCamelCase ( self: Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*UpperCAmelCase_ )
def UpperCamelCase ( self: Any ):
'''simple docstring'''
return
@unittest.skip("""The model doesn't support left padding""" ) # and it's not used enough to be worth fixing :)
def UpperCamelCase ( self: Any ):
'''simple docstring'''
pass
| 569
| 0
|
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : str = {
'''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Tuple = 'git_vision_model'
def __init__( self , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=3072 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=224 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_="quick_gelu" , SCREAMING_SNAKE_CASE_=1e-5 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = hidden_size
UpperCamelCase : Optional[Any] = intermediate_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : List[str] = num_attention_heads
UpperCamelCase : List[Any] = num_channels
UpperCamelCase : Tuple = patch_size
UpperCamelCase : Union[str, Any] = image_size
UpperCamelCase : Optional[int] = initializer_range
UpperCamelCase : str = attention_dropout
UpperCamelCase : Optional[Any] = layer_norm_eps
UpperCamelCase : str = hidden_act
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : List[Any] = cls.get_config_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("""model_type""" ) == "git":
UpperCamelCase : 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(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = 'git'
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=3_0522 , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=3072 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1e-12 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_="absolute" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=101 , SCREAMING_SNAKE_CASE_=102 , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , pad_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if vision_config is None:
UpperCamelCase : Dict = {}
logger.info("""vision_config is None. initializing the GitVisionConfig with default values.""" )
UpperCamelCase : Union[str, Any] = GitVisionConfig(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Optional[int] = hidden_size
UpperCamelCase : int = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : Tuple = hidden_act
UpperCamelCase : List[str] = intermediate_size
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : Tuple = attention_probs_dropout_prob
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : List[str] = initializer_range
UpperCamelCase : str = layer_norm_eps
UpperCamelCase : Any = position_embedding_type
UpperCamelCase : List[Any] = use_cache
UpperCamelCase : Optional[Any] = tie_word_embeddings
UpperCamelCase : Tuple = num_image_with_embedding
UpperCamelCase : Optional[int] = bos_token_id
UpperCamelCase : Any = eos_token_id
def a_ ( self ):
UpperCamelCase : Optional[Any] = copy.deepcopy(self.__dict__ )
UpperCamelCase : List[Any] = self.vision_config.to_dict()
UpperCamelCase : Dict = self.__class__.model_type
return output
| 499
|
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : List[Any] = checkpoints.load_tax_checkpoint(snake_case_ )
UpperCamelCase : Dict = flatten_dict(snake_case_ )
return flax_params
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
UpperCamelCase : Optional[int] = {}
UpperCamelCase : Tuple = {
"""token_embedder""": """embeddings""",
"""encoder_norm""": """layernorm""",
"""kernel""": """weight""",
""".out""": """.output""",
"""scale""": """weight""",
"""embedders_0.pos_embedding""": """row_embedder.weight""",
"""embedders_1.pos_embedding""": """column_embedder.weight""",
}
UpperCamelCase : Optional[int] = {
"""query""": """attention.query""",
"""key""": """attention.key""",
"""value""": """attention.value""",
"""output.dense""": """output""",
"""encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
"""pre_self_attention_layer_norm""": """self_attention.layer_norm""",
"""pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
"""mlp.""": """mlp.DenseReluDense.""",
"""pre_mlp_layer_norm""": """mlp.layer_norm""",
"""self_attention.o""": """self_attention.attention.o""",
"""decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
"""decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
"""decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.logits_dense.weight""": """decoder.lm_head.weight""",
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
UpperCamelCase : List[str] = """.""".join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
UpperCamelCase : List[str] = new_key.replace(snake_case_ ,snake_case_ )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
UpperCamelCase : str = new_key.replace(snake_case_ ,snake_case_ )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
UpperCamelCase : List[str] = re.sub(R"""layers_(\d+)""" ,R"""layer.\1""" ,snake_case_ )
UpperCamelCase : Union[str, Any] = new_key.replace("""encoder""" ,"""encoder.encoder""" )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
UpperCamelCase : Any = re.sub(R"""layers_(\d+)""" ,R"""layer.\1""" ,snake_case_ )
UpperCamelCase : Any = flax_dict[key]
UpperCamelCase : Optional[Any] = {}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
UpperCamelCase : Union[str, Any] = torch.from_numpy(converted_dict[key].T )
else:
UpperCamelCase : List[Any] = torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Dict ,snake_case_ : str=False ,snake_case_ : Dict=False ):
'''simple docstring'''
UpperCamelCase : Optional[int] = get_flax_param(snake_case_ )
if not use_large:
UpperCamelCase : List[str] = PixaStructVisionConfig()
UpperCamelCase : int = PixaStructTextConfig()
else:
UpperCamelCase : List[str] = PixaStructVisionConfig(
hidden_size=1_5_3_6 ,d_ff=3_9_6_8 ,num_attention_heads=2_4 ,num_hidden_layers=1_8 )
UpperCamelCase : Tuple = PixaStructTextConfig(hidden_size=1_5_3_6 ,d_ff=3_9_6_8 ,num_heads=2_4 ,num_layers=1_8 )
UpperCamelCase : List[Any] = PixaStructConfig(
vision_config=encoder_config.to_dict() ,text_config=decoder_config.to_dict() ,is_vqa=snake_case_ )
UpperCamelCase : Optional[int] = PixaStructForConditionalGeneration(snake_case_ )
UpperCamelCase : Optional[Any] = rename_and_convert_flax_params(snake_case_ )
model.load_state_dict(snake_case_ )
UpperCamelCase : List[Any] = AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer""" )
UpperCamelCase : Dict = PixaStructImageProcessor()
UpperCamelCase : List[str] = PixaStructProcessor(image_processor=snake_case_ ,tokenizer=snake_case_ )
if use_large:
UpperCamelCase : int = 4_0_9_6
UpperCamelCase : int = True
# mkdir if needed
os.makedirs(snake_case_ ,exist_ok=snake_case_ )
model.save_pretrained(snake_case_ )
processor.save_pretrained(snake_case_ )
print("""Model saved in {}""".format(snake_case_ ) )
if __name__ == "__main__":
__A : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
__A : Optional[Any] = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 499
| 1
|
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase_ ( a):
def __init__( self, __a, __a=13, __a=7, __a=True, __a=True, __a=True, __a=True, __a=99, __a=32, __a=5, __a=4, __a=37, __a="gelu", __a=0.1, __a=0.1, __a=512, __a=16, __a=2, __a=0.02, __a=False, __a=True, __a="None", __a=3, __a=4, __a=None, ):
'''simple docstring'''
_lowerCAmelCase : Tuple = parent
_lowerCAmelCase : Dict = batch_size
_lowerCAmelCase : str = seq_length
_lowerCAmelCase : Dict = is_training
_lowerCAmelCase : int = use_input_mask
_lowerCAmelCase : Union[str, Any] = use_token_type_ids
_lowerCAmelCase : List[Any] = use_labels
_lowerCAmelCase : List[str] = vocab_size
_lowerCAmelCase : int = hidden_size
_lowerCAmelCase : Tuple = num_hidden_layers
_lowerCAmelCase : List[str] = num_attention_heads
_lowerCAmelCase : Any = intermediate_size
_lowerCAmelCase : Dict = hidden_act
_lowerCAmelCase : str = hidden_dropout_prob
_lowerCAmelCase : Optional[Any] = attention_probs_dropout_prob
_lowerCAmelCase : Any = max_position_embeddings
_lowerCAmelCase : List[str] = type_vocab_size
_lowerCAmelCase : str = type_sequence_label_size
_lowerCAmelCase : List[Any] = initializer_range
_lowerCAmelCase : str = num_labels
_lowerCAmelCase : List[str] = num_choices
_lowerCAmelCase : Optional[int] = relative_attention
_lowerCAmelCase : int = position_biased_input
_lowerCAmelCase : Dict = pos_att_type
_lowerCAmelCase : Optional[Any] = scope
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
_lowerCAmelCase : List[str] = None
if self.use_input_mask:
_lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
_lowerCAmelCase : Any = None
if self.use_token_type_ids:
_lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
_lowerCAmelCase : Tuple = None
_lowerCAmelCase : int = None
_lowerCAmelCase : Optional[Any] = None
if self.use_labels:
_lowerCAmelCase : List[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
_lowerCAmelCase : Tuple = ids_tensor([self.batch_size], self.num_choices)
_lowerCAmelCase : List[str] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case__ ( self):
'''simple docstring'''
return DebertaConfig(
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, relative_attention=self.relative_attention, position_biased_input=self.position_biased_input, pos_att_type=self.pos_att_type, )
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[str] = self.get_config()
_lowerCAmelCase : int = 300
return config
def snake_case__ ( self, __a):
'''simple docstring'''
self.parent.assertListEqual(list(result.loss.size()), [])
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = DebertaModel(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Dict = model(__a, attention_mask=__a, token_type_ids=__a)[0]
_lowerCAmelCase : Tuple = model(__a, token_type_ids=__a)[0]
_lowerCAmelCase : str = model(__a)[0]
self.parent.assertListEqual(list(sequence_output.size()), [self.batch_size, self.seq_length, self.hidden_size])
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Dict = DebertaForMaskedLM(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : int = model(__a, attention_mask=__a, token_type_ids=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Dict = self.num_labels
_lowerCAmelCase : Tuple = DebertaForSequenceClassification(__a)
model.to(__a)
model.eval()
_lowerCAmelCase : str = model(__a, attention_mask=__a, token_type_ids=__a, labels=__a)
self.parent.assertListEqual(list(result.logits.size()), [self.batch_size, self.num_labels])
self.check_loss_output(__a)
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : int = self.num_labels
_lowerCAmelCase : Tuple = DebertaForTokenClassification(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Optional[Any] = model(__a, attention_mask=__a, token_type_ids=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : str = DebertaForQuestionAnswering(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Tuple = model(
__a, attention_mask=__a, token_type_ids=__a, start_positions=__a, end_positions=__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 snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) : Optional[Any] = config_and_inputs
_lowerCAmelCase : Union[str, Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( a , a , unittest.TestCase):
lowerCamelCase__ = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
'feature-extraction': DebertaModel,
'fill-mask': DebertaForMaskedLM,
'question-answering': DebertaForQuestionAnswering,
'text-classification': DebertaForSequenceClassification,
'token-classification': DebertaForTokenClassification,
'zero-shot': DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = True
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = DebertaModelTester(self)
_lowerCAmelCase : Optional[int] = ConfigTester(self, config_class=__a, hidden_size=37)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__a)
@slow
def snake_case__ ( self):
'''simple docstring'''
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase : Optional[Any] = DebertaModel.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase_ ( unittest.TestCase):
@unittest.skip(reason="Model not available yet")
def snake_case__ ( self):
'''simple docstring'''
pass
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = DebertaModel.from_pretrained("microsoft/deberta-base")
_lowerCAmelCase : str = torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]])
_lowerCAmelCase : Optional[Any] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
with torch.no_grad():
_lowerCAmelCase : Dict = model(__a, attention_mask=__a)[0]
# compare the actual values for a slice.
_lowerCAmelCase : List[Any] = torch.tensor(
[[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]])
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], __a, atol=1E-4), f"{output[:, 1:4, 1:4]}")
| 658
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class UpperCAmelCase_ ( a , a):
lowerCamelCase__ = 'focalnet'
def __init__( self, __a=224, __a=4, __a=3, __a=96, __a=False, __a=[192, 384, 768, 768], __a=[2, 2, 6, 2], __a=[2, 2, 2, 2], __a=[3, 3, 3, 3], __a="gelu", __a=4.0, __a=0.0, __a=0.1, __a=False, __a=1E-4, __a=False, __a=False, __a=False, __a=0.02, __a=1E-5, __a=32, __a=None, __a=None, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : str = image_size
_lowerCAmelCase : List[str] = patch_size
_lowerCAmelCase : List[Any] = num_channels
_lowerCAmelCase : Tuple = embed_dim
_lowerCAmelCase : List[Any] = use_conv_embed
_lowerCAmelCase : Any = hidden_sizes
_lowerCAmelCase : Tuple = depths
_lowerCAmelCase : Dict = focal_levels
_lowerCAmelCase : Optional[Any] = focal_windows
_lowerCAmelCase : str = hidden_act
_lowerCAmelCase : Union[str, Any] = mlp_ratio
_lowerCAmelCase : Any = hidden_dropout_prob
_lowerCAmelCase : Dict = drop_path_rate
_lowerCAmelCase : str = use_layerscale
_lowerCAmelCase : str = layerscale_value
_lowerCAmelCase : Union[str, Any] = use_post_layernorm
_lowerCAmelCase : Optional[int] = use_post_layernorm_in_modulation
_lowerCAmelCase : str = normalize_modulator
_lowerCAmelCase : Any = initializer_range
_lowerCAmelCase : Union[str, Any] = layer_norm_eps
_lowerCAmelCase : Any = encoder_stride
_lowerCAmelCase : List[str] = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.depths) + 1)]
_lowerCAmelCase , _lowerCAmelCase : List[str] = get_aligned_output_features_output_indices(
out_features=__a, out_indices=__a, stage_names=self.stage_names)
| 658
| 1
|
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
_lowercase = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n'
_lowercase = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'
_lowercase = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase__ ( datasets.Metric ):
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://github.com/krishnap25/mauve""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/krishnap25/mauve"""] , reference_urls=[
"""https://arxiv.org/abs/2102.01454""",
"""https://github.com/krishnap25/mauve""",
] , )
def lowerCamelCase_ ( self : Dict , __a : int , __a : Union[str, Any] , __a : Optional[int]=None , __a : Tuple=None , __a : Union[str, Any]=None , __a : List[Any]=None , __a : Optional[int]="auto" , __a : Optional[Any]=-1 , __a : List[Any]=0.9 , __a : str=5 , __a : str=500 , __a : str="gpt2-large" , __a : Optional[int]=-1 , __a : Any=1024 , __a : Union[str, Any]=25 , __a : str=5 , __a : List[str]=True , __a : Optional[Any]=25 , ):
'''simple docstring'''
lowerCamelCase__: str = compute_mauve(
p_text=__a , q_text=__a , p_features=__a , q_features=__a , p_tokens=__a , q_tokens=__a , num_buckets=__a , pca_max_data=__a , kmeans_explained_var=__a , kmeans_num_redo=__a , kmeans_max_iter=__a , featurize_model_name=__a , device_id=__a , max_text_length=__a , divergence_curve_discretization_size=__a , mauve_scaling_factor=__a , verbose=__a , seed=__a , )
return out
| 306
|
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, 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__ ( A__ ):
def __init__( self : Tuple , *__a : Tuple , __a : Dict=None , __a : List[str]=None , **__a : Dict ):
'''simple docstring'''
super().__init__(*__a , **__a )
lowerCamelCase__: str = eval_examples
lowerCamelCase__: Optional[int] = post_process_function
def lowerCamelCase_ ( self : str , __a : Optional[Dataset] = None , __a : List[Any]=None , __a : Optional[List[str]] = None , __a : str = "eval" , **__a : Tuple , ):
'''simple docstring'''
lowerCamelCase__: Tuple = gen_kwargs.copy()
lowerCamelCase__: Union[str, Any] = (
gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length""" ) is not None else self.args.generation_max_length
)
lowerCamelCase__: Tuple = (
gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams""" ) is not None else self.args.generation_num_beams
)
lowerCamelCase__: Optional[Any] = gen_kwargs
lowerCamelCase__: List[Any] = self.eval_dataset if eval_dataset is None else eval_dataset
lowerCamelCase__: Union[str, Any] = self.get_eval_dataloader(__a )
lowerCamelCase__: Union[str, Any] = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
lowerCamelCase__: Optional[int] = self.compute_metrics
lowerCamelCase__: Union[str, Any] = None
lowerCamelCase__: Dict = time.time()
lowerCamelCase__: Optional[int] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowerCamelCase__: Any = eval_loop(
__a , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__a , metric_key_prefix=__a , )
finally:
lowerCamelCase__: Any = compute_metrics
lowerCamelCase__: int = 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(
__a , __a , 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
lowerCamelCase__: Tuple = self.post_process_function(__a , __a , __a )
lowerCamelCase__: List[Any] = self.compute_metrics(__a )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
lowerCamelCase__: Dict = metrics.pop(__a )
metrics.update(output.metrics )
else:
lowerCamelCase__: int = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(__a )
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() )
lowerCamelCase__: List[Any] = self.callback_handler.on_evaluate(self.args , self.state , self.control , __a )
return metrics
def lowerCamelCase_ ( self : str , __a : List[str] , __a : List[Any] , __a : Tuple=None , __a : str = "test" , **__a : Optional[int] ):
'''simple docstring'''
lowerCamelCase__: List[Any] = gen_kwargs.copy()
lowerCamelCase__: Optional[Any] = self.get_test_dataloader(__a )
# Temporarily disable metric computation, we will do it in the loop here.
lowerCamelCase__: Any = self.compute_metrics
lowerCamelCase__: Optional[int] = None
lowerCamelCase__: int = time.time()
lowerCamelCase__: Tuple = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowerCamelCase__: List[str] = eval_loop(
__a , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__a , metric_key_prefix=__a , )
finally:
lowerCamelCase__: Any = compute_metrics
lowerCamelCase__: Optional[int] = 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(
__a , __a , 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
lowerCamelCase__: str = self.post_process_function(__a , __a , __a , """predict""" )
lowerCamelCase__: str = self.compute_metrics(__a )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
lowerCamelCase__: Dict = metrics.pop(__a )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__a )
| 306
| 1
|
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class __snake_case ( SCREAMING_SNAKE_CASE):
'''simple docstring'''
UpperCamelCase__ : int = 0
UpperCamelCase__ : bool = False
UpperCamelCase__ : float = 3.0
class __snake_case ( unittest.TestCase):
'''simple docstring'''
def _a ( self ):
# If no defaults are changed, `to_kwargs` returns an empty dict.
self.assertDictEqual(MockClass().to_kwargs() , {} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() , {"""a""": 2} )
self.assertDictEqual(MockClass(a=2 , b=a_ ).to_kwargs() , {"""a""": 2, """b""": True} )
self.assertDictEqual(MockClass(a=2 , c=2.25 ).to_kwargs() , {"""a""": 2, """c""": 2.25} )
@require_cuda
def _a ( self ):
# If no defaults are changed, `to_kwargs` returns an empty dict.
a__ = GradScalerKwargs(init_scale=1_024 , growth_factor=2 )
AcceleratorState._reset_state()
a__ = Accelerator(mixed_precision="""fp16""" , kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
a__ = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale , 1_024.0 )
self.assertEqual(scaler._growth_factor , 2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor , 0.5 )
self.assertEqual(scaler._growth_interval , 2_000 )
self.assertEqual(scaler._enabled , a_ )
@require_multi_gpu
def _a ( self ):
a__ = ["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
execute_subprocess_async(a_ , env=os.environ.copy() )
if __name__ == "__main__":
UpperCAmelCase = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
UpperCAmelCase = Accelerator(kwargs_handlers=[ddp_scaler])
UpperCAmelCase = torch.nn.Linear(100, 200)
UpperCAmelCase = accelerator.prepare(model)
# Check the values changed in kwargs
UpperCAmelCase = """"""
UpperCAmelCase = model.bucket_bytes_cap // (1_024 * 1_024)
if observed_bucket_cap_map != 15:
error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# 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)
| 706
|
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
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"""vocab_file""": """spiece.model"""}
UpperCAmelCase = {
"""vocab_file""": {
"""bert_for_seq_generation""": (
"""https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
),
}
}
UpperCAmelCase = {"""bert_for_seq_generation""": 512}
class __snake_case ( SCREAMING_SNAKE_CASE):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = VOCAB_FILES_NAMES
UpperCamelCase__ : Any = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ : List[int] = []
UpperCamelCase__ : Optional[int] = ["""input_ids""", """attention_mask"""]
def __init__( self , a_ , a_="<s>" , a_="</s>" , a_="<unk>" , a_="<pad>" , a_="<::::>" , a_ = None , **a_ , ):
a__ = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=a_ , eos_token=a_ , unk_token=a_ , pad_token=a_ , sep_token=a_ , sp_model_kwargs=self.sp_model_kwargs , **a_ , )
a__ = vocab_file
a__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(a_ )
@property
def _a ( self ):
return self.sp_model.get_piece_size()
def _a ( self ):
a__ = {self.convert_ids_to_tokens(a_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
a__ = self.__dict__.copy()
a__ = None
return state
def __setstate__( self , a_ ):
a__ = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
a__ = {}
a__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self , a_ ):
return self.sp_model.encode(a_ , out_type=a_ )
def _a ( self , a_ ):
return self.sp_model.piece_to_id(a_ )
def _a ( self , a_ ):
a__ = self.sp_model.IdToPiece(a_ )
return token
def _a ( self , a_ ):
a__ = []
a__ = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(a_ ) + token
a__ = []
else:
current_sub_tokens.append(a_ )
out_string += self.sp_model.decode(a_ )
return out_string.strip()
def _a ( self , a_ , a_ = None ):
if not os.path.isdir(a_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
a__ = os.path.join(
a_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , a_ )
elif not os.path.isfile(self.vocab_file ):
with open(a_ , """wb""" ) as fi:
a__ = self.sp_model.serialized_model_proto()
fi.write(a_ )
return (out_vocab_file,)
| 351
| 0
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_snake_case : Union[str, Any] = 16
_snake_case : Optional[Any] = 32
def a_ ( lowerCAmelCase_ : Accelerator, lowerCAmelCase_ : int = 16 ):
__lowerCAmelCase = AutoTokenizer.from_pretrained('bert-base-cased' )
__lowerCAmelCase = load_dataset('glue', 'mrpc' )
def tokenize_function(lowerCAmelCase_ : Any ):
# max_length=None => use the model max length (it's actually the default)
__lowerCAmelCase = 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():
__lowerCAmelCase = 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
__lowerCAmelCase = tokenized_datasets.rename_column('label', 'labels' )
def collate_fn(lowerCAmelCase_ : str ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowerCAmelCase = 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":
__lowerCAmelCase = 16
elif accelerator.mixed_precision != "no":
__lowerCAmelCase = 8
else:
__lowerCAmelCase = None
return tokenizer.pad(
lowerCAmelCase_, padding='longest', max_length=lowerCAmelCase_, pad_to_multiple_of=lowerCAmelCase_, return_tensors='pt', )
# Instantiate dataloaders.
__lowerCAmelCase = DataLoader(
tokenized_datasets['train'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
__lowerCAmelCase = DataLoader(
tokenized_datasets['validation'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_snake_case : Union[str, Any] = mocked_dataloaders # noqa: F811
def a_ ( lowerCAmelCase_ : List[str], lowerCAmelCase_ : Dict ):
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', lowerCAmelCase_ ) == "1":
__lowerCAmelCase = 2
# Initialize accelerator
__lowerCAmelCase = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision )
# 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' )
# If the batch size is too big we use gradient accumulation
__lowerCAmelCase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
__lowerCAmelCase = batch_size // MAX_GPU_BATCH_SIZE
__lowerCAmelCase = MAX_GPU_BATCH_SIZE
set_seed(lowerCAmelCase_ )
__lowerCAmelCase , __lowerCAmelCase = get_dataloaders(lowerCAmelCase_, lowerCAmelCase_ )
# 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=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).
__lowerCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
__lowerCAmelCase = AdamW(params=model.parameters(), lr=lowerCAmelCase_ )
# Instantiate scheduler
__lowerCAmelCase = 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.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 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 )
__lowerCAmelCase = model(**lowerCAmelCase_ )
__lowerCAmelCase = outputs.loss
__lowerCAmelCase = loss / gradient_accumulation_steps
accelerator.backward(lowerCAmelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
__lowerCAmelCase = 0
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():
__lowerCAmelCase = model(**lowerCAmelCase_ )
__lowerCAmelCase = outputs.logits.argmax(dim=-1 )
__lowerCAmelCase , __lowerCAmelCase = accelerator.gather((predictions, batch['labels']) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(lowerCAmelCase_ ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
__lowerCAmelCase = predictions[: len(eval_dataloader.dataset ) - samples_seen]
__lowerCAmelCase = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=lowerCAmelCase_, references=lowerCAmelCase_, )
__lowerCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""", lowerCAmelCase_ )
def a_ ( ):
__lowerCAmelCase = 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.' )
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowerCAmelCase_, lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 53
|
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
_snake_case : List[Any] = True
from torch.cuda.amp import autocast
_snake_case : Dict = logging.getLogger(__name__)
def a_ ( lowerCAmelCase_ : str=None, lowerCAmelCase_ : str=None ):
return field(default_factory=lambda: default, metadata=lowerCAmelCase_ )
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
a_ = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
a_ = field(
default=_UpperCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
a_ = field(
default=_UpperCamelCase , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""} )
a_ = field(
default=0.1 , metadata={"""help""": """The dropout ratio for the attention probabilities."""} )
a_ = field(
default=0.1 , metadata={"""help""": """The dropout ratio for activations inside the fully connected layer."""} )
a_ = field(
default=0.1 , metadata={
"""help""": """The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."""
} , )
a_ = field(
default=0.1 , metadata={"""help""": """The dropout probabilitiy for all 1D convolutional layers in feature extractor."""} , )
a_ = field(
default=0.05 , metadata={
"""help""": (
"""Propability of each feature vector along the time axis to be chosen as the start of the vector"""
"""span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"""
"""vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."""
)
} , )
a_ = field(default=0.0 , metadata={"""help""": """The LayerDrop probability."""} )
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
a_ = field(
default=_UpperCamelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
a_ = field(
default="""train+validation""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
a_ = field(
default=_UpperCamelCase , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
a_ = field(
default=_UpperCamelCase , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
a_ = field(
default=_UpperCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
a_ = field(
default=_UpperCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of validation examples to this """
"""value if set."""
)
} , )
a_ = list_field(
default=[""",""", """?""", """.""", """!""", """-""", """;""", """:""", """\"\"""", """%""", """'""", """\"""", """�"""] , metadata={"""help""": """A list of characters to remove from the transcripts."""} , )
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
a_ = 42
a_ = True
a_ = None
a_ = None
a_ = None
a_ = None
def __call__( self : int , lowerCAmelCase_ : List[Dict[str, Union[List[int], torch.Tensor]]] ) -> Dict[str, torch.Tensor]:
# split inputs and labels since they have to be of different lenghts and need
# different padding methods
__lowerCAmelCase = [{'input_values': feature['input_values']} for feature in features]
__lowerCAmelCase = [{'input_ids': feature['labels']} for feature in features]
__lowerCAmelCase = self.processor.pad(
lowerCAmelCase_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='pt' , )
__lowerCAmelCase = self.processor.pad(
labels=lowerCAmelCase_ , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='pt' , )
# replace padding with -100 to ignore loss correctly
__lowerCAmelCase = labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1 ) , -1_0_0 )
__lowerCAmelCase = labels
return batch
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
def lowercase ( self : Tuple , lowerCAmelCase_ : nn.Module , lowerCAmelCase_ : Dict[str, Union[torch.Tensor, Any]] ) -> torch.Tensor:
model.train()
__lowerCAmelCase = self._prepare_inputs(lowerCAmelCase_ )
if self.use_amp:
with autocast():
__lowerCAmelCase = self.compute_loss(lowerCAmelCase_ , lowerCAmelCase_ )
else:
__lowerCAmelCase = self.compute_loss(lowerCAmelCase_ , lowerCAmelCase_ )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
__lowerCAmelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
__lowerCAmelCase = loss.sum() / (inputs['labels'] >= 0).sum()
else:
raise ValueError(f"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']""" )
if self.args.gradient_accumulation_steps > 1:
__lowerCAmelCase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(lowerCAmelCase_ ).backward()
elif self.use_apex:
with amp.scale_loss(lowerCAmelCase_ , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(lowerCAmelCase_ )
else:
loss.backward()
return loss.detach()
def a_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
__lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', handlers=[logging.StreamHandler(sys.stdout )], )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('Training/evaluation parameters %s', lowerCAmelCase_ )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
__lowerCAmelCase = datasets.load_dataset(
'common_voice', data_args.dataset_config_name, split=data_args.train_split_name )
__lowerCAmelCase = datasets.load_dataset('common_voice', data_args.dataset_config_name, split='test' )
# Create and save tokenizer
__lowerCAmelCase = F"""[{"".join(data_args.chars_to_ignore )}]"""
def remove_special_characters(lowerCAmelCase_ : Any ):
__lowerCAmelCase = re.sub(lowerCAmelCase_, '', batch['sentence'] ).lower() + ' '
return batch
__lowerCAmelCase = train_dataset.map(lowerCAmelCase_, remove_columns=['sentence'] )
__lowerCAmelCase = eval_dataset.map(lowerCAmelCase_, remove_columns=['sentence'] )
def extract_all_chars(lowerCAmelCase_ : Tuple ):
__lowerCAmelCase = ' '.join(batch['text'] )
__lowerCAmelCase = list(set(lowerCAmelCase_ ) )
return {"vocab": [vocab], "all_text": [all_text]}
__lowerCAmelCase = train_dataset.map(
lowerCAmelCase_, batched=lowerCAmelCase_, batch_size=-1, keep_in_memory=lowerCAmelCase_, remove_columns=train_dataset.column_names, )
__lowerCAmelCase = train_dataset.map(
lowerCAmelCase_, batched=lowerCAmelCase_, batch_size=-1, keep_in_memory=lowerCAmelCase_, remove_columns=eval_dataset.column_names, )
__lowerCAmelCase = list(set(vocab_train['vocab'][0] ) | set(vocab_test['vocab'][0] ) )
__lowerCAmelCase = {v: k for k, v in enumerate(lowerCAmelCase_ )}
__lowerCAmelCase = vocab_dict[' ']
del vocab_dict[" "]
__lowerCAmelCase = len(lowerCAmelCase_ )
__lowerCAmelCase = len(lowerCAmelCase_ )
with open('vocab.json', 'w' ) as vocab_file:
json.dump(lowerCAmelCase_, lowerCAmelCase_ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCAmelCase = WavaVecaCTCTokenizer(
'vocab.json', unk_token='[UNK]', pad_token='[PAD]', word_delimiter_token='|', )
__lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1, sampling_rate=1_6000, padding_value=0.0, do_normalize=lowerCAmelCase_, return_attention_mask=lowerCAmelCase_ )
__lowerCAmelCase = WavaVecaProcessor(feature_extractor=lowerCAmelCase_, tokenizer=lowerCAmelCase_ )
__lowerCAmelCase = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, activation_dropout=model_args.activation_dropout, attention_dropout=model_args.attention_dropout, hidden_dropout=model_args.hidden_dropout, feat_proj_dropout=model_args.feat_proj_dropout, mask_time_prob=model_args.mask_time_prob, gradient_checkpointing=training_args.gradient_checkpointing, layerdrop=model_args.layerdrop, ctc_loss_reduction='mean', pad_token_id=processor.tokenizer.pad_token_id, vocab_size=len(processor.tokenizer ), )
if data_args.max_train_samples is not None:
__lowerCAmelCase = min(len(lowerCAmelCase_ ), data_args.max_train_samples )
__lowerCAmelCase = train_dataset.select(range(lowerCAmelCase_ ) )
if data_args.max_val_samples is not None:
__lowerCAmelCase = eval_dataset.select(range(data_args.max_val_samples ) )
__lowerCAmelCase = torchaudio.transforms.Resample(4_8000, 1_6000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(lowerCAmelCase_ : int ):
__lowerCAmelCase , __lowerCAmelCase = torchaudio.load(batch['path'] )
__lowerCAmelCase = resampler(lowerCAmelCase_ ).squeeze().numpy()
__lowerCAmelCase = 1_6000
__lowerCAmelCase = batch['text']
return batch
__lowerCAmelCase = train_dataset.map(
lowerCAmelCase_, remove_columns=train_dataset.column_names, num_proc=data_args.preprocessing_num_workers, )
__lowerCAmelCase = eval_dataset.map(
lowerCAmelCase_, remove_columns=eval_dataset.column_names, num_proc=data_args.preprocessing_num_workers, )
def prepare_dataset(lowerCAmelCase_ : Union[str, Any] ):
# check that all files have the correct sampling rate
assert (
len(set(batch['sampling_rate'] ) ) == 1
), F"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
__lowerCAmelCase = processor(
audio=batch['speech'], text=batch['target_text'], sampling_rate=batch['sampling_rate'][0] )
batch.update(lowerCAmelCase_ )
return batch
__lowerCAmelCase = train_dataset.map(
lowerCAmelCase_, remove_columns=train_dataset.column_names, batch_size=training_args.per_device_train_batch_size, batched=lowerCAmelCase_, num_proc=data_args.preprocessing_num_workers, )
__lowerCAmelCase = eval_dataset.map(
lowerCAmelCase_, remove_columns=eval_dataset.column_names, batch_size=training_args.per_device_train_batch_size, batched=lowerCAmelCase_, num_proc=data_args.preprocessing_num_workers, )
# Metric
__lowerCAmelCase = datasets.load_metric('wer' )
def compute_metrics(lowerCAmelCase_ : Optional[Any] ):
__lowerCAmelCase = pred.predictions
__lowerCAmelCase = np.argmax(lowerCAmelCase_, axis=-1 )
__lowerCAmelCase = processor.tokenizer.pad_token_id
__lowerCAmelCase = processor.batch_decode(lowerCAmelCase_ )
# we do not want to group tokens when computing the metrics
__lowerCAmelCase = processor.batch_decode(pred.label_ids, group_tokens=lowerCAmelCase_ )
__lowerCAmelCase = wer_metric.compute(predictions=lowerCAmelCase_, references=lowerCAmelCase_ )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
__lowerCAmelCase = DataCollatorCTCWithPadding(processor=lowerCAmelCase_, padding=lowerCAmelCase_ )
# Initialize our Trainer
__lowerCAmelCase = CTCTrainer(
model=lowerCAmelCase_, data_collator=lowerCAmelCase_, args=lowerCAmelCase_, compute_metrics=lowerCAmelCase_, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=processor.feature_extractor, )
# Training
if training_args.do_train:
if last_checkpoint is not None:
__lowerCAmelCase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
__lowerCAmelCase = model_args.model_name_or_path
else:
__lowerCAmelCase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
__lowerCAmelCase = trainer.train(resume_from_checkpoint=lowerCAmelCase_ )
trainer.save_model()
__lowerCAmelCase = train_result.metrics
__lowerCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase_ )
)
__lowerCAmelCase = min(lowerCAmelCase_, len(lowerCAmelCase_ ) )
trainer.log_metrics('train', lowerCAmelCase_ )
trainer.save_metrics('train', lowerCAmelCase_ )
trainer.save_state()
# Evaluation
__lowerCAmelCase = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
__lowerCAmelCase = trainer.evaluate()
__lowerCAmelCase = data_args.max_val_samples if data_args.max_val_samples is not None else len(lowerCAmelCase_ )
__lowerCAmelCase = min(lowerCAmelCase_, len(lowerCAmelCase_ ) )
trainer.log_metrics('eval', lowerCAmelCase_ )
trainer.save_metrics('eval', lowerCAmelCase_ )
return results
if __name__ == "__main__":
main()
| 53
| 1
|
from functools import reduce
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def A ( _UpperCAmelCase : str = N ) -> int:
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _UpperCAmelCase , _UpperCAmelCase : str(int(_UpperCAmelCase ) * int(_UpperCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(_UpperCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 639
|
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __lowerCAmelCase :
@staticmethod
def _lowerCamelCase ( *A : Union[str, Any] , **A : List[Any]) -> Union[str, Any]:
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@require_torch
def _lowerCamelCase ( self : List[str]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = pipeline(
model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification' , )
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
_UpperCAmelCase = image_classifier(A , candidate_labels=['a', 'b', 'c'])
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(A) , [
[{'score': 0.3_3_3, 'label': 'a'}, {'score': 0.3_3_3, 'label': 'b'}, {'score': 0.3_3_3, 'label': 'c'}],
[{'score': 0.3_3_3, 'label': 'a'}, {'score': 0.3_3_3, 'label': 'c'}, {'score': 0.3_3_3, 'label': 'b'}],
] , )
_UpperCAmelCase = image_classifier([image] * 5 , candidate_labels=['A', 'B', 'C'] , batch_size=2)
self.assertEqual(
nested_simplify(A) , [
[
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
],
[
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
],
[
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
],
[
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
],
[
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
],
] , )
@require_tf
def _lowerCamelCase ( self : str) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = pipeline(
model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification' , framework='tf')
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
_UpperCAmelCase = image_classifier(A , candidate_labels=['a', 'b', 'c'])
self.assertEqual(
nested_simplify(A) , [{'score': 0.3_3_3, 'label': 'a'}, {'score': 0.3_3_3, 'label': 'b'}, {'score': 0.3_3_3, 'label': 'c'}] , )
_UpperCAmelCase = image_classifier([image] * 5 , candidate_labels=['A', 'B', 'C'] , batch_size=2)
self.assertEqual(
nested_simplify(A) , [
[
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
],
[
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
],
[
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
],
[
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
],
[
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
{'score': 0.3_3_3, 'label': ANY(A)},
],
] , )
@slow
@require_torch
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = pipeline(
task='zero-shot-image-classification' , model='openai/clip-vit-base-patch32' , )
# This is an image of 2 cats with remotes and no planes
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
_UpperCAmelCase = image_classifier(A , candidate_labels=['cat', 'plane', 'remote'])
self.assertEqual(
nested_simplify(A) , [
{'score': 0.5_1_1, 'label': 'remote'},
{'score': 0.4_8_5, 'label': 'cat'},
{'score': 0.0_0_4, 'label': 'plane'},
] , )
_UpperCAmelCase = image_classifier([image] * 5 , candidate_labels=['cat', 'plane', 'remote'] , batch_size=2)
self.assertEqual(
nested_simplify(A) , [
[
{'score': 0.5_1_1, 'label': 'remote'},
{'score': 0.4_8_5, 'label': 'cat'},
{'score': 0.0_0_4, 'label': 'plane'},
],
]
* 5 , )
@slow
@require_tf
def _lowerCamelCase ( self : List[str]) -> Any:
"""simple docstring"""
_UpperCAmelCase = pipeline(
task='zero-shot-image-classification' , model='openai/clip-vit-base-patch32' , framework='tf')
# This is an image of 2 cats with remotes and no planes
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
_UpperCAmelCase = image_classifier(A , candidate_labels=['cat', 'plane', 'remote'])
self.assertEqual(
nested_simplify(A) , [
{'score': 0.5_1_1, 'label': 'remote'},
{'score': 0.4_8_5, 'label': 'cat'},
{'score': 0.0_0_4, 'label': 'plane'},
] , )
_UpperCAmelCase = image_classifier([image] * 5 , candidate_labels=['cat', 'plane', 'remote'] , batch_size=2)
self.assertEqual(
nested_simplify(A) , [
[
{'score': 0.5_1_1, 'label': 'remote'},
{'score': 0.4_8_5, 'label': 'cat'},
{'score': 0.0_0_4, 'label': 'plane'},
],
]
* 5 , )
| 639
| 1
|
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _UpperCamelCase (_lowerCamelCase : Union[dict, list, tuple, torch.Tensor] )-> List[Tuple[int, ...]]:
'''simple docstring'''
__snake_case = []
if isinstance(_lowerCamelCase , _lowerCamelCase ):
for v in tree.values():
shapes.extend(_fetch_dims(_lowerCamelCase ) )
elif isinstance(_lowerCamelCase , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(_lowerCamelCase ) )
elif isinstance(_lowerCamelCase , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError('''Not supported''' )
return shapes
@torch.jit.ignore
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : Tuple[int, ...] )-> Tuple[int, ...]:
'''simple docstring'''
__snake_case = []
for d in reversed(_lowerCamelCase ):
idx.append(flat_idx % d )
__snake_case = flat_idx // d
return tuple(reversed(_lowerCamelCase ) )
@torch.jit.ignore
def _UpperCamelCase (_lowerCamelCase : Sequence[int] , _lowerCamelCase : Sequence[int] , _lowerCamelCase : Sequence[int] , _lowerCamelCase : Optional[Sequence[bool]] = None , _lowerCamelCase : Optional[Sequence[bool]] = None , )-> List[Tuple[slice, ...]]:
'''simple docstring'''
def reduce_edge_list(_lowerCamelCase : List[bool] ) -> None:
__snake_case = True
for i in range(len(_lowerCamelCase ) ):
__snake_case = -1 * (i + 1)
l[reversed_idx] &= tally
__snake_case = l[reversed_idx]
if start_edges is None:
__snake_case = [s == 0 for s in start]
reduce_edge_list(_lowerCamelCase )
if end_edges is None:
__snake_case = [e == (d - 1) for e, d in zip(_lowerCamelCase , _lowerCamelCase )]
reduce_edge_list(_lowerCamelCase )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(_lowerCamelCase ) == 0:
return [()]
elif len(_lowerCamelCase ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
__snake_case = []
__snake_case = []
# Dimensions common to start and end can be selected directly
for s, e in zip(_lowerCamelCase , _lowerCamelCase ):
if s == e:
path_list.append(slice(_lowerCamelCase , s + 1 ) )
else:
break
__snake_case = tuple(_lowerCamelCase )
__snake_case = len(_lowerCamelCase )
# start == end, and we're done
if divergence_idx == len(_lowerCamelCase ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
__snake_case = start[divergence_idx]
return tuple(
path + (slice(_lowerCamelCase , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
__snake_case = end[divergence_idx]
return tuple(
path + (slice(_lowerCamelCase , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
__snake_case = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def _UpperCamelCase (_lowerCamelCase : torch.Tensor , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int )-> torch.Tensor:
'''simple docstring'''
__snake_case = t.shape[:no_batch_dims]
__snake_case = list(_flat_idx_to_idx(_lowerCamelCase , _lowerCamelCase ) )
# _get_minimal_slice_set is inclusive
__snake_case = list(_flat_idx_to_idx(flat_end - 1 , _lowerCamelCase ) )
# Get an ordered list of slices to perform
__snake_case = _get_minimal_slice_set(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , )
__snake_case = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def _UpperCamelCase (_lowerCamelCase : Callable , _lowerCamelCase : Dict[str, Any] , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : bool = False , _lowerCamelCase : Any = None , _lowerCamelCase : bool = False , )-> Any:
'''simple docstring'''
if not (len(_lowerCamelCase ) > 0):
raise ValueError('''Must provide at least one input''' )
__snake_case = [shape[:no_batch_dims] for shape in _fetch_dims(_lowerCamelCase )]
__snake_case = tuple([max(_lowerCamelCase ) for s in zip(*_lowerCamelCase )] )
def _prep_inputs(_lowerCamelCase : torch.Tensor ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
__snake_case = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
__snake_case = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
__snake_case = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
__snake_case = tensor_tree_map(_prep_inputs , _lowerCamelCase )
__snake_case = None
if _out is not None:
__snake_case = tensor_tree_map(lambda _lowerCamelCase : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
__snake_case = 1
for d in orig_batch_dims:
flat_batch_dim *= d
__snake_case = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(_lowerCamelCase : torch.Tensor ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
__snake_case = 0
__snake_case = prepped_outputs
for _ in range(_lowerCamelCase ):
# Chunk the input
if not low_mem:
__snake_case = _select_chunk
else:
__snake_case = partial(
_chunk_slice , flat_start=_lowerCamelCase , flat_end=min(_lowerCamelCase , i + chunk_size ) , no_batch_dims=len(_lowerCamelCase ) , )
__snake_case = tensor_tree_map(_lowerCamelCase , _lowerCamelCase )
# Run the layer on the chunk
__snake_case = layer(**_lowerCamelCase )
# Allocate space for the output
if out is None:
__snake_case = tensor_tree_map(lambda _lowerCamelCase : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , _lowerCamelCase )
# Put the chunk in its pre-allocated space
if isinstance(_lowerCamelCase , _lowerCamelCase ):
def assign(_lowerCamelCase : dict , _lowerCamelCase : dict ) -> None:
for k, v in da.items():
if isinstance(_lowerCamelCase , _lowerCamelCase ):
assign(_lowerCamelCase , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
__snake_case = da[k]
assign(_lowerCamelCase , _lowerCamelCase )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
for xa, xa in zip(_lowerCamelCase , _lowerCamelCase ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
__snake_case = xa
elif isinstance(_lowerCamelCase , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
__snake_case = output_chunk
else:
raise ValueError('''Not supported''' )
i += chunk_size
__snake_case = tensor_tree_map(lambda _lowerCamelCase : t.view(orig_batch_dims + t.shape[1:] ) , _lowerCamelCase )
return out
class lowerCAmelCase :
def __init__( self , __SCREAMING_SNAKE_CASE = 512 , ) -> List[Any]:
'''simple docstring'''
__snake_case = max_chunk_size
__snake_case = None
__snake_case = None
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
logging.info('''Tuning chunk size...''' )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
__snake_case = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
__snake_case = [c for c in candidates if c > min_chunk_size]
__snake_case = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(__SCREAMING_SNAKE_CASE ) -> bool:
try:
with torch.no_grad():
fn(*__SCREAMING_SNAKE_CASE , chunk_size=__SCREAMING_SNAKE_CASE )
return True
except RuntimeError:
return False
__snake_case = 0
__snake_case = len(__SCREAMING_SNAKE_CASE ) - 1
while i > min_viable_chunk_size_index:
__snake_case = test_chunk_size(candidates[i] )
if not viable:
__snake_case = (min_viable_chunk_size_index + i) // 2
else:
__snake_case = i
__snake_case = (i + len(__SCREAMING_SNAKE_CASE ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
__snake_case = True
for aa, aa in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
assert type(__SCREAMING_SNAKE_CASE ) == type(__SCREAMING_SNAKE_CASE )
if isinstance(__SCREAMING_SNAKE_CASE , (list, tuple) ):
consistent &= self._compare_arg_caches(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__snake_case = [v for _, v in sorted(aa.items() , key=lambda __SCREAMING_SNAKE_CASE : x[0] )]
__snake_case = [v for _, v in sorted(aa.items() , key=lambda __SCREAMING_SNAKE_CASE : x[0] )]
consistent &= self._compare_arg_caches(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
else:
consistent &= aa == aa
return consistent
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , ) -> int:
'''simple docstring'''
__snake_case = True
__snake_case = tree_map(lambda __SCREAMING_SNAKE_CASE : a.shape if isinstance(__SCREAMING_SNAKE_CASE , torch.Tensor ) else a , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(__SCREAMING_SNAKE_CASE )
__snake_case = self._compare_arg_caches(self.cached_arg_data , __SCREAMING_SNAKE_CASE )
else:
# Otherwise, we can reuse the precomputed value
__snake_case = False
if not consistent:
__snake_case = self._determine_favorable_chunk_size(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )
__snake_case = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 24
|
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class lowerCamelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase=None , lowerCAmelCase=None ):
if not conversation_id:
UpperCAmelCase_ = uuid.uuida()
if past_user_inputs is None:
UpperCAmelCase_ = []
if generated_responses is None:
UpperCAmelCase_ = []
UpperCAmelCase_ = conversation_id
UpperCAmelCase_ = past_user_inputs
UpperCAmelCase_ = generated_responses
UpperCAmelCase_ = text
def __eq__( self , lowerCAmelCase ):
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def A__ ( self , lowerCAmelCase , lowerCAmelCase = False ):
if self.new_user_input:
if overwrite:
logger.warning(
f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
f'''with: "{text}".''' )
UpperCAmelCase_ = text
else:
logger.warning(
f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input''' )
else:
UpperCAmelCase_ = text
def A__ ( self ):
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
UpperCAmelCase_ = None
def A__ ( self , lowerCAmelCase ):
self.generated_responses.append(lowerCAmelCase )
def A__ ( self ):
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
UpperCAmelCase_ = f'''Conversation id: {self.uuid} \n'''
for is_user, text in self.iter_texts():
UpperCAmelCase_ = "user" if is_user else "bot"
output += f'''{name} >> {text} \n'''
return output
@add_end_docstrings(
lowercase__, r'\n min_length_for_response (`int`, *optional*, defaults to 32):\n The minimum length (in number of tokens) for a response.\n minimum_tokens (`int`, *optional*, defaults to 10):\n The minimum length of tokens to leave for a response.\n ', )
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self , *lowerCAmelCase , **lowerCAmelCase ):
super().__init__(*lowerCAmelCase , **lowerCAmelCase )
if self.tokenizer.pad_token_id is None:
UpperCAmelCase_ = self.tokenizer.eos_token
def A__ ( self , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None , **lowerCAmelCase ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = {}
UpperCAmelCase_ = {}
if min_length_for_response is not None:
UpperCAmelCase_ = min_length_for_response
if minimum_tokens is not None:
UpperCAmelCase_ = minimum_tokens
if "max_length" in generate_kwargs:
UpperCAmelCase_ = generate_kwargs["max_length"]
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
UpperCAmelCase_ = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(lowerCAmelCase )
return preprocess_params, forward_params, postprocess_params
def __call__( self , lowerCAmelCase , lowerCAmelCase=0 , **lowerCAmelCase ):
UpperCAmelCase_ = super().__call__(lowerCAmelCase , num_workers=lowerCAmelCase , **lowerCAmelCase )
if isinstance(lowerCAmelCase , lowerCAmelCase ) and len(lowerCAmelCase ) == 1:
return outputs[0]
return outputs
def A__ ( self , lowerCAmelCase , lowerCAmelCase=32 ):
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
raise ValueError("ConversationalPipeline, expects Conversation as inputs" )
if conversation.new_user_input is None:
raise ValueError(
f'''Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '''
"Add user inputs with the conversation's `add_user_input` method" )
if hasattr(self.tokenizer , "_build_conversation_input_ids" ):
UpperCAmelCase_ = self.tokenizer._build_conversation_input_ids(lowerCAmelCase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
UpperCAmelCase_ = self._legacy_parse_and_tokenize(lowerCAmelCase )
if self.framework == "pt":
UpperCAmelCase_ = torch.LongTensor([input_ids] )
elif self.framework == "tf":
UpperCAmelCase_ = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def A__ ( self , lowerCAmelCase , lowerCAmelCase=10 , **lowerCAmelCase ):
UpperCAmelCase_ = generate_kwargs.get("max_length" , self.model.config.max_length )
UpperCAmelCase_ = model_inputs["input_ids"].shape[1]
if max_length - minimum_tokens < n:
logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})''' )
UpperCAmelCase_ = max_length - minimum_tokens
UpperCAmelCase_ = model_inputs["input_ids"][:, -trim:]
if "attention_mask" in model_inputs:
UpperCAmelCase_ = model_inputs["attention_mask"][:, -trim:]
UpperCAmelCase_ = model_inputs.pop("conversation" )
UpperCAmelCase_ = max_length
UpperCAmelCase_ = self.model.generate(**lowerCAmelCase , **lowerCAmelCase )
if self.model.config.is_encoder_decoder:
UpperCAmelCase_ = 1
else:
UpperCAmelCase_ = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def A__ ( self , lowerCAmelCase , lowerCAmelCase=True ):
UpperCAmelCase_ = model_outputs["output_ids"]
UpperCAmelCase_ = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=lowerCAmelCase , clean_up_tokenization_spaces=lowerCAmelCase , )
UpperCAmelCase_ = model_outputs["conversation"]
conversation.mark_processed()
conversation.append_response(lowerCAmelCase )
return conversation
def A__ ( self , lowerCAmelCase ):
UpperCAmelCase_ = self.tokenizer.eos_token_id
UpperCAmelCase_ = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
if len(lowerCAmelCase ) > self.tokenizer.model_max_length:
UpperCAmelCase_ = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 579
| 0
|
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__SCREAMING_SNAKE_CASE : Dict =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Any ={
'''openai/imagegpt-small''': '''''',
'''openai/imagegpt-medium''': '''''',
'''openai/imagegpt-large''': '''''',
}
class A_ ( __a ):
_A :Union[str, Any] = '''imagegpt'''
_A :List[Any] = ['''past_key_values''']
_A :Optional[int] = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Dict , snake_case__ : Optional[Any]=5_12 + 1 , snake_case__ : Dict=32 * 32 , snake_case__ : Union[str, Any]=5_12 , snake_case__ : Dict=24 , snake_case__ : int=8 , snake_case__ : Dict=None , snake_case__ : List[Any]="quick_gelu" , snake_case__ : Optional[Any]=0.1 , snake_case__ : Optional[int]=0.1 , snake_case__ : List[Any]=0.1 , snake_case__ : List[Any]=1E-5 , snake_case__ : int=0.02 , snake_case__ : str=True , snake_case__ : Union[str, Any]=True , snake_case__ : Any=False , snake_case__ : Union[str, Any]=False , snake_case__ : str=False , **snake_case__ : Optional[Any] , ):
lowercase = vocab_size
lowercase = n_positions
lowercase = n_embd
lowercase = n_layer
lowercase = n_head
lowercase = n_inner
lowercase = activation_function
lowercase = resid_pdrop
lowercase = embd_pdrop
lowercase = attn_pdrop
lowercase = layer_norm_epsilon
lowercase = initializer_range
lowercase = scale_attn_weights
lowercase = use_cache
lowercase = scale_attn_by_inverse_layer_idx
lowercase = reorder_and_upcast_attn
lowercase = tie_word_embeddings
super().__init__(tie_word_embeddings=snake_case__ , **snake_case__ )
class A_ ( __a ):
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
] )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , snake_case__ : "FeatureExtractionMixin" , snake_case__ : int = 1 , snake_case__ : int = -1 , snake_case__ : bool = False , snake_case__ : Optional["TensorType"] = None , snake_case__ : int = 3 , snake_case__ : int = 32 , snake_case__ : int = 32 , ):
lowercase = self._generate_dummy_images(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
lowercase = dict(preprocessor(images=snake_case__ , return_tensors=snake_case__ ) )
return inputs
| 72
|
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
__SCREAMING_SNAKE_CASE : Tuple =get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
__SCREAMING_SNAKE_CASE : Union[str, Any] =get_tests_dir('''fixtures/vocab.json''')
__SCREAMING_SNAKE_CASE : Union[str, Any] =get_tests_dir('''fixtures''')
class A_ ( unittest.TestCase ):
_A :List[str] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
lowercase = 0
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
lowercase = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" )
self.assertIsInstance(snake_case__ , snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase = WavaVecaConfig()
lowercase = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" )
# save in new folder
model_config.save_pretrained(snake_case__ )
processor.save_pretrained(snake_case__ )
lowercase = AutoProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(snake_case__ , os.path.join(snake_case__ , snake_case__ ) )
copyfile(snake_case__ , os.path.join(snake_case__ , """vocab.json""" ) )
lowercase = AutoProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : int ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase = WavaVecaFeatureExtractor()
lowercase = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" )
lowercase = WavaVecaProcessor(snake_case__ , snake_case__ )
# save in new folder
processor.save_pretrained(snake_case__ )
# drop `processor_class` in tokenizer
with open(os.path.join(snake_case__ , snake_case__ ) , """r""" ) as f:
lowercase = json.load(snake_case__ )
config_dict.pop("""processor_class""" )
with open(os.path.join(snake_case__ , snake_case__ ) , """w""" ) as f:
f.write(json.dumps(snake_case__ ) )
lowercase = AutoProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase = WavaVecaFeatureExtractor()
lowercase = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" )
lowercase = WavaVecaProcessor(snake_case__ , snake_case__ )
# save in new folder
processor.save_pretrained(snake_case__ )
# drop `processor_class` in feature extractor
with open(os.path.join(snake_case__ , snake_case__ ) , """r""" ) as f:
lowercase = json.load(snake_case__ )
config_dict.pop("""processor_class""" )
with open(os.path.join(snake_case__ , snake_case__ ) , """w""" ) as f:
f.write(json.dumps(snake_case__ ) )
lowercase = AutoProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : str ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase = WavaVecaConfig(processor_class="""Wav2Vec2Processor""" )
model_config.save_pretrained(snake_case__ )
# copy relevant files
copyfile(snake_case__ , os.path.join(snake_case__ , """vocab.json""" ) )
# create emtpy sample processor
with open(os.path.join(snake_case__ , snake_case__ ) , """w""" ) as f:
f.write("""{}""" )
lowercase = AutoProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(snake_case__ ):
lowercase = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(snake_case__ ):
lowercase = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=snake_case__ )
lowercase = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=snake_case__ )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
lowercase = processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
lowercase = processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizerFast""" )
# Test we can also load the slow version
lowercase = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=snake_case__ , use_fast=snake_case__ )
lowercase = new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , """NewTokenizer""" )
else:
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoFeatureExtractor.register(snake_case__ , snake_case__ )
AutoTokenizer.register(snake_case__ , slow_tokenizer_class=snake_case__ )
AutoProcessor.register(snake_case__ , snake_case__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(snake_case__ ):
AutoProcessor.register(snake_case__ , snake_case__ )
# Now that the config is registered, it can be used as any other config with the auto-API
lowercase = CustomFeatureExtractor.from_pretrained(snake_case__ )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase = os.path.join(snake_case__ , """vocab.txt""" )
with open(snake_case__ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowercase = CustomTokenizer(snake_case__ )
lowercase = CustomProcessor(snake_case__ , snake_case__ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(snake_case__ )
lowercase = AutoProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
class A_ ( __a ):
_A :List[str] = False
class A_ ( __a ):
_A :Dict = False
class A_ ( __a ):
_A :Union[str, Any] = '''AutoFeatureExtractor'''
_A :Tuple = '''AutoTokenizer'''
_A :Optional[Any] = False
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoFeatureExtractor.register(snake_case__ , snake_case__ )
AutoTokenizer.register(snake_case__ , slow_tokenizer_class=snake_case__ )
AutoProcessor.register(snake_case__ , snake_case__ )
# If remote code is not set, the default is to use local classes.
lowercase = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
lowercase = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
lowercase = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
lowercase = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
self.assertEqual(processor.__class__.__name__ , """BertTokenizerFast""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
lowercase = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext""" )
self.assertEqual(processor.__class__.__name__ , """ConvNextImageProcessor""" )
@is_staging_test
class A_ ( unittest.TestCase ):
_A :Optional[int] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Optional[int] ):
lowercase = TOKEN
HfFolder.save_token(snake_case__ )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Union[str, Any] ):
try:
delete_repo(token=cls._token , repo_id="""test-processor""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-processor-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""test-dynamic-processor""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
lowercase = WavaVecaProcessor.from_pretrained(snake_case__ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(snake_case__ , """test-processor""" ) , push_to_hub=snake_case__ , use_auth_token=self._token )
lowercase = WavaVecaProcessor.from_pretrained(F"""{USER}/test-processor""" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(snake_case__ , getattr(new_processor.feature_extractor , snake_case__ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
lowercase = WavaVecaProcessor.from_pretrained(snake_case__ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(snake_case__ , """test-processor-org""" ) , push_to_hub=snake_case__ , use_auth_token=self._token , organization="""valid_org""" , )
lowercase = WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org""" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(snake_case__ , getattr(new_processor.feature_extractor , snake_case__ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
lowercase = CustomFeatureExtractor.from_pretrained(snake_case__ )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase = os.path.join(snake_case__ , """vocab.txt""" )
with open(snake_case__ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowercase = CustomTokenizer(snake_case__ )
lowercase = CustomProcessor(snake_case__ , snake_case__ )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(F"""{USER}/test-dynamic-processor""" , token=self._token )
lowercase = Repository(snake_case__ , clone_from=F"""{USER}/test-dynamic-processor""" , token=self._token )
processor.save_pretrained(snake_case__ )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
"""AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
"""AutoProcessor""": """custom_processing.CustomProcessor""",
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(snake_case__ , """tokenizer_config.json""" ) ) as f:
lowercase = json.load(snake_case__ )
self.assertDictEqual(
tokenizer_config["""auto_map"""] , {
"""AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
"""AutoProcessor""": """custom_processing.CustomProcessor""",
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(snake_case__ , """custom_feature_extraction.py""" ) ) )
self.assertTrue(os.path.isfile(os.path.join(snake_case__ , """custom_tokenization.py""" ) ) )
self.assertTrue(os.path.isfile(os.path.join(snake_case__ , """custom_processing.py""" ) ) )
repo.push_to_hub()
lowercase = AutoProcessor.from_pretrained(F"""{USER}/test-dynamic-processor""" , trust_remote_code=snake_case__ )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , """CustomProcessor""" )
| 72
| 1
|
"""simple docstring"""
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = set_counts
lowerCAmelCase = max(_snake_case )
lowerCAmelCase = len(_snake_case )
lowerCAmelCase = [1] * num_sets
lowerCAmelCase = list(range(_snake_case ) )
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.get_parent(_snake_case )
lowerCAmelCase = self.get_parent(_snake_case )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
lowerCAmelCase = 0
lowerCAmelCase = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
lowerCAmelCase = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
lowerCAmelCase = 0
lowerCAmelCase = src_parent
lowerCAmelCase = self.set_counts[src_parent]
lowerCAmelCase = max(self.max_set , _snake_case )
return True
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
if self.parents[disj_set] == disj_set:
return disj_set
lowerCAmelCase = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 4
|
'''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 _UpperCamelCase :
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=64 , _a=2 , _a=3 , _a=True , _a=True , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=10 , _a=0.02 , _a=[1, 16, 4, 4] , _a=None , ):
"""simple docstring"""
a__ = parent
a__ = batch_size
a__ = image_size
a__ = patch_size
a__ = num_channels
a__ = is_training
a__ = use_labels
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__ = type_sequence_label_size
a__ = initializer_range
a__ = scope
a__ = 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
a__ = (self.image_size // 32) ** 2
a__ = num_patches + 1
def lowercase__ ( self ):
"""simple docstring"""
a__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a__ = None
if self.use_labels:
a__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ = self.get_config()
return config, pixel_values, labels
def lowercase__ ( self ):
"""simple docstring"""
a__ = {
'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=_a , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=_a , )
def lowercase__ ( self , _a , _a , _a ):
"""simple docstring"""
a__ = ViTHybridModel(config=_a )
model.to(_a )
model.eval()
a__ = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self , _a , _a , _a ):
"""simple docstring"""
a__ = self.type_sequence_label_size
a__ = ViTHybridForImageClassification(_a )
model.to(_a )
model.eval()
a__ = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowercase__ ( self ):
"""simple docstring"""
a__ = self.prepare_config_and_inputs()
a__ , a__ , a__ = config_and_inputs
a__ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( _A , _A , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE:Union[str, Any] = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
SCREAMING_SNAKE_CASE:Tuple = (
{'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE:Union[str, Any] = False
SCREAMING_SNAKE_CASE:Optional[int] = False
SCREAMING_SNAKE_CASE:Tuple = False
def lowercase__ ( self ):
"""simple docstring"""
a__ = ViTHybridModelTester(self )
a__ = ConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='ViT does not use inputs_embeds' )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
a__ , a__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ = model_class(_a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
a__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def lowercase__ ( self ):
"""simple docstring"""
a__ , a__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ = model_class(_a )
a__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__ = [*signature.parameters.keys()]
a__ = ['pixel_values']
self.assertListEqual(arg_names[:1] , _a )
def lowercase__ ( self ):
"""simple docstring"""
a__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def lowercase__ ( self ):
"""simple docstring"""
a__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
def lowercase__ ( self ):
"""simple docstring"""
a__ , a__ = self.model_tester.prepare_config_and_inputs_for_common()
a__ = _config_zero_init(_a )
for model_class in self.all_model_classes:
a__ = model_class(config=_a )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
a__ = [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 lowercase__ ( self ):
"""simple docstring"""
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ = ViTHybridModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def lowerCAmelCase_ ( ):
a__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowercase__ ( self ):
"""simple docstring"""
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowercase__ ( self ):
"""simple docstring"""
a__ = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
_a )
a__ = self.default_image_processor
a__ = prepare_img()
a__ = image_processor(images=_a , return_tensors='pt' ).to(_a )
# forward pass
with torch.no_grad():
a__ = model(**_a )
# verify the logits
a__ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _a )
a__ = torch.tensor([-1.9090, -0.4993, -0.2389] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
@slow
@require_accelerate
def lowercase__ ( self ):
"""simple docstring"""
a__ = ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384' )
a__ = ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384' , device_map='auto' )
a__ = prepare_img()
a__ = image_processor(images=_a , return_tensors='pt' )
a__ = model(**_a )
a__ = outputs.logits
# model predicts one of the 1000 ImageNet classes
a__ = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , 'tabby, tabby cat' )
| 394
| 0
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowercase__ ( _snake_case, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = KandinskyVaaInpaintPipeline
_UpperCAmelCase = ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
_UpperCAmelCase = [
'image_embeds',
'negative_image_embeds',
'image',
'mask_image',
]
_UpperCAmelCase = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
_UpperCAmelCase = False
@property
def lowerCamelCase_ ( self ) -> Dict:
return 32
@property
def lowerCamelCase_ ( self ) -> Any:
return 32
@property
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return self.time_input_dim
@property
def lowerCamelCase_ ( self ) -> List[str]:
return self.time_input_dim * 4
@property
def lowerCamelCase_ ( self ) -> List[Any]:
return 100
@property
def lowerCamelCase_ ( self ) -> Any:
torch.manual_seed(0 )
_UpperCAmelCase = {
'in_channels': 9,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': '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': '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(**snake_case )
return model
@property
def lowerCamelCase_ ( self ) -> int:
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 lowerCamelCase_ ( self ) -> Tuple:
torch.manual_seed(0 )
_UpperCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = self.dummy_unet
_UpperCAmelCase = self.dummy_movq
_UpperCAmelCase = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='linear' , beta_start=0.00085 , beta_end=0.012 , clip_sample=snake_case , set_alpha_to_one=snake_case , steps_offset=1 , prediction_type='epsilon' , thresholding=snake_case , )
_UpperCAmelCase = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Any:
_UpperCAmelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(snake_case ) ).to(snake_case )
_UpperCAmelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
snake_case )
# create init_image
_UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(snake_case ) ).to(snake_case )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase = Image.fromarray(np.uinta(snake_case ) ).convert('RGB' ).resize((256, 256) )
# create mask
_UpperCAmelCase = np.ones((64, 64) , dtype=np.floataa )
_UpperCAmelCase = 0
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
_UpperCAmelCase = pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = pipe(**self.get_dummy_inputs(snake_case ) )
_UpperCAmelCase = output.images
_UpperCAmelCase = pipe(
**self.get_dummy_inputs(snake_case ) , return_dict=snake_case , )[0]
_UpperCAmelCase = image[0, -3:, -3:, -1]
_UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
print(f'image.shape {image.shape}' )
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase = np.array(
[0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848] )
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()}'
def lowerCamelCase_ ( self ) -> Tuple:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> List[Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy' )
_UpperCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
_UpperCAmelCase = np.ones((768, 768) , dtype=np.floataa )
_UpperCAmelCase = 0
_UpperCAmelCase = 'a hat'
_UpperCAmelCase = KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(snake_case )
_UpperCAmelCase = KandinskyVaaInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-decoder-inpaint' , torch_dtype=torch.floataa )
_UpperCAmelCase = pipeline.to(snake_case )
pipeline.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = torch.Generator(device='cpu' ).manual_seed(0 )
_UpperCAmelCase , _UpperCAmelCase = pipe_prior(
snake_case , generator=snake_case , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
_UpperCAmelCase = pipeline(
image=snake_case , mask_image=snake_case , image_embeds=snake_case , negative_image_embeds=snake_case , generator=snake_case , num_inference_steps=100 , height=768 , width=768 , output_type='np' , )
_UpperCAmelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(snake_case , snake_case )
| 713
|
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
lowercase = logging.getLogger()
def UpperCAmelCase ( A : Path , A : list ):
'''simple docstring'''
_UpperCAmelCase = '\n'.join(A )
Path(A ).open('w' ).writelines(A )
lowercase = '''patrickvonplaten/t5-tiny-random'''
lowercase = '''sshleifer/bart-tiny-random'''
lowercase = '''sshleifer/tiny-mbart'''
lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case ) -> str:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
_dump_articles(snake_case , snake_case )
_UpperCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(snake_case , 'argv' , snake_case ):
run_generate()
assert Path(snake_case ).exists()
# os.remove(Path(output_file_name))
def lowerCamelCase_ ( self ) -> str:
self.run_eval_tester(snake_case )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
self.run_eval_tester(snake_case )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> Dict:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = {
'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
'de': [
'Maschinelles Lernen ist großartig, oder?',
'Ich esse gerne Bananen',
'Morgen ist wieder ein toller Tag!',
],
}
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() )
_UpperCAmelCase = str(tmp_dir / 'scores.json' )
_UpperCAmelCase = str(tmp_dir / 'val.target' )
_dump_articles(snake_case , text['en'] )
_dump_articles(snake_case , text['de'] )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {str(snake_case )}\n {str(snake_case )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )
with patch.object(snake_case , 'argv' , snake_case ):
with CaptureStdout() as cs:
run_search()
_UpperCAmelCase = [' num_beams | length_penalty', model, 'Best score args']
_UpperCAmelCase = ['Info']
if "translation" in task:
expected_strings.append('bleu' )
else:
expected_strings.extend(snake_case )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(snake_case ).exists()
os.remove(Path(snake_case ) )
| 24
| 0
|
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _SCREAMING_SNAKE_CASE ( datasets.BuilderConfig ):
'''simple docstring'''
lowerCamelCase__ = None
class _SCREAMING_SNAKE_CASE ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
lowerCamelCase__ = PandasConfig
def _snake_case ( self : Tuple ):
return datasets.DatasetInfo(features=self.config.features )
def _snake_case ( self : int , __lowerCamelCase : str ):
if not self.config.data_files:
raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}" )
SCREAMING_SNAKE_CASE = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__lowerCamelCase , (str, list, tuple) ):
SCREAMING_SNAKE_CASE = data_files
if isinstance(__lowerCamelCase , __lowerCamelCase ):
SCREAMING_SNAKE_CASE = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
SCREAMING_SNAKE_CASE = [dl_manager.iter_files(__lowerCamelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
SCREAMING_SNAKE_CASE = []
for split_name, files in data_files.items():
if isinstance(__lowerCamelCase , __lowerCamelCase ):
SCREAMING_SNAKE_CASE = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
SCREAMING_SNAKE_CASE = [dl_manager.iter_files(__lowerCamelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=__lowerCamelCase , gen_kwargs={"files": files} ) )
return splits
def _snake_case ( self : Optional[Any] , __lowerCamelCase : pa.Table ):
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
SCREAMING_SNAKE_CASE = table_cast(__lowerCamelCase , self.config.features.arrow_schema )
return pa_table
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : Tuple ):
for i, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase ) ):
with open(__lowerCamelCase , "rb" ) as f:
SCREAMING_SNAKE_CASE = pa.Table.from_pandas(pd.read_pickle(__lowerCamelCase ) )
yield i, self._cast_table(__lowerCamelCase )
| 16
|
'''simple docstring'''
def lowerCamelCase__ ( a ):
assert (
isinstance(a , a ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
if number_of_steps == 1:
return 1
__snake_case , __snake_case = 1, 1
for _ in range(number_of_steps - 1 ):
__snake_case , __snake_case = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod()
| 356
| 0
|
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class _a :
'''simple docstring'''
def __init__( self , UpperCAmelCase_ , UpperCAmelCase_ = 13 , UpperCAmelCase_ = 64 , UpperCAmelCase_ = 2 , UpperCAmelCase_ = 3 , UpperCAmelCase_ = 3 , UpperCAmelCase_ = True , UpperCAmelCase_ = True , UpperCAmelCase_ = 128 , UpperCAmelCase_=[16, 32, 64, 128] , UpperCAmelCase_ = 7 , UpperCAmelCase_ = 4 , UpperCAmelCase_ = 37 , UpperCAmelCase_ = "gelu" , UpperCAmelCase_ = 0.1 , UpperCAmelCase_ = 0.1 , UpperCAmelCase_ = 10 , UpperCAmelCase_ = 0.02 , UpperCAmelCase_ = 2 , UpperCAmelCase_ = 1 , UpperCAmelCase_ = 128 , UpperCAmelCase_ = [2, 2, 2, 2] , UpperCAmelCase_ = 2 , UpperCAmelCase_ = 2 , ) -> str:
'''simple docstring'''
lowercase__: str = parent
lowercase__: Any = batch_size
lowercase__: Tuple = image_size
lowercase__: Union[str, Any] = patch_size
lowercase__: Optional[int] = num_channels
lowercase__: Optional[Any] = is_training
lowercase__: Dict = use_labels
lowercase__: Optional[int] = hidden_size
lowercase__: str = num_hidden_layers
lowercase__: Union[str, Any] = num_attention_heads
lowercase__: Union[str, Any] = intermediate_size
lowercase__: str = hidden_act
lowercase__: Optional[Any] = hidden_dropout_prob
lowercase__: List[Any] = attention_probs_dropout_prob
lowercase__: Optional[int] = type_sequence_label_size
lowercase__: str = initializer_range
lowercase__: Optional[int] = encoder_stride
lowercase__: List[Any] = num_attention_outputs
lowercase__: Union[str, Any] = embed_dim
lowercase__: Any = embed_dim + 1
lowercase__: str = resolution
lowercase__: Any = depths
lowercase__: Tuple = hidden_sizes
lowercase__: Union[str, Any] = dim
lowercase__: Optional[Any] = mlp_expansion_ratio
def __lowercase ( self) -> Tuple:
'''simple docstring'''
lowercase__: Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowercase__: Optional[Any] = None
if self.use_labels:
lowercase__: Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowercase__: Tuple = self.get_config()
return config, pixel_values, labels
def __lowercase ( self) -> List[str]:
'''simple docstring'''
return EfficientFormerConfig(
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=A_ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def __lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_) -> List[Any]:
'''simple docstring'''
lowercase__: List[str] = TFEfficientFormerModel(config=A_)
lowercase__: str = model(A_ , training=A_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def __lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_) -> Union[str, Any]:
'''simple docstring'''
lowercase__: Dict = self.type_sequence_label_size
lowercase__: Optional[int] = TFEfficientFormerForImageClassification(A_)
lowercase__: Optional[Any] = model(A_ , labels=A_ , training=A_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
# test greyscale images
lowercase__: Dict = 1
lowercase__: List[Any] = TFEfficientFormerForImageClassification(A_)
lowercase__: Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
lowercase__: Optional[int] = model(A_ , labels=A_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
lowercase__: Union[str, Any] = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__: Dict = config_and_inputs
lowercase__: Optional[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class _a ( _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase__ = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
UpperCamelCase__ = (
{
'''feature-extraction''': TFEfficientFormerModel,
'''image-classification''': (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
lowercase__: Optional[int] = TFEfficientFormerModelTester(self)
lowercase__: Any = ConfigTester(
self , config_class=A_ , has_text_modality=A_ , hidden_size=37)
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="EfficientFormer does not use inputs_embeds")
def __lowercase ( self) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="EfficientFormer does not support input and output embeddings")
def __lowercase ( self) -> Dict:
'''simple docstring'''
pass
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
lowercase__ , lowercase__: Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__: Dict = model_class(A_)
lowercase__: Optional[Any] = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__: str = [*signature.parameters.keys()]
lowercase__: Optional[int] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , A_)
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
def check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_):
lowercase__: List[str] = model_class(A_)
lowercase__: Optional[Any] = model(**self._prepare_for_class(A_ , A_) , training=A_)
lowercase__: Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase__: Optional[Any] = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1)
self.assertEqual(len(A_) , A_)
if hasattr(self.model_tester , "encoder_seq_length"):
lowercase__: Union[str, Any] = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , "chunk_length") and self.model_tester.chunk_length > 1:
lowercase__: Optional[int] = seq_length * self.model_tester.chunk_length
else:
lowercase__: Union[str, Any] = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:]) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
lowercase__: Optional[Any] = outputs.decoder_hidden_states
self.asseretIsInstance(A_ , (list, tuple))
self.assertEqual(len(A_) , A_)
lowercase__: List[Any] = getattr(self.model_tester , "seq_length" , A_)
lowercase__: Any = getattr(self.model_tester , "decoder_seq_length" , A_)
self.assertListEqual(
list(hidden_states[-1].shape[-2:]) , [decoder_seq_length, self.model_tester.hidden_size] , )
lowercase__ , lowercase__: Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__: Any = True
check_hidden_states_output(A_ , A_ , A_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__: Union[str, Any] = True
check_hidden_states_output(A_ , A_ , A_)
def __lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=False) -> List[Any]:
'''simple docstring'''
lowercase__: Any = super()._prepare_for_class(A_ , A_ , return_labels=A_)
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __lowercase ( self) -> Any:
'''simple docstring'''
lowercase__: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_)
@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet")
def __lowercase ( self) -> List[str]:
'''simple docstring'''
lowercase__: Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*A_)
def __lowercase ( self) -> List[str]:
'''simple docstring'''
lowercase__: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A_)
@slow
def __lowercase ( self) -> Any:
'''simple docstring'''
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__: Any = TFEfficientFormerModel.from_pretrained(A_)
self.assertIsNotNone(A_)
def __lowercase ( self) -> Any:
'''simple docstring'''
lowercase__ , lowercase__: List[str] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__: Optional[Any] = True
lowercase__: Dict = getattr(self.model_tester , "seq_length" , A_)
lowercase__: Union[str, Any] = getattr(self.model_tester , "encoder_seq_length" , A_)
lowercase__: List[str] = getattr(self.model_tester , "key_length" , A_)
lowercase__: Dict = getattr(self.model_tester , "chunk_length" , A_)
if chunk_length is not None and hasattr(self.model_tester , "num_hashes"):
lowercase__: Union[str, Any] = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
lowercase__: Any = True
lowercase__: Dict = False
lowercase__: Optional[Any] = True
lowercase__: str = model_class(A_)
lowercase__: List[str] = model(**self._prepare_for_class(A_ , A_) , training=A_)
lowercase__: Optional[int] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A_) , self.model_tester.num_attention_outputs)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__: Dict = True
lowercase__: int = model_class(A_)
lowercase__: Tuple = model(**self._prepare_for_class(A_ , A_) , training=A_)
lowercase__: List[Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A_) , self.model_tester.num_attention_outputs)
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:]) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
lowercase__ , lowercase__: str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
lowercase__: Dict = model_class(A_)
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
lowercase__: Union[str, Any] = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=A_)
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
lowercase__: int = model(A_)
self.assertTrue(outputs_dict is not None)
def A( ):
"""simple docstring"""
lowercase__: List[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class _a ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
return (
EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300")
if is_vision_available()
else None
)
@slow
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
lowercase__: int = TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300")
lowercase__: int = self.default_image_processor
lowercase__: Dict = prepare_img()
lowercase__: Optional[int] = image_processor(images=A_ , return_tensors="tf")
# forward pass
lowercase__: List[str] = model(**A_ , training=A_)
# verify the logits
lowercase__: List[str] = tf.TensorShape((1, 1_000))
self.assertEqual(outputs.logits.shape , A_)
lowercase__: Optional[int] = tf.constant([-0.05_55, 0.48_25, -0.08_52])
self.assertTrue(np.allclose(outputs.logits[0, :3] , A_ , atol=1E-4))
@slow
def __lowercase ( self) -> int:
'''simple docstring'''
lowercase__: Dict = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"snap-research/efficientformer-l1-300")
lowercase__: List[str] = self.default_image_processor
lowercase__: Any = prepare_img()
lowercase__: Tuple = image_processor(images=A_ , return_tensors="tf")
# forward pass
lowercase__: int = model(**A_ , training=A_)
# verify the logits
lowercase__: Union[str, Any] = tf.TensorShape((1, 1_000))
self.assertEqual(outputs.logits.shape , A_)
lowercase__: Optional[Any] = tf.constant([-0.13_12, 0.43_53, -1.04_99])
self.assertTrue(np.allclose(outputs.logits[0, :3] , A_ , atol=1E-4))
| 700
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
"""SqueezeBertOnnxConfig""",
],
"""tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ["""SqueezeBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
"""SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SqueezeBertForMaskedLM""",
"""SqueezeBertForMultipleChoice""",
"""SqueezeBertForQuestionAnswering""",
"""SqueezeBertForSequenceClassification""",
"""SqueezeBertForTokenClassification""",
"""SqueezeBertModel""",
"""SqueezeBertModule""",
"""SqueezeBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_squeezebert import (
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
SqueezeBertConfig,
SqueezeBertOnnxConfig,
)
from .tokenization_squeezebert import SqueezeBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertModule,
SqueezeBertPreTrainedModel,
)
else:
import sys
UpperCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 120
| 0
|
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def _A( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
if a == 0:
raise ValueError("""Coefficient 'a' must not be zero.""" )
A__ : Any = b * b - 4 * a * c
A__ : List[str] = (-b + sqrt(lowerCAmelCase )) / (2 * a)
A__ : Union[str, Any] = (-b - sqrt(lowerCAmelCase )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def _A( ):
A__ , A__ : str = quadratic_roots(a=5 , b=6 , c=1 )
print(F'''The solutions are: {solutiona} and {solutiona}''' )
if __name__ == "__main__":
main()
| 363
|
"""simple docstring"""
from __future__ import annotations
def _A( lowerCAmelCase ):
if len(lowerCAmelCase ) == 0:
return []
A__ , A__ : Dict = min(lowerCAmelCase ), max(lowerCAmelCase )
A__ : List[Any] = int(max_value - min_value ) + 1
A__ : list[list] = [[] for _ in range(lowerCAmelCase )]
for i in my_list:
buckets[int(i - min_value )].append(lowerCAmelCase )
return [v for bucket in buckets for v in sorted(lowerCAmelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| 363
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCAmelCase_ ( __A ):
'''simple docstring'''
_lowercase = 'megatron-bert'
def __init__( self , __UpperCAmelCase=29_056 , __UpperCAmelCase=1_024 , __UpperCAmelCase=24 , __UpperCAmelCase=16 , __UpperCAmelCase=4_096 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-12 , __UpperCAmelCase=0 , __UpperCAmelCase="absolute" , __UpperCAmelCase=True , **__UpperCAmelCase , ):
super().__init__(pad_token_id=__UpperCAmelCase , **__UpperCAmelCase )
SCREAMING_SNAKE_CASE_ : Any =vocab_size
SCREAMING_SNAKE_CASE_ : int =hidden_size
SCREAMING_SNAKE_CASE_ : Optional[int] =num_hidden_layers
SCREAMING_SNAKE_CASE_ : List[Any] =num_attention_heads
SCREAMING_SNAKE_CASE_ : Any =hidden_act
SCREAMING_SNAKE_CASE_ : Optional[int] =intermediate_size
SCREAMING_SNAKE_CASE_ : int =hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : List[str] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : List[Any] =max_position_embeddings
SCREAMING_SNAKE_CASE_ : Union[str, Any] =type_vocab_size
SCREAMING_SNAKE_CASE_ : Any =initializer_range
SCREAMING_SNAKE_CASE_ : Dict =layer_norm_eps
SCREAMING_SNAKE_CASE_ : List[str] =position_embedding_type
SCREAMING_SNAKE_CASE_ : Any =use_cache
| 153
|
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ,lowerCAmelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] =get_failure_array(lowerCAmelCase_ )
# 2) Step through text searching for pattern
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[Any] =0, 0 # index into text, pattern
while i < len(lowerCAmelCase_ ):
if pattern[j] == text[i]:
if j == (len(lowerCAmelCase_ ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
SCREAMING_SNAKE_CASE_ : Optional[Any] =failure[j - 1]
continue
i += 1
return False
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any =[0]
SCREAMING_SNAKE_CASE_ : List[str] =0
SCREAMING_SNAKE_CASE_ : int =1
while j < len(lowerCAmelCase_ ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
SCREAMING_SNAKE_CASE_ : Optional[int] =failure[i - 1]
continue
j += 1
failure.append(lowerCAmelCase_ )
return failure
if __name__ == "__main__":
# Test 1)
__SCREAMING_SNAKE_CASE = 'abc1abc12'
__SCREAMING_SNAKE_CASE = 'alskfjaldsabc1abc1abc12k23adsfabcabc'
__SCREAMING_SNAKE_CASE = 'alskfjaldsk23adsfabcabc'
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
__SCREAMING_SNAKE_CASE = 'ABABX'
__SCREAMING_SNAKE_CASE = 'ABABZABABYABABX'
assert kmp(pattern, text)
# Test 3)
__SCREAMING_SNAKE_CASE = 'AAAB'
__SCREAMING_SNAKE_CASE = 'ABAAAAAB'
assert kmp(pattern, text)
# Test 4)
__SCREAMING_SNAKE_CASE = 'abcdabcy'
__SCREAMING_SNAKE_CASE = 'abcxabcdabxabcdabcdabcy'
assert kmp(pattern, text)
# Test 5)
__SCREAMING_SNAKE_CASE = 'aabaabaaa'
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 153
| 1
|
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class lowerCamelCase_ :
def __init__( self : Optional[int] , __A : Union[str, Any] , __A : Dict=13 , __A : Any=10 , __A : Any=3 , __A : Tuple=2 , __A : List[Any]=2 , __A : Union[str, Any]=True , __A : Tuple=True , __A : str=32 , __A : Dict=5 , __A : List[str]=4 , __A : Any=37 , __A : str="gelu" , __A : Optional[Any]=0.1 , __A : Tuple=0.1 , __A : Union[str, Any]=10 , __A : Dict=0.0_2 , __A : str="divided_space_time" , __A : Any=None , ):
__A : Optional[Any] = parent
__A : str = batch_size
__A : int = image_size
__A : Dict = num_channels
__A : str = patch_size
__A : Optional[Any] = num_frames
__A : List[Any] = is_training
__A : Optional[Any] = use_labels
__A : List[Any] = hidden_size
__A : int = num_hidden_layers
__A : Tuple = num_attention_heads
__A : Union[str, Any] = intermediate_size
__A : Dict = hidden_act
__A : Union[str, Any] = hidden_dropout_prob
__A : Tuple = attention_probs_dropout_prob
__A : List[str] = attention_type
__A : Optional[Any] = initializer_range
__A : List[Any] = scope
__A : List[Any] = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
__A : Union[str, Any] = (image_size // patch_size) ** 2
__A : int = (num_frames) * self.num_patches_per_frame + 1
def lowerCAmelCase_ ( self : int ):
__A : Dict = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__A : List[Any] = None
if self.use_labels:
__A : Tuple = ids_tensor([self.batch_size] , self.num_labels )
__A : Any = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase_ ( self : Optional[Any] ):
__A : str = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
__A : Optional[int] = self.num_labels
return config
def lowerCAmelCase_ ( self : Any , __A : List[str] , __A : Optional[Any] , __A : int ):
__A : Tuple = TimesformerModel(config=__A )
model.to(__A )
model.eval()
__A : Tuple = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase_ ( self : Dict , __A : Any , __A : Any , __A : Union[str, Any] ):
__A : Optional[Any] = TimesformerForVideoClassification(__A )
model.to(__A )
model.eval()
__A : Union[str, Any] = model(__A )
# verify the logits shape
__A : str = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __A )
def lowerCAmelCase_ ( self : Tuple ):
__A : List[str] = self.prepare_config_and_inputs()
__A : str = config_and_inputs
__A : List[Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( a_ , a_ , unittest.TestCase ):
_lowercase : Optional[Any] = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
_lowercase : str = (
{'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
if is_torch_available()
else {}
)
_lowercase : List[Any] = False
_lowercase : Union[str, Any] = False
_lowercase : Any = False
_lowercase : Union[str, Any] = False
def lowerCAmelCase_ ( self : List[Any] ):
__A : List[Any] = TimesformerModelTester(self )
__A : List[str] = ConfigTester(
self , config_class=__A , has_text_modality=__A , hidden_size=37 )
def lowerCAmelCase_ ( self : Optional[int] , __A : Dict , __A : Optional[int] , __A : Optional[Any]=False ):
__A : List[str] = copy.deepcopy(__A )
if return_labels:
if model_class in get_values(__A ):
__A : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__A )
return inputs_dict
def lowerCAmelCase_ ( self : List[Any] ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""TimeSformer does not use inputs_embeds""" )
def lowerCAmelCase_ ( self : Any ):
pass
def lowerCAmelCase_ ( self : Union[str, Any] ):
__A : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A : List[str] = model_class(__A )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__A : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A , nn.Linear ) )
def lowerCAmelCase_ ( self : int ):
__A : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A : Union[str, Any] = model_class(__A )
__A : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__A : Dict = [*signature.parameters.keys()]
__A : Dict = ['pixel_values']
self.assertListEqual(arg_names[:1] , __A )
def lowerCAmelCase_ ( self : Union[str, Any] ):
__A : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def lowerCAmelCase_ ( self : str ):
__A : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__A )
@slow
def lowerCAmelCase_ ( self : str ):
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A : Optional[int] = TimesformerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def lowerCAmelCase_ ( self : Union[str, Any] ):
if not self.has_attentions:
pass
else:
__A : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
__A : Union[str, Any] = True
for model_class in self.all_model_classes:
__A : int = self.model_tester.seq_length
__A : Tuple = self.model_tester.num_frames
__A : Tuple = True
__A : List[str] = False
__A : Any = True
__A : Optional[Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
__A : List[Any] = model(**self._prepare_for_class(__A , __A ) )
__A : Any = outputs.attentions
self.assertEqual(len(__A ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__A : Dict = True
__A : Optional[int] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
__A : str = model(**self._prepare_for_class(__A , __A ) )
__A : Dict = outputs.attentions
self.assertEqual(len(__A ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
__A : Optional[Any] = len(__A )
# Check attention is always last and order is fine
__A : Optional[int] = True
__A : Tuple = True
__A : Tuple = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
__A : str = model(**self._prepare_for_class(__A , __A ) )
self.assertEqual(out_len + 1 , len(__A ) )
__A : Optional[Any] = outputs.attentions
self.assertEqual(len(__A ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def lowerCAmelCase_ ( self : List[Any] ):
def check_hidden_states_output(__A : Union[str, Any] , __A : List[str] , __A : List[Any] ):
__A : Dict = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
__A : Tuple = model(**self._prepare_for_class(__A , __A ) )
__A : Dict = outputs.hidden_states
__A : Optional[Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__A ) , __A )
__A : Tuple = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__A : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A : Union[str, Any] = True
check_hidden_states_output(__A , __A , __A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__A : List[str] = True
check_hidden_states_output(__A , __A , __A )
def __SCREAMING_SNAKE_CASE ( ) -> List[str]:
__A : List[str] = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" ,filename="""eating_spaghetti.npy""" ,repo_type="""dataset""" )
__A : Optional[int] = np.load(a__ )
return list(a__ )
@require_torch
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@cached_property
def lowerCAmelCase_ ( self : List[str] ):
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def lowerCAmelCase_ ( self : Any ):
__A : int = TimesformerForVideoClassification.from_pretrained("""facebook/timesformer-base-finetuned-k400""" ).to(
__A )
__A : str = self.default_image_processor
__A : Optional[Any] = prepare_video()
__A : str = image_processor(video[:8] , return_tensors="""pt""" ).to(__A )
# forward pass
with torch.no_grad():
__A : Tuple = model(**__A )
# verify the logits
__A : str = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , __A )
__A : Optional[Any] = torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __A , atol=1e-4 ) )
| 17
|
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
A : Optional[Any] = logging.get_logger(__name__)
class __lowerCamelCase :
"""simple docstring"""
a = 42
a = None
@staticmethod
def A ( ):
raise NotImplementedError
def A ( self : str , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str , **SCREAMING_SNAKE_CASE : str):
raise NotImplementedError
def A ( self : Tuple , SCREAMING_SNAKE_CASE : Any):
raise NotImplementedError
def A ( self : Tuple):
if not self.is_available():
raise RuntimeError(
F'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.')
@classmethod
def A ( cls : Tuple):
return F'`pip install {cls.pip_package or cls.name}`'
class __lowerCamelCase ( a_ ):
"""simple docstring"""
a = "optuna"
@staticmethod
def A ( ):
return is_optuna_available()
def A ( self : List[str] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str , **SCREAMING_SNAKE_CASE : str):
return run_hp_search_optuna(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE)
def A ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : int):
return default_hp_space_optuna(SCREAMING_SNAKE_CASE)
class __lowerCamelCase ( a_ ):
"""simple docstring"""
a = "ray"
a = "'ray[tune]'"
@staticmethod
def A ( ):
return is_ray_available()
def A ( self : str , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str , **SCREAMING_SNAKE_CASE : Dict):
return run_hp_search_ray(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE)
def A ( self : Any , SCREAMING_SNAKE_CASE : Optional[Any]):
return default_hp_space_ray(SCREAMING_SNAKE_CASE)
class __lowerCamelCase ( a_ ):
"""simple docstring"""
a = "sigopt"
@staticmethod
def A ( ):
return is_sigopt_available()
def A ( self : Any , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str , **SCREAMING_SNAKE_CASE : List[Any]):
return run_hp_search_sigopt(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE)
def A ( self : List[Any] , SCREAMING_SNAKE_CASE : List[Any]):
return default_hp_space_sigopt(SCREAMING_SNAKE_CASE)
class __lowerCamelCase ( a_ ):
"""simple docstring"""
a = "wandb"
@staticmethod
def A ( ):
return is_wandb_available()
def A ( self : Optional[int] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str , **SCREAMING_SNAKE_CASE : str):
return run_hp_search_wandb(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE)
def A ( self : List[Any] , SCREAMING_SNAKE_CASE : List[Any]):
return default_hp_space_wandb(SCREAMING_SNAKE_CASE)
A : Dict = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCAmelCase__ ( ):
_A : Any = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowerCamelCase ) > 0:
_A : Optional[Any] = available_backends[0].name
if len(lowerCamelCase ) > 1:
logger.info(
F'{len(lowerCamelCase )} hyperparameter search backends available. Using {name} as the default.' )
return name
raise RuntimeError(
'No hyperparameter search backend available.\n'
+ '\n'.join(
F' - To install {backend.name} run {backend.pip_install()}'
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 128
| 0
|
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger()
@dataclass
class A :
"""simple docstring"""
lowerCamelCase = 42
lowerCamelCase = field(default_factory=_UpperCAmelCase )
lowerCamelCase = field(default_factory=_UpperCAmelCase )
def snake_case__ ( self : Union[str, Any],lowercase_ : Dict,lowercase_ : Tensor,lowercase_ : Tensor )-> Tuple:
'''simple docstring'''
A__ = len(list(m.modules() ) ) == 1 or isinstance(lowercase_,nn.Convad ) or isinstance(lowercase_,nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowercase_ )
def __call__( self : Tuple,lowercase_ : Tensor )-> Any:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowercase_ )
[x.remove() for x in self.handles]
return self
@property
def snake_case__ ( self : Optional[int] )-> Optional[int]:
'''simple docstring'''
return list(filter(lambda lowercase_ : len(list(x.state_dict().keys() ) ) > 0,self.traced ) )
@dataclass
class A :
"""simple docstring"""
lowerCamelCase = 42
lowerCamelCase = 42
lowerCamelCase = 1
lowerCamelCase = field(default_factory=_UpperCAmelCase )
lowerCamelCase = field(default_factory=_UpperCAmelCase )
lowerCamelCase = True
def __call__( self : str,lowercase_ : Tensor )-> Dict:
'''simple docstring'''
A__ = Tracker(self.dest )(lowercase_ ).parametrized
A__ = Tracker(self.src )(lowercase_ ).parametrized
A__ = list(filter(lambda lowercase_ : type(lowercase_ ) not in self.src_skip,lowercase_ ) )
A__ = list(filter(lambda lowercase_ : type(lowercase_ ) not in self.dest_skip,lowercase_ ) )
if len(lowercase_ ) != len(lowercase_ ) and self.raise_if_mismatch:
raise Exception(
F'Numbers of operations are different. Source module has {len(lowercase_ )} operations while'
F' destination module has {len(lowercase_ )}.' )
for dest_m, src_m in zip(lowercase_,lowercase_ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'Transfered from={src_m} to={dest_m}' )
class A ( nn.Module ):
"""simple docstring"""
def __init__( self : Any,lowercase_ : nn.Module )-> int:
'''simple docstring'''
super().__init__()
A__ = []
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), F'Unexpected layer name {k}'
A__ = len(lowercase_ ) + 1
feature_blocks.append((F'res{block_index}', v) )
A__ = nn.ModuleDict(lowercase_ )
def snake_case__ ( self : List[Any],lowercase_ : Tensor )-> Any:
'''simple docstring'''
return get_trunk_forward_outputs(
lowercase_,out_feat_keys=lowercase_,feature_blocks=self._feature_blocks,)
class A ( _UpperCAmelCase ):
"""simple docstring"""
def snake_case__ ( self : List[Any],lowercase_ : str )-> str:
'''simple docstring'''
A__ = x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Optional[Any],lowercase_ : str )-> Callable[[], Tuple[nn.Module, Dict]]:
'''simple docstring'''
if x not in self:
A__ = self.convert_name_to_timm(lowercase_ )
A__ = partial(lambda: (timm.create_model(lowercase_,pretrained=lowercase_ ).eval(), None) )
else:
A__ = super().__getitem__(lowercase_ )
return val
class A ( _UpperCAmelCase ):
"""simple docstring"""
def __getitem__( self : Tuple,lowercase_ : str )-> Callable[[], nn.Module]:
'''simple docstring'''
if "seer" in x and "in1k" not in x:
A__ = RegNetModel
else:
A__ = RegNetForImageClassification
return val
def _snake_case( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[Tuple[str, str]] ) -> Dict:
'''simple docstring'''
for from_key, to_key in keys:
A__ = from_state_dict[from_key].clone()
print(f'Copied key={from_key} to={to_key}' )
return to_state_dict
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Callable[[], nn.Module] , SCREAMING_SNAKE_CASE__ : Callable[[], nn.Module] , SCREAMING_SNAKE_CASE__ : RegNetConfig , SCREAMING_SNAKE_CASE__ : Path , SCREAMING_SNAKE_CASE__ : bool = True , ) -> Any:
'''simple docstring'''
print(f'Converting {name}...' )
with torch.no_grad():
A__ , A__ = from_model_func()
A__ = our_model_func(SCREAMING_SNAKE_CASE__ ).eval()
A__ = ModuleTransfer(src=SCREAMING_SNAKE_CASE__ , dest=SCREAMING_SNAKE_CASE__ , raise_if_mismatch=SCREAMING_SNAKE_CASE__ )
A__ = torch.randn((1, 3, 224, 224) )
module_transfer(SCREAMING_SNAKE_CASE__ )
if from_state_dict is not None:
A__ = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
A__ = [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
A__ = manually_copy_vissl_head(SCREAMING_SNAKE_CASE__ , our_model.state_dict() , SCREAMING_SNAKE_CASE__ )
our_model.load_state_dict(SCREAMING_SNAKE_CASE__ )
A__ = our_model(SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ )
A__ = (
our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else our_outputs.last_hidden_state
)
A__ = from_model(SCREAMING_SNAKE_CASE__ )
A__ = from_output[-1] if type(SCREAMING_SNAKE_CASE__ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
A__ = our_outputs.hidden_states[-1]
assert torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add model' , use_temp_dir=SCREAMING_SNAKE_CASE__ , )
A__ = 224 if 'seer' not in name else 384
# we can use the convnext one
A__ = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' , size=SCREAMING_SNAKE_CASE__ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add image processor' , use_temp_dir=SCREAMING_SNAKE_CASE__ , )
print(f'Pushed {name}' )
def _snake_case( SCREAMING_SNAKE_CASE__ : Path , SCREAMING_SNAKE_CASE__ : str = None , SCREAMING_SNAKE_CASE__ : bool = True ) -> List[Any]:
'''simple docstring'''
A__ = 'imagenet-1k-id2label.json'
A__ = 1000
A__ = (1, num_labels)
A__ = 'huggingface/label-files'
A__ = num_labels
A__ = json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type='dataset' ) ) , 'r' ) )
A__ = {int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}
A__ = idalabel
A__ = {v: k for k, v in idalabel.items()}
A__ = partial(SCREAMING_SNAKE_CASE__ , num_labels=SCREAMING_SNAKE_CASE__ , idalabel=SCREAMING_SNAKE_CASE__ , labelaid=SCREAMING_SNAKE_CASE__ )
A__ = {
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 152, 368] , groups_width=8 , layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 160, 384] , groups_width=16 , layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 240, 528] , groups_width=24 , layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 128, 288, 672] , groups_width=16 , layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 168, 408, 912] , groups_width=24 , layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 192, 432, 1008] , groups_width=48 , layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 240, 560, 1360] , groups_width=40 , layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[168, 392, 784, 1624] , groups_width=56 , layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 240, 720, 1920] , groups_width=120 , layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[224, 448, 896, 2240] , groups_width=112 , layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[256, 512, 896, 2048] , groups_width=128 , layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[336, 672, 1344, 2520] , groups_width=168 , layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 152, 368] , groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 104, 208, 440] , groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 112, 256, 608] , groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 128, 320, 768] , groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 120, 336, 888] , groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 216, 576, 1512] , groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[128, 192, 512, 1088] , groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[144, 288, 576, 1296] , groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[168, 448, 896, 2016] , groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[224, 448, 896, 2240] , groups_width=112 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[224, 448, 1232, 3024] , groups_width=112 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1392, 3712] , groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1392, 3712] , groups_width=232 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[328, 984, 1968, 4920] , groups_width=328 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[528, 1056, 2904, 7392] , groups_width=264 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[640, 1696, 2544, 5088] , groups_width=640 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[2020, 4040, 11110, 28280] , groups_width=1010 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1392, 3712] , groups_width=232 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[328, 984, 1968, 4920] , groups_width=328 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[528, 1056, 2904, 7392] , groups_width=264 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[640, 1696, 2544, 5088] , groups_width=640 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[2020, 4040, 11110, 28280] , groups_width=1010 ),
}
A__ = NameToOurModelFuncMap()
A__ = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Callable[[], nn.Module] ) -> Tuple[nn.Module, Dict]:
A__ = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__ , model_dir=str(SCREAMING_SNAKE_CASE__ ) , map_location='cpu' )
A__ = model_func()
# check if we have a head, if yes add it
A__ = files['classy_state_dict']['base_model']['model']
A__ = model_state_dict['trunk']
model.load_state_dict(SCREAMING_SNAKE_CASE__ )
return model.eval(), model_state_dict["heads"]
# pretrained
A__ = partial(
SCREAMING_SNAKE_CASE__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
A__ = partial(
SCREAMING_SNAKE_CASE__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
A__ = partial(
SCREAMING_SNAKE_CASE__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
A__ = partial(
SCREAMING_SNAKE_CASE__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=1010 , w_a=1744 , w_a=620.83 , w_m=2.52 ) ) ) , )
# IN1K finetuned
A__ = partial(
SCREAMING_SNAKE_CASE__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
A__ = partial(
SCREAMING_SNAKE_CASE__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
A__ = partial(
SCREAMING_SNAKE_CASE__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
A__ = partial(
SCREAMING_SNAKE_CASE__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=1010 , w_a=1744 , w_a=620.83 , w_m=2.52 ) ) ) , )
if model_name:
convert_weight_and_push(
SCREAMING_SNAKE_CASE__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
SCREAMING_SNAKE_CASE__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , )
return config, expected_shape
if __name__ == "__main__":
lowercase_ = 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 regnet* architecture,"
" currently: regnetx-*, regnety-*. 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_ = parser.parse_args()
lowercase_ = 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)
| 711
|
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 A :
"""simple docstring"""
def __init__( self : List[Any],lowercase_ : Dict,lowercase_ : Optional[Any]=1_3,lowercase_ : str=7,lowercase_ : Optional[Any]=True,lowercase_ : Dict=True,lowercase_ : Union[str, Any]=True,lowercase_ : List[Any]=True,lowercase_ : Any=9_9,lowercase_ : Dict=3_2,lowercase_ : str=2,lowercase_ : str=4,lowercase_ : Any=3_7,lowercase_ : Union[str, Any]="gelu",lowercase_ : Union[str, Any]=0.1,lowercase_ : Optional[int]=0.1,lowercase_ : Optional[int]=5_1_2,lowercase_ : Optional[int]=1_6,lowercase_ : str=2,lowercase_ : Optional[int]=0.02,lowercase_ : Union[str, Any]=3,lowercase_ : Optional[Any]=4,lowercase_ : Dict=None,)-> List[Any]:
'''simple docstring'''
A__ = parent
A__ = 1_3
A__ = 7
A__ = True
A__ = True
A__ = True
A__ = True
A__ = 9_9
A__ = 3_2
A__ = 2
A__ = 4
A__ = 3_7
A__ = 'gelu'
A__ = 0.1
A__ = 0.1
A__ = 5_1_2
A__ = 1_6
A__ = 2
A__ = 0.02
A__ = 3
A__ = 4
A__ = None
def snake_case__ ( self : Any )-> Any:
'''simple docstring'''
A__ = ids_tensor([self.batch_size, self.seq_length],self.vocab_size )
A__ = None
if self.use_input_mask:
A__ = random_attention_mask([self.batch_size, self.seq_length] )
A__ = None
if self.use_token_type_ids:
A__ = ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size )
A__ = None
A__ = None
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size],self.type_sequence_label_size )
A__ = ids_tensor([self.batch_size, self.seq_length],self.num_labels )
A__ = ids_tensor([self.batch_size],self.num_choices )
A__ = RoFormerConfig(
vocab_size=self.vocab_size,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,initializer_range=self.initializer_range,return_dict=lowercase_,)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case__ ( self : Union[str, Any],lowercase_ : int,lowercase_ : Dict,lowercase_ : int,lowercase_ : Any,lowercase_ : List[str],lowercase_ : Optional[Any],lowercase_ : Optional[int] )-> Any:
'''simple docstring'''
A__ = TFRoFormerModel(config=lowercase_ )
A__ = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
A__ = [input_ids, input_mask]
A__ = model(lowercase_ )
A__ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size) )
def snake_case__ ( self : Tuple,lowercase_ : Optional[Any],lowercase_ : int,lowercase_ : Tuple,lowercase_ : Optional[int],lowercase_ : List[Any],lowercase_ : Union[str, Any],lowercase_ : str )-> Tuple:
'''simple docstring'''
A__ = True
A__ = TFRoFormerForCausalLM(config=lowercase_ )
A__ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
A__ = model(lowercase_ )['logits']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ),[self.batch_size, self.seq_length, self.vocab_size] )
def snake_case__ ( self : Tuple,lowercase_ : Any,lowercase_ : Optional[Any],lowercase_ : Dict,lowercase_ : Optional[Any],lowercase_ : Optional[Any],lowercase_ : Any,lowercase_ : int )-> Any:
'''simple docstring'''
A__ = TFRoFormerForMaskedLM(config=lowercase_ )
A__ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
A__ = model(lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size) )
def snake_case__ ( self : Optional[Any],lowercase_ : Union[str, Any],lowercase_ : str,lowercase_ : Optional[Any],lowercase_ : Union[str, Any],lowercase_ : Tuple,lowercase_ : List[str],lowercase_ : Union[str, Any] )-> List[str]:
'''simple docstring'''
A__ = self.num_labels
A__ = TFRoFormerForSequenceClassification(config=lowercase_ )
A__ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
A__ = model(lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels) )
def snake_case__ ( self : List[Any],lowercase_ : List[str],lowercase_ : Tuple,lowercase_ : Tuple,lowercase_ : Tuple,lowercase_ : List[str],lowercase_ : Optional[int],lowercase_ : Dict )-> List[str]:
'''simple docstring'''
A__ = self.num_choices
A__ = TFRoFormerForMultipleChoice(config=lowercase_ )
A__ = tf.tile(tf.expand_dims(lowercase_,1 ),(1, self.num_choices, 1) )
A__ = tf.tile(tf.expand_dims(lowercase_,1 ),(1, self.num_choices, 1) )
A__ = tf.tile(tf.expand_dims(lowercase_,1 ),(1, self.num_choices, 1) )
A__ = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
A__ = model(lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices) )
def snake_case__ ( self : Tuple,lowercase_ : Dict,lowercase_ : Tuple,lowercase_ : Tuple,lowercase_ : Union[str, Any],lowercase_ : Dict,lowercase_ : List[Any],lowercase_ : List[str] )-> Optional[Any]:
'''simple docstring'''
A__ = self.num_labels
A__ = TFRoFormerForTokenClassification(config=lowercase_ )
A__ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
A__ = model(lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels) )
def snake_case__ ( self : Optional[Any],lowercase_ : Optional[Any],lowercase_ : Any,lowercase_ : Tuple,lowercase_ : List[str],lowercase_ : List[Any],lowercase_ : Tuple,lowercase_ : Tuple )-> Optional[Any]:
'''simple docstring'''
A__ = TFRoFormerForQuestionAnswering(config=lowercase_ )
A__ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
A__ = model(lowercase_ )
self.parent.assertEqual(result.start_logits.shape,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape,(self.batch_size, self.seq_length) )
def snake_case__ ( self : Union[str, Any] )-> Tuple:
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
(
(
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) ,
) = config_and_inputs
A__ = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class A ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
lowerCamelCase = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCamelCase = False
lowerCamelCase = False
def snake_case__ ( self : Dict,lowercase_ : str,lowercase_ : Union[str, Any],lowercase_ : int,lowercase_ : Tuple,lowercase_ : Any )-> List[str]:
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def snake_case__ ( self : Tuple )-> Optional[Any]:
'''simple docstring'''
A__ = TFRoFormerModelTester(self )
A__ = ConfigTester(self,config_class=lowercase_,hidden_size=3_7 )
def snake_case__ ( self : str )-> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self : str )-> str:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def snake_case__ ( self : List[str] )-> Tuple:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase_ )
def snake_case__ ( self : Optional[int] )-> str:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*lowercase_ )
def snake_case__ ( self : Optional[int] )-> List[str]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowercase_ )
def snake_case__ ( self : List[Any] )-> int:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase_ )
def snake_case__ ( self : Optional[Any] )-> Tuple:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase_ )
def snake_case__ ( self : Tuple )-> Any:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase_ )
@slow
def snake_case__ ( self : List[str] )-> Tuple:
'''simple docstring'''
A__ = TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base' )
self.assertIsNotNone(lowercase_ )
@require_tf
class A ( unittest.TestCase ):
"""simple docstring"""
@slow
def snake_case__ ( self : List[Any] )-> Optional[int]:
'''simple docstring'''
A__ = TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
A__ = tf.constant([[0, 1, 2, 3, 4, 5]] )
A__ = model(lowercase_ )[0]
# TODO Replace vocab size
A__ = 5_0_0_0_0
A__ = [1, 6, vocab_size]
self.assertEqual(output.shape,lowercase_ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
A__ = tf.constant(
[
[
[-0.12_053_341, -1.0_264_901, 0.29_221_946],
[-1.5_133_783, 0.197_433, 0.15_190_607],
[-5.0_135_403, -3.900_256, -0.84_038_764],
]
] )
tf.debugging.assert_near(output[:, :3, :3],lowercase_,atol=1E-4 )
@require_tf
class A ( unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = 1E-4
def snake_case__ ( self : Union[str, Any] )-> Optional[Any]:
'''simple docstring'''
A__ = tf.constant([[4, 1_0]] )
A__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6,embedding_dim=6 )
A__ = emba(input_ids.shape )
A__ = tf.constant(
[[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]] )
tf.debugging.assert_near(lowercase_,lowercase_,atol=self.tolerance )
def snake_case__ ( self : List[Any] )-> List[str]:
'''simple docstring'''
A__ = tf.constant(
[
[0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
[0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
[0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
] )
A__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2,embedding_dim=5_1_2 )
emba([2, 1_6, 5_1_2] )
A__ = emba.weight[:3, :5]
tf.debugging.assert_near(lowercase_,lowercase_,atol=self.tolerance )
@require_tf
class A ( unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = 1E-4
def snake_case__ ( self : Union[str, Any] )-> str:
'''simple docstring'''
A__ = tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4,dtype=tf.floataa ),shape=(2, 1_2, 1_6, 6_4) ) / 1_0_0
A__ = -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4,dtype=tf.floataa ),shape=(2, 1_2, 1_6, 6_4) ) / 1_0_0
A__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2,embedding_dim=6_4 )
A__ = embed_positions([2, 1_6, 7_6_8] )[None, None, :, :]
A__ , A__ = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
lowercase_,lowercase_,lowercase_ )
A__ = tf.constant(
[
[0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
[-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
[-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
[-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
[0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
[3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
] )
A__ = tf.constant(
[
[0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
[0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
[1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
[2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
[-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
[-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8],lowercase_,atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8],lowercase_,atol=self.tolerance )
| 586
| 0
|
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class _a ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase_ ( self ):
'''simple docstring'''
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(A ):
SCREAMING_SNAKE_CASE : Dict = AutoConfig.from_pretrained(A )
self.assertIsNotNone(A )
self.assertIsInstance(A, A )
SCREAMING_SNAKE_CASE : Tuple = FlaxAutoModel.from_pretrained(A )
self.assertIsNotNone(A )
self.assertIsInstance(A, A )
@slow
def UpperCamelCase_ ( self ):
'''simple docstring'''
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(A ):
SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(A )
self.assertIsNotNone(A )
self.assertIsInstance(A, A )
SCREAMING_SNAKE_CASE : str = FlaxAutoModel.from_pretrained(A )
self.assertIsNotNone(A )
self.assertIsInstance(A, A )
@slow
def UpperCamelCase_ ( self ):
'''simple docstring'''
for model_name in ["bert-base-cased", "bert-large-uncased"]:
SCREAMING_SNAKE_CASE : Optional[Any] = AutoTokenizer.from_pretrained(A )
SCREAMING_SNAKE_CASE : List[str] = FlaxBertModel.from_pretrained(A )
SCREAMING_SNAKE_CASE : List[str] = tokenizer('Do you support jax jitted function?', return_tensors=TensorType.JAX )
@jax.jit
def eval(**A ):
return model(**A )
eval(**A ).block_until_ready()
@slow
def UpperCamelCase_ ( self ):
'''simple docstring'''
for model_name in ["roberta-base", "roberta-large"]:
SCREAMING_SNAKE_CASE : List[str] = AutoTokenizer.from_pretrained(A )
SCREAMING_SNAKE_CASE : int = FlaxRobertaModel.from_pretrained(A )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer('Do you support jax jitted function?', return_tensors=TensorType.JAX )
@jax.jit
def eval(**A ):
return model(**A )
eval(**A ).block_until_ready()
def UpperCamelCase_ ( self ):
'''simple docstring'''
with self.assertRaisesRegex(
A, 'bert-base is not a local folder and is not a valid model identifier' ):
SCREAMING_SNAKE_CASE : Optional[int] = FlaxAutoModel.from_pretrained('bert-base' )
def UpperCamelCase_ ( self ):
'''simple docstring'''
with self.assertRaisesRegex(
A, r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
SCREAMING_SNAKE_CASE : Union[str, Any] = FlaxAutoModel.from_pretrained(A, revision='aaaaaa' )
def UpperCamelCase_ ( self ):
'''simple docstring'''
with self.assertRaisesRegex(
A, 'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack', ):
SCREAMING_SNAKE_CASE : Optional[int] = FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def UpperCamelCase_ ( self ):
'''simple docstring'''
with self.assertRaisesRegex(A, 'Use `from_pt=True` to load this model' ):
SCREAMING_SNAKE_CASE : Union[str, Any] = FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
| 28
|
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__UpperCamelCase = input('''Enter image url: ''').strip()
print(f'''Downloading image from {url} ...''')
__UpperCamelCase = BeautifulSoup(requests.get(url).content, '''html.parser''')
# The image URL is in the content field of the first meta tag with property og:image
__UpperCamelCase = soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
__UpperCamelCase = requests.get(image_url).content
__UpperCamelCase = f'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
with open(file_name, '''wb''') as fp:
fp.write(image_data)
print(f'''Done. Image saved to disk as {file_name}.''')
| 247
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
),
}
class UpperCamelCase__ ( __UpperCAmelCase ):
"""simple docstring"""
A__ : str = "gptsan-japanese"
A__ : Optional[int] = [
"past_key_values",
]
A__ : List[str] = {
"hidden_size": "d_model",
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self , SCREAMING_SNAKE_CASE__=36000 , SCREAMING_SNAKE_CASE__=1280 , SCREAMING_SNAKE_CASE__=1024 , SCREAMING_SNAKE_CASE__=8192 , SCREAMING_SNAKE_CASE__=4096 , SCREAMING_SNAKE_CASE__=128 , SCREAMING_SNAKE_CASE__=10 , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=128 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=1e-5 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__="float32" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=0.0_0_2 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=35998 , SCREAMING_SNAKE_CASE__=35995 , SCREAMING_SNAKE_CASE__=35999 , **SCREAMING_SNAKE_CASE__ , ) -> Union[str, Any]:
A__ = vocab_size
A__ = max_position_embeddings
A__ = d_model
A__ = d_ff
A__ = d_ext
A__ = d_spout
A__ = num_switch_layers
A__ = num_ext_layers
A__ = num_switch_layers + num_ext_layers
A__ = num_heads
A__ = num_experts
A__ = expert_capacity
A__ = dropout_rate
A__ = layer_norm_epsilon
A__ = router_bias
A__ = router_jitter_noise
A__ = router_dtype
A__ = router_ignore_padding_tokens
A__ = output_hidden_states
A__ = output_attentions
A__ = initializer_factor
A__ = output_router_logits
A__ = use_cache
super().__init__(
separator_token_id=lowerCAmelCase_ , pad_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , **lowerCAmelCase_ , )
| 712
|
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
UpperCamelCase = logging.get_logger(__name__)
@add_end_docstrings(_lowerCAmelCase )
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
def __init__( self , **SCREAMING_SNAKE_CASE__ ) -> str:
super().__init__(**SCREAMING_SNAKE_CASE__ )
if self.framework == "tf":
raise ValueError(f"""The {self.__class__} is only available in PyTorch.""" )
requires_backends(self , "vision" )
self.check_model_type(SCREAMING_SNAKE_CASE__ )
def __call__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ) -> Optional[Any]:
if "text_queries" in kwargs:
A__ = kwargs.pop("text_queries" )
if isinstance(SCREAMING_SNAKE_CASE__ , (str, Image.Image) ):
A__ = {"image": image, "candidate_labels": candidate_labels}
else:
A__ = image
A__ = super().__call__(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
return results
def snake_case__ ( self , **SCREAMING_SNAKE_CASE__ ) -> List[str]:
A__ = {}
if "threshold" in kwargs:
A__ = kwargs["threshold"]
if "top_k" in kwargs:
A__ = kwargs["top_k"]
return {}, {}, postprocess_params
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
A__ = load_image(inputs["image"] )
A__ = inputs["candidate_labels"]
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
A__ = candidate_labels.split("," )
A__ = torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(SCREAMING_SNAKE_CASE__ ):
A__ = self.tokenizer(SCREAMING_SNAKE_CASE__ , return_tensors=self.framework )
A__ = self.image_processor(SCREAMING_SNAKE_CASE__ , return_tensors=self.framework )
yield {
"is_last": i == len(SCREAMING_SNAKE_CASE__ ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ ) -> List[str]:
A__ = model_inputs.pop("target_size" )
A__ = model_inputs.pop("candidate_label" )
A__ = model_inputs.pop("is_last" )
A__ = self.model(**SCREAMING_SNAKE_CASE__ )
A__ = {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
return model_outputs
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=None ) -> List[Any]:
A__ = []
for model_output in model_outputs:
A__ = model_output["candidate_label"]
A__ = BaseModelOutput(SCREAMING_SNAKE_CASE__ )
A__ = self.image_processor.post_process_object_detection(
outputs=SCREAMING_SNAKE_CASE__ , threshold=SCREAMING_SNAKE_CASE__ , target_sizes=model_output["target_size"] )[0]
for index in outputs["scores"].nonzero():
A__ = outputs["scores"][index].item()
A__ = self._get_bounding_box(outputs["boxes"][index][0] )
A__ = {"score": score, "label": label, "box": box}
results.append(SCREAMING_SNAKE_CASE__ )
A__ = sorted(SCREAMING_SNAKE_CASE__ , key=lambda SCREAMING_SNAKE_CASE__ : x["score"] , reverse=SCREAMING_SNAKE_CASE__ )
if top_k:
A__ = results[:top_k]
return results
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ ) -> Dict[str, int]:
if self.framework != "pt":
raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch." )
A__ , A__ , A__ , A__ = box.int().tolist()
A__ = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 562
| 0
|
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
# General docstring
lowerCamelCase__ : Dict = 'RegNetConfig'
# Base docstring
lowerCamelCase__ : Union[str, Any] = 'facebook/regnet-y-040'
lowerCamelCase__ : Dict = [1, 1_088, 7, 7]
# Image classification docstring
lowerCamelCase__ : List[Any] = 'facebook/regnet-y-040'
lowerCamelCase__ : Any = 'tabby, tabby cat'
lowerCamelCase__ : List[Any] = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class lowerCamelCase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : int = 3 , _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 1 , _lowerCAmelCase : Optional[str] = "relu" , **_lowerCAmelCase : Tuple , ):
super().__init__(**_lowerCAmelCase )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
SCREAMING_SNAKE_CASE_ = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
SCREAMING_SNAKE_CASE_ = tf.keras.layers.ConvaD(
filters=_lowerCAmelCase , kernel_size=_lowerCAmelCase , strides=_lowerCAmelCase , padding='VALID' , groups=_lowerCAmelCase , use_bias=_lowerCAmelCase , name='convolution' , )
SCREAMING_SNAKE_CASE_ = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='normalization' )
SCREAMING_SNAKE_CASE_ = ACTaFN[activation] if activation is not None else tf.identity
def lowerCAmelCase_ ( self : int , _lowerCAmelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE_ = self.convolution(self.padding(_lowerCAmelCase ) )
SCREAMING_SNAKE_CASE_ = self.normalization(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = self.activation(_lowerCAmelCase )
return hidden_state
class lowerCamelCase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Optional[int] , _lowerCAmelCase : RegNetConfig , **_lowerCAmelCase : List[Any] ):
super().__init__(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = config.num_channels
SCREAMING_SNAKE_CASE_ = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='embedder' , )
def lowerCAmelCase_ ( self : List[str] , _lowerCAmelCase : List[str] ):
SCREAMING_SNAKE_CASE_ = shape_list(_lowerCAmelCase )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
SCREAMING_SNAKE_CASE_ = tf.transpose(_lowerCAmelCase , perm=(0, 2, 3, 1) )
SCREAMING_SNAKE_CASE_ = self.embedder(_lowerCAmelCase )
return hidden_state
class lowerCamelCase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : int , _lowerCAmelCase : int , _lowerCAmelCase : int = 2 , **_lowerCAmelCase : Dict ):
super().__init__(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tf.keras.layers.ConvaD(
filters=_lowerCAmelCase , kernel_size=1 , strides=_lowerCAmelCase , use_bias=_lowerCAmelCase , name='convolution' )
SCREAMING_SNAKE_CASE_ = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='normalization' )
def lowerCAmelCase_ ( self : Union[str, Any] , _lowerCAmelCase : tf.Tensor , _lowerCAmelCase : bool = False ):
return self.normalization(self.convolution(_lowerCAmelCase ) , training=_lowerCAmelCase )
class lowerCamelCase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Union[str, Any] , _lowerCAmelCase : int , _lowerCAmelCase : int , **_lowerCAmelCase : Any ):
super().__init__(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowerCAmelCase , name='pooler' )
SCREAMING_SNAKE_CASE_ = [
tf.keras.layers.ConvaD(filters=_lowerCAmelCase , kernel_size=1 , activation='relu' , name='attention.0' ),
tf.keras.layers.ConvaD(filters=_lowerCAmelCase , kernel_size=1 , activation='sigmoid' , name='attention.2' ),
]
def lowerCAmelCase_ ( self : Union[str, Any] , _lowerCAmelCase : int ):
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
SCREAMING_SNAKE_CASE_ = self.pooler(_lowerCAmelCase )
for layer_module in self.attention:
SCREAMING_SNAKE_CASE_ = layer_module(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = hidden_state * pooled
return hidden_state
class lowerCamelCase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : str , _lowerCAmelCase : RegNetConfig , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int = 1 , **_lowerCAmelCase : str ):
super().__init__(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = in_channels != out_channels or stride != 1
SCREAMING_SNAKE_CASE_ = max(1 , out_channels // config.groups_width )
SCREAMING_SNAKE_CASE_ = (
TFRegNetShortCut(_lowerCAmelCase , stride=_lowerCAmelCase , name='shortcut' )
if should_apply_shortcut
else tf.keras.layers.Activation('linear' , name='shortcut' )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
SCREAMING_SNAKE_CASE_ = [
TFRegNetConvLayer(_lowerCAmelCase , kernel_size=1 , activation=config.hidden_act , name='layer.0' ),
TFRegNetConvLayer(
_lowerCAmelCase , stride=_lowerCAmelCase , groups=_lowerCAmelCase , activation=config.hidden_act , name='layer.1' ),
TFRegNetConvLayer(_lowerCAmelCase , kernel_size=1 , activation=_lowerCAmelCase , name='layer.2' ),
]
SCREAMING_SNAKE_CASE_ = ACTaFN[config.hidden_act]
def lowerCAmelCase_ ( self : Optional[int] , _lowerCAmelCase : Dict ):
SCREAMING_SNAKE_CASE_ = hidden_state
for layer_module in self.layers:
SCREAMING_SNAKE_CASE_ = layer_module(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = self.shortcut(_lowerCAmelCase )
hidden_state += residual
SCREAMING_SNAKE_CASE_ = self.activation(_lowerCAmelCase )
return hidden_state
class lowerCamelCase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[Any] , _lowerCAmelCase : RegNetConfig , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int = 1 , **_lowerCAmelCase : Dict ):
super().__init__(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = in_channels != out_channels or stride != 1
SCREAMING_SNAKE_CASE_ = max(1 , out_channels // config.groups_width )
SCREAMING_SNAKE_CASE_ = (
TFRegNetShortCut(_lowerCAmelCase , stride=_lowerCAmelCase , name='shortcut' )
if should_apply_shortcut
else tf.keras.layers.Activation('linear' , name='shortcut' )
)
SCREAMING_SNAKE_CASE_ = [
TFRegNetConvLayer(_lowerCAmelCase , kernel_size=1 , activation=config.hidden_act , name='layer.0' ),
TFRegNetConvLayer(
_lowerCAmelCase , stride=_lowerCAmelCase , groups=_lowerCAmelCase , activation=config.hidden_act , name='layer.1' ),
TFRegNetSELayer(_lowerCAmelCase , reduced_channels=int(round(in_channels / 4 ) ) , name='layer.2' ),
TFRegNetConvLayer(_lowerCAmelCase , kernel_size=1 , activation=_lowerCAmelCase , name='layer.3' ),
]
SCREAMING_SNAKE_CASE_ = ACTaFN[config.hidden_act]
def lowerCAmelCase_ ( self : Dict , _lowerCAmelCase : Dict ):
SCREAMING_SNAKE_CASE_ = hidden_state
for layer_module in self.layers:
SCREAMING_SNAKE_CASE_ = layer_module(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = self.shortcut(_lowerCAmelCase )
hidden_state += residual
SCREAMING_SNAKE_CASE_ = self.activation(_lowerCAmelCase )
return hidden_state
class lowerCamelCase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : str , _lowerCAmelCase : RegNetConfig , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 2 , **_lowerCAmelCase : Optional[Any] ):
super().__init__(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
SCREAMING_SNAKE_CASE_ = [
# downsampling is done in the first layer with stride of 2
layer(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , stride=_lowerCAmelCase , name='layers.0' ),
*[layer(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , name=F"layers.{i+1}" ) for i in range(depth - 1 )],
]
def lowerCAmelCase_ ( self : List[Any] , _lowerCAmelCase : Optional[int] ):
for layer_module in self.layers:
SCREAMING_SNAKE_CASE_ = layer_module(_lowerCAmelCase )
return hidden_state
class lowerCamelCase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] , _lowerCAmelCase : RegNetConfig , **_lowerCAmelCase : str ):
super().__init__(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
_lowerCAmelCase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='stages.0' , ) )
SCREAMING_SNAKE_CASE_ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(_lowerCAmelCase , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , depth=_lowerCAmelCase , name=F"stages.{i+1}" ) )
def lowerCAmelCase_ ( self : List[str] , _lowerCAmelCase : tf.Tensor , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = True ):
SCREAMING_SNAKE_CASE_ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
SCREAMING_SNAKE_CASE_ = hidden_states + (hidden_state,)
SCREAMING_SNAKE_CASE_ = stage_module(_lowerCAmelCase )
if output_hidden_states:
SCREAMING_SNAKE_CASE_ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=_lowerCAmelCase , hidden_states=_lowerCAmelCase )
@keras_serializable
class lowerCamelCase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
lowercase_ = RegNetConfig
def __init__( self : int , _lowerCAmelCase : Optional[int] , **_lowerCAmelCase : Optional[int] ):
super().__init__(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = config
SCREAMING_SNAKE_CASE_ = TFRegNetEmbeddings(_lowerCAmelCase , name='embedder' )
SCREAMING_SNAKE_CASE_ = TFRegNetEncoder(_lowerCAmelCase , name='encoder' )
SCREAMING_SNAKE_CASE_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowerCAmelCase , name='pooler' )
@unpack_inputs
def lowerCAmelCase_ ( self : List[Any] , _lowerCAmelCase : tf.Tensor , _lowerCAmelCase : Optional[bool] = None , _lowerCAmelCase : Optional[bool] = None , _lowerCAmelCase : bool = False , ):
SCREAMING_SNAKE_CASE_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE_ = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE_ = self.embedder(_lowerCAmelCase , training=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = self.encoder(
_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , return_dict=_lowerCAmelCase , training=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = encoder_outputs[0]
SCREAMING_SNAKE_CASE_ = self.pooler(_lowerCAmelCase )
# Change to NCHW output format have uniformity in the modules
SCREAMING_SNAKE_CASE_ = tf.transpose(_lowerCAmelCase , perm=(0, 3, 1, 2) )
SCREAMING_SNAKE_CASE_ = tf.transpose(_lowerCAmelCase , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
SCREAMING_SNAKE_CASE_ = tuple([tf.transpose(_lowerCAmelCase , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_lowerCAmelCase , pooler_output=_lowerCAmelCase , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = RegNetConfig
lowercase_ = "regnet"
lowercase_ = "pixel_values"
@property
def lowerCAmelCase_ ( self : List[str] ):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) , dtype=tf.floataa )}
lowerCamelCase__ : int = r'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCamelCase__ : Any = r'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." , _SCREAMING_SNAKE_CASE , )
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Dict , _lowerCAmelCase : RegNetConfig , *_lowerCAmelCase : Optional[int] , **_lowerCAmelCase : Tuple ):
super().__init__(_lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = TFRegNetMainLayer(_lowerCAmelCase , name='regnet' )
@unpack_inputs
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_lowerCAmelCase , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowerCAmelCase_ ( self : Union[str, Any] , _lowerCAmelCase : tf.Tensor , _lowerCAmelCase : Optional[bool] = None , _lowerCAmelCase : Optional[bool] = None , _lowerCAmelCase : str=False , ):
SCREAMING_SNAKE_CASE_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE_ = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE_ = self.regnet(
pixel_values=_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , return_dict=_lowerCAmelCase , training=_lowerCAmelCase , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , _SCREAMING_SNAKE_CASE , )
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Tuple , _lowerCAmelCase : RegNetConfig , *_lowerCAmelCase : List[Any] , **_lowerCAmelCase : Optional[int] ):
super().__init__(_lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = config.num_labels
SCREAMING_SNAKE_CASE_ = TFRegNetMainLayer(_lowerCAmelCase , name='regnet' )
# classification head
SCREAMING_SNAKE_CASE_ = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name='classifier.1' ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_lowerCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowerCAmelCase_ ( self : List[Any] , _lowerCAmelCase : tf.Tensor = None , _lowerCAmelCase : tf.Tensor = None , _lowerCAmelCase : bool = None , _lowerCAmelCase : bool = None , _lowerCAmelCase : Dict=False , ):
SCREAMING_SNAKE_CASE_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE_ = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE_ = self.regnet(
_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , return_dict=_lowerCAmelCase , training=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = outputs.pooler_output if return_dict else outputs[1]
SCREAMING_SNAKE_CASE_ = self.classifier[0](_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = self.classifier[1](_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = None if labels is None else self.hf_compute_loss(labels=_lowerCAmelCase , logits=_lowerCAmelCase )
if not return_dict:
SCREAMING_SNAKE_CASE_ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=_lowerCAmelCase , logits=_lowerCAmelCase , hidden_states=outputs.hidden_states )
| 31
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
SCREAMING_SNAKE_CASE__ : Dict = {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
SCREAMING_SNAKE_CASE__ : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 538
| 0
|
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowercase :
"""simple docstring"""
def __init__( self , A , A=2 , A=8 , A=True , A=True , A=True , A=True , A=9_9 , A=1_6 , A=5 , A=2 , A=3_6 , A="gelu" , A=0.0 , A=0.0 , A=5_1_2 , A=1_6 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> List[Any]:
snake_case : Optional[Any] = parent
snake_case : str = batch_size
snake_case : Optional[Any] = seq_length
snake_case : Optional[int] = is_training
snake_case : Dict = use_input_mask
snake_case : Union[str, Any] = use_token_type_ids
snake_case : Tuple = use_labels
snake_case : Any = vocab_size
snake_case : int = hidden_size
snake_case : List[str] = num_hidden_layers
snake_case : Optional[int] = num_attention_heads
snake_case : Any = intermediate_size
snake_case : Union[str, Any] = hidden_act
snake_case : Optional[int] = hidden_dropout_prob
snake_case : Any = attention_probs_dropout_prob
snake_case : List[Any] = max_position_embeddings
snake_case : int = type_vocab_size
snake_case : Dict = type_sequence_label_size
snake_case : Tuple = initializer_range
snake_case : Union[str, Any] = num_labels
snake_case : Optional[Any] = num_choices
snake_case : List[str] = scope
def UpperCAmelCase ( self ) -> str:
snake_case : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case : Optional[Any] = None
if self.use_input_mask:
snake_case : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case : List[str] = None
if self.use_token_type_ids:
snake_case : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case : str = None
snake_case : int = None
snake_case : Optional[int] = None
if self.use_labels:
snake_case : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case : str = ids_tensor([self.batch_size] , self.num_choices )
snake_case : str = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self ) -> List[Any]:
return MraConfig(
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 , )
def UpperCAmelCase ( self ) -> Union[str, Any]:
snake_case : Optional[int] = self.get_config()
snake_case : Tuple = 3_0_0
return config
def UpperCAmelCase ( self ) -> Any:
(
snake_case
) : Union[str, Any] = self.prepare_config_and_inputs()
snake_case : Any = True
snake_case : Dict = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def UpperCAmelCase ( self , A , A , A , A , A , A , A ) -> Any:
snake_case : Tuple = MraModel(config=A )
model.to(A )
model.eval()
snake_case : Tuple = model(A , attention_mask=A , token_type_ids=A )
snake_case : List[str] = model(A , token_type_ids=A )
snake_case : Tuple = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self , A , A , A , A , A , A , A , A , A , ) -> Optional[Any]:
snake_case : List[Any] = True
snake_case : Any = MraModel(A )
model.to(A )
model.eval()
snake_case : str = model(
A , attention_mask=A , token_type_ids=A , encoder_hidden_states=A , encoder_attention_mask=A , )
snake_case : str = model(
A , attention_mask=A , token_type_ids=A , encoder_hidden_states=A , )
snake_case : List[str] = model(A , attention_mask=A , token_type_ids=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self , A , A , A , A , A , A , A ) -> Union[str, Any]:
snake_case : Union[str, Any] = MraForMaskedLM(config=A )
model.to(A )
model.eval()
snake_case : Dict = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self , A , A , A , A , A , A , A ) -> Union[str, Any]:
snake_case : Tuple = MraForQuestionAnswering(config=A )
model.to(A )
model.eval()
snake_case : Any = model(
A , attention_mask=A , token_type_ids=A , start_positions=A , end_positions=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 , A , A , A , A , A , A , A ) -> Any:
snake_case : Tuple = self.num_labels
snake_case : Any = MraForSequenceClassification(A )
model.to(A )
model.eval()
snake_case : List[Any] = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase ( self , A , A , A , A , A , A , A ) -> Tuple:
snake_case : List[str] = self.num_labels
snake_case : str = MraForTokenClassification(config=A )
model.to(A )
model.eval()
snake_case : Optional[int] = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase ( self , A , A , A , A , A , A , A ) -> Dict:
snake_case : List[Any] = self.num_choices
snake_case : str = MraForMultipleChoice(config=A )
model.to(A )
model.eval()
snake_case : Tuple = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case : List[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case : Any = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case : Any = model(
A , attention_mask=A , token_type_ids=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase ( self ) -> List[Any]:
snake_case : Optional[Any] = self.prepare_config_and_inputs()
(
snake_case
) : Dict = config_and_inputs
snake_case : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __lowercase (UpperCamelCase__ , unittest.TestCase ):
"""simple docstring"""
_snake_case = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
_snake_case = False
_snake_case = False
_snake_case = False
_snake_case = False
_snake_case = ()
def UpperCAmelCase ( self ) -> Optional[int]:
snake_case : Union[str, Any] = MraModelTester(self )
snake_case : Optional[Any] = ConfigTester(self , config_class=A , hidden_size=3_7 )
def UpperCAmelCase ( self ) -> Dict:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self ) -> List[Any]:
snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def UpperCAmelCase ( self ) -> Optional[int]:
snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case : Dict = type
self.model_tester.create_and_check_model(*A )
def UpperCAmelCase ( self ) -> Tuple:
snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A )
def UpperCAmelCase ( self ) -> Union[str, Any]:
snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A )
def UpperCAmelCase ( self ) -> Optional[int]:
snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
def UpperCAmelCase ( self ) -> str:
snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def UpperCAmelCase ( self ) -> Optional[int]:
snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A )
@slow
def UpperCAmelCase ( self ) -> Optional[Any]:
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case : Tuple = MraModel.from_pretrained(A )
self.assertIsNotNone(A )
@unittest.skip(reason="""MRA does not output attentions""" )
def UpperCAmelCase ( self ) -> Optional[Any]:
return
@require_torch
class __lowercase (unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase ( self ) -> Dict:
snake_case : List[str] = MraModel.from_pretrained("""uw-madison/mra-base-512-4""" )
snake_case : Optional[Any] = torch.arange(2_5_6 ).unsqueeze(0 )
with torch.no_grad():
snake_case : Optional[int] = model(A )[0]
snake_case : Any = torch.Size((1, 2_5_6, 7_6_8) )
self.assertEqual(output.shape , A )
snake_case : Any = torch.tensor(
[[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A , atol=1e-4 ) )
@slow
def UpperCAmelCase ( self ) -> Optional[int]:
snake_case : Optional[int] = MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4""" )
snake_case : Optional[Any] = torch.arange(2_5_6 ).unsqueeze(0 )
with torch.no_grad():
snake_case : Dict = model(A )[0]
snake_case : List[str] = 5_0_2_6_5
snake_case : Union[str, Any] = torch.Size((1, 2_5_6, vocab_size) )
self.assertEqual(output.shape , A )
snake_case : str = torch.tensor(
[[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A , atol=1e-4 ) )
@slow
def UpperCAmelCase ( self ) -> int:
snake_case : List[Any] = MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3""" )
snake_case : List[Any] = torch.arange(4_0_9_6 ).unsqueeze(0 )
with torch.no_grad():
snake_case : List[str] = model(A )[0]
snake_case : List[str] = 5_0_2_6_5
snake_case : int = torch.Size((1, 4_0_9_6, vocab_size) )
self.assertEqual(output.shape , A )
snake_case : Union[str, Any] = torch.tensor(
[[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A , atol=1e-4 ) )
| 704
|
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
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {'vocab_file': 'spm_char.model'}
lowerCamelCase : List[str] = {
'vocab_file': {
'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
}
}
lowerCamelCase : List[Any] = {
'microsoft/speecht5_asr': 1_0_2_4,
'microsoft/speecht5_tts': 1_0_2_4,
'microsoft/speecht5_vc': 1_0_2_4,
}
class __lowercase (UpperCamelCase__ ):
"""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"""]
def __init__( self , A , A="<s>" , A="</s>" , A="<unk>" , A="<pad>" , A = None , **A , ) -> None:
snake_case : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A , eos_token=A , unk_token=A , pad_token=A , sp_model_kwargs=self.sp_model_kwargs , **A , )
snake_case : Tuple = vocab_file
snake_case : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
@property
def UpperCAmelCase ( self ) -> List[Any]:
return self.sp_model.get_piece_size()
def UpperCAmelCase ( self ) -> Optional[int]:
snake_case : Any = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> List[str]:
snake_case : Optional[Any] = self.__dict__.copy()
snake_case : Optional[Any] = None
return state
def __setstate__( self , A ) -> Tuple:
snake_case : Any = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
snake_case : List[Any] = {}
snake_case : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCAmelCase ( self , A ) -> List[str]:
return self.sp_model.encode(A , out_type=A )
def UpperCAmelCase ( self , A ) -> Tuple:
return self.sp_model.piece_to_id(A )
def UpperCAmelCase ( self , A ) -> int:
snake_case : Union[str, Any] = self.sp_model.IdToPiece(A )
return token
def UpperCAmelCase ( self , A ) -> Tuple:
snake_case : Optional[int] = []
snake_case : str = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A ) + token
snake_case : Dict = []
else:
current_sub_tokens.append(A )
out_string += self.sp_model.decode(A )
return out_string.strip()
def UpperCAmelCase ( self , A , A=None ) -> List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def UpperCAmelCase ( self , A , A = None , A = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A , token_ids_a=A , already_has_special_tokens=A )
snake_case : Any = [1]
if token_ids_a is None:
return ([0] * len(A )) + suffix_ones
return ([0] * len(A )) + ([0] * len(A )) + suffix_ones
def UpperCAmelCase ( self , A , A = None ) -> Tuple[str]:
if not os.path.isdir(A ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case : Optional[Any] = os.path.join(
A , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A )
elif not os.path.isfile(self.vocab_file ):
with open(A , """wb""" ) as fi:
snake_case : Union[str, Any] = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 684
| 0
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
'umberto-commoncrawl-cased-v1': (
'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
),
'umberto-wikipedia-uncased-v1': (
'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
),
}
class __snake_case ( UpperCamelCase_ ):
"""simple docstring"""
lowerCAmelCase_ : List[Any] = 'camembert'
def __init__( self :Union[str, Any] , UpperCamelCase__ :Optional[Any]=30_522 , UpperCamelCase__ :str=768 , UpperCamelCase__ :Tuple=12 , UpperCamelCase__ :str=12 , UpperCamelCase__ :int=3_072 , UpperCamelCase__ :Dict="gelu" , UpperCamelCase__ :Any=0.1 , UpperCamelCase__ :List[str]=0.1 , UpperCamelCase__ :Any=512 , UpperCamelCase__ :Optional[int]=2 , UpperCamelCase__ :Optional[Any]=0.02 , UpperCamelCase__ :List[str]=1E-12 , UpperCamelCase__ :Optional[int]=1 , UpperCamelCase__ :Union[str, Any]=0 , UpperCamelCase__ :str=2 , UpperCamelCase__ :Any="absolute" , UpperCamelCase__ :Optional[Any]=True , UpperCamelCase__ :Dict=None , **UpperCamelCase__ :int , ):
super().__init__(pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase )
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = hidden_act
_a = intermediate_size
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = initializer_range
_a = layer_norm_eps
_a = position_embedding_type
_a = use_cache
_a = classifier_dropout
class __snake_case ( UpperCamelCase_ ):
"""simple docstring"""
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
if self.task == "multiple-choice":
_a = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_a = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 388
|
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowercase__( A ):
if "model" in orig_key:
snake_case__ : Any = orig_key.replace('model.' , '' )
if "norm1" in orig_key:
snake_case__ : Optional[int] = orig_key.replace('norm1' , 'attention.output.LayerNorm' )
if "norm2" in orig_key:
snake_case__ : Tuple = orig_key.replace('norm2' , 'output.LayerNorm' )
if "norm" in orig_key:
snake_case__ : List[Any] = orig_key.replace('norm' , 'LayerNorm' )
if "transformer" in orig_key:
snake_case__ : Tuple = orig_key.split('.' )[0].split('_' )[-1]
snake_case__ : Optional[Any] = orig_key.replace(f'''transformer_{layer_num}''' , f'''encoder.layer.{layer_num}''' )
if "mha.attn" in orig_key:
snake_case__ : Union[str, Any] = orig_key.replace('mha.attn' , 'attention.self' )
if "mha" in orig_key:
snake_case__ : Optional[Any] = orig_key.replace('mha' , 'attention' )
if "W_q" in orig_key:
snake_case__ : Optional[int] = orig_key.replace('W_q' , 'self.query' )
if "W_k" in orig_key:
snake_case__ : List[Any] = orig_key.replace('W_k' , 'self.key' )
if "W_v" in orig_key:
snake_case__ : str = orig_key.replace('W_v' , 'self.value' )
if "ff1" in orig_key:
snake_case__ : int = orig_key.replace('ff1' , 'intermediate.dense' )
if "ff2" in orig_key:
snake_case__ : str = orig_key.replace('ff2' , 'output.dense' )
if "ff" in orig_key:
snake_case__ : Union[str, Any] = orig_key.replace('ff' , 'output.dense' )
if "mlm_class" in orig_key:
snake_case__ : int = orig_key.replace('mlm.mlm_class' , 'cls.predictions.decoder' )
if "mlm" in orig_key:
snake_case__ : Optional[int] = orig_key.replace('mlm' , 'cls.predictions.transform' )
if "cls" not in orig_key:
snake_case__ : Optional[int] = 'yoso.' + orig_key
return orig_key
def lowercase__( A , A ):
for key in orig_state_dict.copy().keys():
snake_case__ : Optional[Any] = orig_state_dict.pop(A )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
snake_case__ : Optional[Any] = val
snake_case__ : Tuple = orig_state_dict['cls.predictions.decoder.bias']
snake_case__ : Optional[Any] = torch.arange(A ).expand((1, -1) ) + 2
return orig_state_dict
def lowercase__( A , A , A ):
snake_case__ : Tuple = torch.load(A , map_location='cpu' )['model_state_dict']
snake_case__ : Union[str, Any] = YosoConfig.from_json_file(A )
snake_case__ : Optional[int] = YosoForMaskedLM(A )
snake_case__ : str = convert_checkpoint_helper(config.max_position_embeddings , A )
print(model.load_state_dict(A ) )
model.eval()
model.save_pretrained(A )
print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
if __name__ == "__main__":
lowerCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The json file for YOSO model config.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
lowerCamelCase : Optional[Any] = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 170
| 0
|
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
_lowerCamelCase = """<<<<<<< This should probably be modified because it mentions: """
_lowerCamelCase = """=======
>>>>>>>
"""
_lowerCamelCase = [
"""TextEncoderConfig""",
"""ByteTextEncoder""",
"""SubwordTextEncoder""",
"""encoder_config""",
"""maybe_build_from_corpus""",
"""manual_dir""",
]
_lowerCamelCase = [
# (pattern, replacement)
# Order is important here for some replacements
(r"""tfds\.core""", r"""datasets"""),
(r"""tf\.io\.gfile\.GFile""", r"""open"""),
(r"""tf\.([\w\d]+)""", r"""datasets.Value(\'\1\')"""),
(r"""tfds\.features\.Text\(\)""", r"""datasets.Value(\'string\')"""),
(r"""tfds\.features\.Text\(""", r"""datasets.Value(\'string\'),"""),
(r"""features\s*=\s*tfds.features.FeaturesDict\(""", r"""features=datasets.Features("""),
(r"""tfds\.features\.FeaturesDict\(""", r"""dict("""),
(r"""The TensorFlow Datasets Authors""", r"""The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"""),
(r"""tfds\.""", r"""datasets."""),
(r"""dl_manager\.manual_dir""", r"""self.config.data_dir"""),
(r"""self\.builder_config""", r"""self.config"""),
]
def _lowerCAmelCase ( __lowerCamelCase : Dict ):
"""simple docstring"""
return ConvertCommand(args.tfds_path , args.datasets_directory )
class _SCREAMING_SNAKE_CASE (__a ):
@staticmethod
def __snake_case ( UpperCamelCase : Tuple )->Dict:
__SCREAMING_SNAKE_CASE : str = parser.add_parser(
"convert" , help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset." , )
train_parser.add_argument(
"--tfds_path" , type=lowerCAmelCase_ , required=lowerCAmelCase_ , help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert." , )
train_parser.add_argument(
"--datasets_directory" , type=lowerCAmelCase_ , required=lowerCAmelCase_ , help="Path to the HuggingFace Datasets folder." )
train_parser.set_defaults(func=lowerCAmelCase_ )
def __init__( self : Dict , UpperCamelCase : Union[str, Any] , UpperCamelCase : Dict , *UpperCamelCase : Union[str, Any] )->Union[str, Any]:
__SCREAMING_SNAKE_CASE : List[str] = get_logger("datasets-cli/converting" )
__SCREAMING_SNAKE_CASE : List[Any] = tfds_path
__SCREAMING_SNAKE_CASE : List[str] = datasets_directory
def __snake_case ( self : Union[str, Any] )->List[str]:
if os.path.isdir(self._tfds_path ):
__SCREAMING_SNAKE_CASE : Tuple = os.path.abspath(self._tfds_path )
elif os.path.isfile(self._tfds_path ):
__SCREAMING_SNAKE_CASE : Optional[int] = os.path.dirname(self._tfds_path )
else:
raise ValueError("--tfds_path is neither a directory nor a file. Please check path." )
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.abspath(self._datasets_directory )
self._logger.info(F"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}""" )
__SCREAMING_SNAKE_CASE : List[str] = []
__SCREAMING_SNAKE_CASE : Optional[Any] = []
__SCREAMING_SNAKE_CASE : Optional[Any] = {}
if os.path.isdir(self._tfds_path ):
__SCREAMING_SNAKE_CASE : Any = os.listdir(lowerCAmelCase_ )
else:
__SCREAMING_SNAKE_CASE : Optional[int] = [os.path.basename(self._tfds_path )]
for f_name in file_names:
self._logger.info(F"""Looking at file {f_name}""" )
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE : int = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
if not os.path.isfile(lowerCAmelCase_ ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info("Skipping file" )
continue
with open(lowerCAmelCase_ , encoding="utf-8" ) as f:
__SCREAMING_SNAKE_CASE : Optional[int] = f.readlines()
__SCREAMING_SNAKE_CASE : Dict = []
__SCREAMING_SNAKE_CASE : Any = False
__SCREAMING_SNAKE_CASE : str = False
__SCREAMING_SNAKE_CASE : Any = []
for line in lines:
__SCREAMING_SNAKE_CASE : int = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
__SCREAMING_SNAKE_CASE : Optional[Any] = "import datasets\n"
elif "import tensorflow" in out_line:
# order is important here
__SCREAMING_SNAKE_CASE : List[Any] = ""
continue
elif "from absl import logging" in out_line:
__SCREAMING_SNAKE_CASE : List[Any] = "from datasets import logging\n"
elif "getLogger" in out_line:
__SCREAMING_SNAKE_CASE : int = out_line.replace("getLogger" , "get_logger" )
elif any(expression in out_line for expression in TO_HIGHLIGHT ):
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : Optional[int] = list(filter(lambda UpperCamelCase : e in out_line , lowerCAmelCase_ ) )
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(lowerCAmelCase_ ) + "\n" )
out_lines.append(lowerCAmelCase_ )
out_lines.append(lowerCAmelCase_ )
continue
else:
for pattern, replacement in TO_CONVERT:
__SCREAMING_SNAKE_CASE : Any = re.sub(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
__SCREAMING_SNAKE_CASE : str = re.match(r"from\stensorflow_datasets.*import\s([^\.\r\n]+)" , lowerCAmelCase_ )
tfds_imports.extend(imp.strip() for imp in match.group(1 ).split("," ) )
__SCREAMING_SNAKE_CASE : Tuple = "from . import " + match.group(1 )
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(F"""Error converting {out_line.strip()}""" )
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
__SCREAMING_SNAKE_CASE : int = True
out_lines.append(lowerCAmelCase_ )
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
__SCREAMING_SNAKE_CASE : List[Any] = f_name.replace(".py" , "" )
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE : Dict = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
self._logger.info(F"""Adding directory {output_dir}""" )
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
else:
# Utilities will be moved at the end
utils_files.append(lowerCAmelCase_ )
if needs_manual_update:
with_manual_update.append(lowerCAmelCase_ )
with open(lowerCAmelCase_ , "w" , encoding="utf-8" ) as f:
f.writelines(lowerCAmelCase_ )
self._logger.info(F"""Converted in {output_file}""" )
for utils_file in utils_files:
try:
__SCREAMING_SNAKE_CASE : List[Any] = os.path.basename(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = imports_to_builder_map[f_name.replace(".py" , "" )]
self._logger.info(F"""Moving {dest_folder} to {utils_file}""" )
shutil.copy(lowerCAmelCase_ , lowerCAmelCase_ )
except KeyError:
self._logger.error(F"""Cannot find destination folder for {utils_file}. Please copy manually.""" )
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
F"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'.""" )
| 719
|
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
_lowerCamelCase = False
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = """ybelkada/fonts"""
def _lowerCAmelCase ( ):
"""simple docstring"""
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
F"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
"Pix2StructImageProcessor. Please upgrade torch." )
def _lowerCAmelCase ( __lowerCamelCase : List[str] , __lowerCamelCase : int , __lowerCamelCase : Dict ):
"""simple docstring"""
requires_backends(__lowerCamelCase , ["torch"] )
_check_torch_version()
__SCREAMING_SNAKE_CASE : List[Any] = image_tensor.unsqueeze(0 )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.nn.functional.unfold(__lowerCamelCase , (patch_height, patch_width) , stride=(patch_height, patch_width) )
__SCREAMING_SNAKE_CASE : List[Any] = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , __lowerCamelCase , __lowerCamelCase , -1 )
__SCREAMING_SNAKE_CASE : Any = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def _lowerCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : int = 36 , __lowerCamelCase : str = "black" , __lowerCamelCase : str = "white" , __lowerCamelCase : int = 5 , __lowerCamelCase : int = 5 , __lowerCamelCase : int = 5 , __lowerCamelCase : int = 5 , __lowerCamelCase : Optional[bytes] = None , __lowerCamelCase : Optional[str] = None , ):
"""simple docstring"""
requires_backends(__lowerCamelCase , "vision" )
# Add new lines so that each line is no more than 80 characters.
__SCREAMING_SNAKE_CASE : Union[str, Any] = textwrap.TextWrapper(width=80 )
__SCREAMING_SNAKE_CASE : Optional[Any] = wrapper.wrap(text=__lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = "\n".join(__lowerCamelCase )
if font_bytes is not None and font_path is None:
__SCREAMING_SNAKE_CASE : List[Any] = io.BytesIO(__lowerCamelCase )
elif font_path is not None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = font_path
else:
__SCREAMING_SNAKE_CASE : Tuple = hf_hub_download(__lowerCamelCase , "Arial.TTF" )
__SCREAMING_SNAKE_CASE : List[Any] = ImageFont.truetype(__lowerCamelCase , encoding="UTF-8" , size=__lowerCamelCase )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
__SCREAMING_SNAKE_CASE : str = ImageDraw.Draw(Image.new("RGB" , (1, 1) , __lowerCamelCase ) )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = temp_draw.textbbox((0, 0) , __lowerCamelCase , __lowerCamelCase )
# Create the actual image with a bit of padding around the text.
__SCREAMING_SNAKE_CASE : Union[str, Any] = text_width + left_padding + right_padding
__SCREAMING_SNAKE_CASE : Tuple = text_height + top_padding + bottom_padding
__SCREAMING_SNAKE_CASE : Tuple = Image.new("RGB" , (image_width, image_height) , __lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = ImageDraw.Draw(__lowerCamelCase )
draw.text(xy=(left_padding, top_padding) , text=__lowerCamelCase , fill=__lowerCamelCase , font=__lowerCamelCase )
return image
def _lowerCAmelCase ( __lowerCamelCase : np.ndarray , __lowerCamelCase : str , **__lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
requires_backends(__lowerCamelCase , "vision" )
# Convert to PIL image if necessary
__SCREAMING_SNAKE_CASE : int = to_pil_image(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = render_text(__lowerCamelCase , **__lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = max(header_image.width , image.width )
__SCREAMING_SNAKE_CASE : Tuple = int(image.height * (new_width / image.width) )
__SCREAMING_SNAKE_CASE : Any = int(header_image.height * (new_width / header_image.width) )
__SCREAMING_SNAKE_CASE : Tuple = Image.new("RGB" , (new_width, new_height + new_header_height) , "white" )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
__SCREAMING_SNAKE_CASE : Dict = to_numpy_array(__lowerCamelCase )
if infer_channel_dimension_format(__lowerCamelCase ) == ChannelDimension.LAST:
__SCREAMING_SNAKE_CASE : Any = to_channel_dimension_format(__lowerCamelCase , ChannelDimension.LAST )
return new_image
class _SCREAMING_SNAKE_CASE (UpperCamelCase ):
lowerCAmelCase = ["""flattened_patches"""]
def __init__( self : Tuple , UpperCamelCase : bool = True , UpperCamelCase : bool = True , UpperCamelCase : Dict[str, int] = None , UpperCamelCase : int = 2_0_4_8 , UpperCamelCase : bool = False , **UpperCamelCase : Optional[int] , )->None:
super().__init__(**UpperCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
__SCREAMING_SNAKE_CASE : List[Any] = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = do_convert_rgb
__SCREAMING_SNAKE_CASE : List[str] = max_patches
__SCREAMING_SNAKE_CASE : List[Any] = is_vqa
def __snake_case ( self : Tuple , UpperCamelCase : np.ndarray , UpperCamelCase : int , UpperCamelCase : dict , **UpperCamelCase : Tuple )->np.ndarray:
requires_backends(self.extract_flattened_patches , "torch" )
_check_torch_version()
# convert to torch
__SCREAMING_SNAKE_CASE : Optional[Any] = to_channel_dimension_format(UpperCamelCase , ChannelDimension.FIRST )
__SCREAMING_SNAKE_CASE : int = torch.from_numpy(UpperCamelCase )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : List[str] = patch_size["height"], patch_size["width"]
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Optional[Any] = get_image_size(UpperCamelCase )
# maximize scale s.t.
__SCREAMING_SNAKE_CASE : List[str] = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
__SCREAMING_SNAKE_CASE : List[str] = max(min(math.floor(scale * image_height / patch_height ) , UpperCamelCase ) , 1 )
__SCREAMING_SNAKE_CASE : Tuple = max(min(math.floor(scale * image_width / patch_width ) , UpperCamelCase ) , 1 )
__SCREAMING_SNAKE_CASE : List[str] = max(num_feasible_rows * patch_height , 1 )
__SCREAMING_SNAKE_CASE : int = max(num_feasible_cols * patch_width , 1 )
__SCREAMING_SNAKE_CASE : Any = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode="bilinear" , align_corners=UpperCamelCase , antialias=UpperCamelCase , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
__SCREAMING_SNAKE_CASE : List[str] = torch_extract_patches(UpperCamelCase , UpperCamelCase , UpperCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = patches.shape
__SCREAMING_SNAKE_CASE : int = patches_shape[1]
__SCREAMING_SNAKE_CASE : List[str] = patches_shape[2]
__SCREAMING_SNAKE_CASE : List[str] = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
__SCREAMING_SNAKE_CASE : Union[str, Any] = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
__SCREAMING_SNAKE_CASE : Any = torch.arange(UpperCamelCase ).reshape([rows, 1] ).repeat(1 , UpperCamelCase ).reshape([rows * columns, 1] )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.arange(UpperCamelCase ).reshape([1, columns] ).repeat(UpperCamelCase , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
__SCREAMING_SNAKE_CASE : str = row_ids.to(torch.floataa )
__SCREAMING_SNAKE_CASE : int = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
__SCREAMING_SNAKE_CASE : Optional[int] = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
__SCREAMING_SNAKE_CASE : Any = torch.nn.functional.pad(UpperCamelCase , [0, 0, 0, max_patches - (rows * columns)] ).float()
__SCREAMING_SNAKE_CASE : str = to_numpy_array(UpperCamelCase )
return result
def __snake_case ( self : Optional[Any] , UpperCamelCase : np.ndarray , UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase : Optional[int] )->np.ndarray:
if image.dtype == np.uinta:
__SCREAMING_SNAKE_CASE : Optional[int] = image.astype(np.floataa )
# take mean across the whole `image`
__SCREAMING_SNAKE_CASE : int = np.mean(UpperCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = np.std(UpperCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = max(UpperCamelCase , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(UpperCamelCase , mean=UpperCamelCase , std=UpperCamelCase , **UpperCamelCase )
def __snake_case ( self : Union[str, Any] , UpperCamelCase : ImageInput , UpperCamelCase : Optional[str] = None , UpperCamelCase : bool = None , UpperCamelCase : Optional[bool] = None , UpperCamelCase : Optional[int] = None , UpperCamelCase : Optional[Dict[str, int]] = None , UpperCamelCase : Optional[Union[str, TensorType]] = None , UpperCamelCase : ChannelDimension = ChannelDimension.FIRST , **UpperCamelCase : Dict , )->ImageInput:
__SCREAMING_SNAKE_CASE : int = do_normalize if do_normalize is not None else self.do_normalize
__SCREAMING_SNAKE_CASE : Optional[Any] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__SCREAMING_SNAKE_CASE : List[Any] = patch_size if patch_size is not None else self.patch_size
__SCREAMING_SNAKE_CASE : List[str] = max_patches if max_patches is not None else self.max_patches
__SCREAMING_SNAKE_CASE : List[Any] = self.is_vqa
if kwargs.get("data_format" , UpperCamelCase ) is not None:
raise ValueError("data_format is not an accepted input as the outputs are " )
__SCREAMING_SNAKE_CASE : Union[str, Any] = make_list_of_images(UpperCamelCase )
if not valid_images(UpperCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__SCREAMING_SNAKE_CASE : Optional[int] = [convert_to_rgb(UpperCamelCase ) for image in images]
# All transformations expect numpy arrays.
__SCREAMING_SNAKE_CASE : str = [to_numpy_array(UpperCamelCase ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError("A header text must be provided for VQA models." )
__SCREAMING_SNAKE_CASE : Any = kwargs.pop("font_bytes" , UpperCamelCase )
__SCREAMING_SNAKE_CASE : Any = kwargs.pop("font_path" , UpperCamelCase )
if isinstance(UpperCamelCase , UpperCamelCase ):
__SCREAMING_SNAKE_CASE : Dict = [header_text] * len(UpperCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = [
render_header(UpperCamelCase , header_text[i] , font_bytes=UpperCamelCase , font_path=UpperCamelCase )
for i, image in enumerate(UpperCamelCase )
]
if do_normalize:
__SCREAMING_SNAKE_CASE : List[str] = [self.normalize(image=UpperCamelCase ) for image in images]
# convert to torch tensor and permute
__SCREAMING_SNAKE_CASE : str = [
self.extract_flattened_patches(image=UpperCamelCase , max_patches=UpperCamelCase , patch_size=UpperCamelCase )
for image in images
]
# create attention mask in numpy
__SCREAMING_SNAKE_CASE : List[Any] = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
__SCREAMING_SNAKE_CASE : Dict = BatchFeature(
data={"flattened_patches": images, "attention_mask": attention_masks} , tensor_type=UpperCamelCase )
return encoded_outputs
| 447
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|
from __future__ import annotations
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None ) -> None:
if start is None:
lowercase : Optional[int] = 0
if end is None:
lowercase : List[str] = len(snake_case__ ) - 1
if start >= end:
return
lowercase : Any = (start + end) // 2
slowsort(snake_case__ , snake_case__ , snake_case__ )
slowsort(snake_case__ , mid + 1 , snake_case__ )
if sequence[end] < sequence[mid]:
lowercase , lowercase : List[Any] = sequence[mid], sequence[end]
slowsort(snake_case__ , snake_case__ , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 336
|
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = ['''image_processor''', '''tokenizer''']
__UpperCAmelCase = '''ViltImageProcessor'''
__UpperCAmelCase = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self , lowercase_=None , lowercase_=None , **lowercase_) -> List[Any]:
__snake_case = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , lowercase_ , )
__snake_case = kwargs.pop('feature_extractor')
__snake_case = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.')
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.')
super().__init__(lowercase_ , lowercase_)
__snake_case = self.image_processor
def __call__( self , lowercase_ , lowercase_ = None , lowercase_ = True , lowercase_ = False , lowercase_ = None , lowercase_ = None , lowercase_ = 0 , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = False , lowercase_ = False , lowercase_ = False , lowercase_ = False , lowercase_ = True , lowercase_ = None , **lowercase_ , ) -> BatchEncoding:
__snake_case = self.tokenizer(
text=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_token_type_ids=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , )
# add pixel_values + pixel_mask
__snake_case = self.image_processor(lowercase_ , return_tensors=lowercase_)
encoding.update(lowercase_)
return encoding
def _a ( self , *lowercase_ , **lowercase_) -> Optional[Any]:
return self.tokenizer.batch_decode(*lowercase_ , **lowercase_)
def _a ( self , *lowercase_ , **lowercase_) -> Dict:
return self.tokenizer.decode(*lowercase_ , **lowercase_)
@property
def _a ( self) -> Tuple:
__snake_case = self.tokenizer.model_input_names
__snake_case = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
@property
def _a ( self) -> Optional[int]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , lowercase_ , )
return self.image_processor_class
@property
def _a ( self) -> List[str]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , lowercase_ , )
return self.image_processor
| 313
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|
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
_UpperCamelCase : List[Any] = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def a_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict ):
'''simple docstring'''
return max(metric_fn(_lowerCAmelCase , _lowerCAmelCase ) for gt in ground_truths )
def a_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : Any = [line.strip() for line in open(_lowerCAmelCase , 'r' ).readlines()]
lowercase__ : Optional[int] = []
if args.gold_data_mode == "qa":
lowercase__ : Any = pd.read_csv(_lowerCAmelCase , sep='\t' , header=_lowerCAmelCase )
for answer_list in data[1]:
lowercase__ : str = ast.literal_eval(_lowerCAmelCase )
answers.append(_lowerCAmelCase )
else:
lowercase__ : List[Any] = [line.strip() for line in open(_lowerCAmelCase , 'r' ).readlines()]
lowercase__ : int = [[reference] for reference in references]
lowercase__ : Tuple = 0
for prediction, ground_truths in zip(_lowerCAmelCase , _lowerCAmelCase ):
total += 1
em += metric_max_over_ground_truths(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
fa += metric_max_over_ground_truths(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowercase__ : str = 1_0_0.0 * em / total
lowercase__ : str = 1_0_0.0 * fa / total
logger.info(f"""F1: {fa:.2f}""" )
logger.info(f"""EM: {em:.2f}""" )
def a_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple ):
'''simple docstring'''
lowercase__ : Optional[Any] = args.k
lowercase__ : Optional[int] = [line.strip() for line in open(_lowerCAmelCase , 'r' ).readlines()]
lowercase__ : List[str] = [line.strip() for line in open(_lowerCAmelCase , 'r' ).readlines()]
lowercase__ : List[Any] = 0
for hypo, reference in zip(_lowerCAmelCase , _lowerCAmelCase ):
lowercase__ : Union[str, Any] = set(hypo.split('\t' )[:k] )
lowercase__ : Tuple = set(reference.split('\t' ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
lowercase__ : int = 1_0_0.0 * em / total
logger.info(f"""Precision@{k}: {em: .2f}""" )
def a_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
def strip_title(_lowerCAmelCase : List[Any] ):
if title.startswith('"' ):
lowercase__ : int = title[1:]
if title.endswith('"' ):
lowercase__ : Optional[int] = title[:-1]
return title
lowercase__ : List[Any] = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
_lowerCAmelCase , return_tensors='pt' , padding=_lowerCAmelCase , truncation=_lowerCAmelCase , )['input_ids'].to(args.device )
lowercase__ : Optional[Any] = rag_model.rag.question_encoder(_lowerCAmelCase )
lowercase__ : Optional[Any] = question_enc_outputs[0]
lowercase__ : str = rag_model.retriever(
_lowerCAmelCase , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors='pt' , )
lowercase__ : List[str] = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
lowercase__ : str = []
for docs in all_docs:
lowercase__ : Tuple = [strip_title(_lowerCAmelCase ) for title in docs['title']]
provenance_strings.append('\t'.join(_lowerCAmelCase ) )
return provenance_strings
def a_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
with torch.no_grad():
lowercase__ : Optional[int] = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
_lowerCAmelCase , return_tensors='pt' , padding=_lowerCAmelCase , truncation=_lowerCAmelCase )
lowercase__ : int = inputs_dict.input_ids.to(args.device )
lowercase__ : int = inputs_dict.attention_mask.to(args.device )
lowercase__ : Any = rag_model.generate( # rag_model overwrites generate
_lowerCAmelCase , attention_mask=_lowerCAmelCase , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=_lowerCAmelCase , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
lowercase__ : List[str] = rag_model.retriever.generator_tokenizer.batch_decode(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase )
if args.print_predictions:
for q, a in zip(_lowerCAmelCase , _lowerCAmelCase ):
logger.info('Q: {} - A: {}'.format(_lowerCAmelCase , _lowerCAmelCase ) )
return answers
def a_ ( ):
'''simple docstring'''
lowercase__ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'--model_type' , choices=['rag_sequence', 'rag_token', 'bart'] , type=_lowerCAmelCase , help=(
'RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'
' model_name_or_path'
) , )
parser.add_argument(
'--index_name' , default=_lowerCAmelCase , choices=['exact', 'compressed', 'legacy'] , type=_lowerCAmelCase , help='RAG model retriever type' , )
parser.add_argument(
'--index_path' , default=_lowerCAmelCase , type=_lowerCAmelCase , help='Path to the retrieval index' , )
parser.add_argument('--n_docs' , default=5 , type=_lowerCAmelCase , help='Number of retrieved docs' )
parser.add_argument(
'--model_name_or_path' , default=_lowerCAmelCase , type=_lowerCAmelCase , required=_lowerCAmelCase , help='Path to pretrained checkpoints or model identifier from huggingface.co/models' , )
parser.add_argument(
'--eval_mode' , choices=['e2e', 'retrieval'] , default='e2e' , type=_lowerCAmelCase , help=(
'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
' precision@k.'
) , )
parser.add_argument('--k' , default=1 , type=_lowerCAmelCase , help='k for the precision@k calculation' )
parser.add_argument(
'--evaluation_set' , default=_lowerCAmelCase , type=_lowerCAmelCase , required=_lowerCAmelCase , help='Path to a file containing evaluation samples' , )
parser.add_argument(
'--gold_data_path' , default=_lowerCAmelCase , type=_lowerCAmelCase , required=_lowerCAmelCase , help='Path to a tab-separated file with gold samples' , )
parser.add_argument(
'--gold_data_mode' , default='qa' , type=_lowerCAmelCase , choices=['qa', 'ans'] , help=(
'Format of the gold data file'
'qa - a single line in the following format: question [tab] answer_list'
'ans - a single line of the gold file contains the expected answer string'
) , )
parser.add_argument(
'--predictions_path' , type=_lowerCAmelCase , default='predictions.txt' , help='Name of the predictions file, to be stored in the checkpoints directory' , )
parser.add_argument(
'--eval_all_checkpoints' , action='store_true' , help='Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number' , )
parser.add_argument(
'--eval_batch_size' , default=8 , type=_lowerCAmelCase , help='Batch size per GPU/CPU for evaluation.' , )
parser.add_argument(
'--recalculate' , help='Recalculate predictions even if the prediction file exists' , action='store_true' , )
parser.add_argument(
'--num_beams' , default=4 , type=_lowerCAmelCase , help='Number of beams to be used when generating answers' , )
parser.add_argument('--min_length' , default=1 , type=_lowerCAmelCase , help='Min length of the generated answers' )
parser.add_argument('--max_length' , default=50 , type=_lowerCAmelCase , help='Max length of the generated answers' )
parser.add_argument(
'--print_predictions' , action='store_true' , help='If True, prints predictions while evaluating.' , )
parser.add_argument(
'--print_docs' , action='store_true' , help='If True, prints docs retried while generating.' , )
lowercase__ : Optional[int] = parser.parse_args()
lowercase__ : Tuple = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
return args
def a_ ( _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : int = {}
if args.model_type is None:
lowercase__ : Union[str, Any] = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith('rag' ):
lowercase__ : Tuple = RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
lowercase__ : Tuple = args.n_docs
if args.index_name is not None:
lowercase__ : Any = args.index_name
if args.index_path is not None:
lowercase__ : Tuple = args.index_path
else:
lowercase__ : Tuple = BartForConditionalGeneration
lowercase__ : Optional[Any] = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info('Evaluate the following checkpoints: %s' , _lowerCAmelCase )
lowercase__ : Dict = get_scores if args.eval_mode == 'e2e' else get_precision_at_k
lowercase__ : Union[str, Any] = evaluate_batch_eae if args.eval_mode == 'e2e' else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info('Calculating metrics based on an existing predictions file: {}'.format(args.predictions_path ) )
score_fn(_lowerCAmelCase , args.predictions_path , args.gold_data_path )
continue
logger.info('***** Running evaluation for {} *****'.format(_lowerCAmelCase ) )
logger.info(' Batch size = %d' , args.eval_batch_size )
logger.info(' Predictions will be stored under {}'.format(args.predictions_path ) )
if args.model_type.startswith('rag' ):
lowercase__ : Tuple = RagRetriever.from_pretrained(_lowerCAmelCase , **_lowerCAmelCase )
lowercase__ : List[str] = model_class.from_pretrained(_lowerCAmelCase , retriever=_lowerCAmelCase , **_lowerCAmelCase )
model.retriever.init_retrieval()
else:
lowercase__ : Optional[int] = model_class.from_pretrained(_lowerCAmelCase , **_lowerCAmelCase )
model.to(args.device )
with open(args.evaluation_set , 'r' ) as eval_file, open(args.predictions_path , 'w' ) as preds_file:
lowercase__ : Dict = []
for line in tqdm(_lowerCAmelCase ):
questions.append(line.strip() )
if len(_lowerCAmelCase ) == args.eval_batch_size:
lowercase__ : List[str] = evaluate_batch_fn(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
preds_file.write('\n'.join(_lowerCAmelCase ) + '\n' )
preds_file.flush()
lowercase__ : Union[str, Any] = []
if len(_lowerCAmelCase ) > 0:
lowercase__ : Union[str, Any] = evaluate_batch_fn(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
preds_file.write('\n'.join(_lowerCAmelCase ) )
preds_file.flush()
score_fn(_lowerCAmelCase , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
_UpperCamelCase : Any = get_args()
main(args)
| 645
|
"""simple docstring"""
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_UpperCamelCase : int = 16
_UpperCamelCase : Union[str, Any] = 32
def a_ ( _lowerCAmelCase : Tuple ):
'''simple docstring'''
return int(x / 2**20 )
class UpperCAmelCase_ :
def __enter__( self ) -> Union[str, Any]:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
lowercase__ : List[str] = torch.cuda.memory_allocated()
return self
def __exit__( self , *a ) -> Any:
gc.collect()
torch.cuda.empty_cache()
lowercase__ : Optional[Any] = torch.cuda.memory_allocated()
lowercase__ : Union[str, Any] = torch.cuda.max_memory_allocated()
lowercase__ : List[Any] = bamb(self.end - self.begin )
lowercase__ : List[Any] = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def a_ ( _lowerCAmelCase : Accelerator , _lowerCAmelCase : int = 16 , _lowerCAmelCase : str = "bert-base-cased" , _lowerCAmelCase : int = 320 , _lowerCAmelCase : int = 160 , ):
'''simple docstring'''
lowercase__ : List[Any] = AutoTokenizer.from_pretrained(_lowerCAmelCase )
lowercase__ : Union[str, Any] = load_dataset(
'glue' , 'mrpc' , split={'train': f"""train[:{n_train}]""", 'validation': f"""validation[:{n_val}]"""} )
def tokenize_function(_lowerCAmelCase : int ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : List[str] = 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
lowercase__ : Union[str, Any] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=_lowerCAmelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : Union[str, Any] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_lowerCAmelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_lowerCAmelCase , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(_lowerCAmelCase , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
lowercase__ : Dict = DataLoader(
tokenized_datasets['train'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
lowercase__ : Dict = DataLoader(
tokenized_datasets['validation'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
return train_dataloader, eval_dataloader
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : List[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : Optional[int] = config['lr']
lowercase__ : Optional[Any] = int(config['num_epochs'] )
lowercase__ : Optional[Any] = int(config['seed'] )
lowercase__ : int = int(config['batch_size'] )
lowercase__ : Union[str, Any] = args.model_name_or_path
set_seed(_lowerCAmelCase )
lowercase__ , lowercase__ : Tuple = get_dataloaders(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : List[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase , return_dict=_lowerCAmelCase )
# Instantiate optimizer
lowercase__ : List[Any] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowercase__ : Optional[Any] = optimizer_cls(params=model.parameters() , lr=_lowerCAmelCase )
if accelerator.state.deepspeed_plugin is not None:
lowercase__ : Optional[Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
lowercase__ : List[Any] = 1
lowercase__ : List[Any] = (len(_lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowercase__ : Optional[int] = get_linear_schedule_with_warmup(
optimizer=_lowerCAmelCase , num_warmup_steps=0 , num_training_steps=_lowerCAmelCase , )
else:
lowercase__ : Tuple = DummyScheduler(_lowerCAmelCase , total_num_steps=_lowerCAmelCase , warmup_num_steps=0 )
# 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.
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : Dict = accelerator.prepare(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# We need to keep track of how many total steps we have iterated over
lowercase__ : Optional[int] = 0
# We also need to keep track of the stating epoch so files are named properly
lowercase__ : Tuple = 0
# Now we train the model
lowercase__ : Optional[Any] = {}
for epoch in range(_lowerCAmelCase , _lowerCAmelCase ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(_lowerCAmelCase ):
lowercase__ : List[Any] = model(**_lowerCAmelCase )
lowercase__ : Dict = outputs.loss
lowercase__ : int = loss / gradient_accumulation_steps
accelerator.backward(_lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin ) ) )
accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used ) )
accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked ) )
accelerator.print(
'Total Peak Memory consumed during the train (max): {}'.format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
lowercase__ : int = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'peak_memory_utilization.json' ) , 'w' ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
def a_ ( ):
'''simple docstring'''
lowercase__ : int = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=_lowerCAmelCase , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=_lowerCAmelCase , )
parser.add_argument(
'--output_dir' , type=_lowerCAmelCase , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--peak_memory_upper_bound' , type=_lowerCAmelCase , default=_lowerCAmelCase , help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.' , )
parser.add_argument(
'--n_train' , type=_lowerCAmelCase , default=320 , help='Number of training examples to use.' , )
parser.add_argument(
'--n_val' , type=_lowerCAmelCase , default=160 , help='Number of validation examples to use.' , )
parser.add_argument(
'--num_epochs' , type=_lowerCAmelCase , default=1 , help='Number of train epochs.' , )
lowercase__ : Any = parser.parse_args()
lowercase__ : Optional[Any] = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 645
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__A = {
"configuration_audio_spectrogram_transformer": [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ASTConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"ASTForAudioClassification",
"ASTModel",
"ASTPreTrainedModel",
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["ASTFeatureExtractor"]
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 469
|
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : List[str] , __UpperCamelCase : str ) -> Optional[Any]:
if isinstance(__UpperCamelCase , torch.Tensor ):
return image
elif isinstance(__UpperCamelCase , PIL.Image.Image ):
UpperCAmelCase_ = [image]
if isinstance(image[0] , PIL.Image.Image ):
UpperCAmelCase_ = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos'''] ) )[None, :] for i in image]
UpperCAmelCase_ = np.concatenate(__UpperCamelCase , axis=0 )
UpperCAmelCase_ = np.array(__UpperCamelCase ).astype(np.floataa ) / 255.0
UpperCAmelCase_ = image.transpose(0 , 3 , 1 , 2 )
UpperCAmelCase_ = 2.0 * image - 1.0
UpperCAmelCase_ = torch.from_numpy(__UpperCamelCase )
elif isinstance(image[0] , torch.Tensor ):
UpperCAmelCase_ = torch.cat(__UpperCamelCase , dim=0 )
return image
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[Any] , __UpperCamelCase : Any , __UpperCamelCase : str , __UpperCamelCase : Optional[int]=0.9_995 ) -> List[str]:
if not isinstance(__UpperCamelCase , np.ndarray ):
UpperCAmelCase_ = True
UpperCAmelCase_ = va.device
UpperCAmelCase_ = va.cpu().numpy()
UpperCAmelCase_ = va.cpu().numpy()
UpperCAmelCase_ = np.sum(va * va / (np.linalg.norm(__UpperCamelCase ) * np.linalg.norm(__UpperCamelCase )) )
if np.abs(__UpperCamelCase ) > DOT_THRESHOLD:
UpperCAmelCase_ = (1 - t) * va + t * va
else:
UpperCAmelCase_ = np.arccos(__UpperCamelCase )
UpperCAmelCase_ = np.sin(__UpperCamelCase )
UpperCAmelCase_ = theta_a * t
UpperCAmelCase_ = np.sin(__UpperCamelCase )
UpperCAmelCase_ = np.sin(theta_a - theta_t ) / sin_theta_a
UpperCAmelCase_ = sin_theta_t / sin_theta_a
UpperCAmelCase_ = sa * va + sa * va
if inputs_are_torch:
UpperCAmelCase_ = torch.from_numpy(__UpperCamelCase ).to(__UpperCamelCase )
return va
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str , __UpperCamelCase : List[str] ) -> Optional[int]:
UpperCAmelCase_ = F.normalize(__UpperCamelCase , dim=-1 )
UpperCAmelCase_ = F.normalize(__UpperCamelCase , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[Any] , __UpperCamelCase : Dict ) -> Optional[int]:
for param in model.parameters():
UpperCAmelCase_ = value
class a ( _A ):
'''simple docstring'''
def __init__( self : List[str] , __snake_case : AutoencoderKL , __snake_case : CLIPTextModel , __snake_case : CLIPModel , __snake_case : CLIPTokenizer , __snake_case : UNetaDConditionModel , __snake_case : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , __snake_case : CLIPFeatureExtractor , __snake_case : Dict=None , __snake_case : List[str]=None , __snake_case : Optional[Any]=None , ):
super().__init__()
self.register_modules(
vae=__snake_case , text_encoder=__snake_case , clip_model=__snake_case , tokenizer=__snake_case , unet=__snake_case , scheduler=__snake_case , feature_extractor=__snake_case , coca_model=__snake_case , coca_tokenizer=__snake_case , coca_transform=__snake_case , )
UpperCAmelCase_ = (
feature_extractor.size
if isinstance(feature_extractor.size , __snake_case )
else feature_extractor.size['''shortest_edge''']
)
UpperCAmelCase_ = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , __snake_case )
set_requires_grad(self.clip_model , __snake_case )
def lowerCamelCase_ ( self : Dict , __snake_case : Optional[Union[str, int]] = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__snake_case )
def lowerCamelCase_ ( self : str ):
self.enable_attention_slicing(__snake_case )
def lowerCamelCase_ ( self : List[Any] ):
set_requires_grad(self.vae , __snake_case )
def lowerCamelCase_ ( self : int ):
set_requires_grad(self.vae , __snake_case )
def lowerCamelCase_ ( self : str ):
set_requires_grad(self.unet , __snake_case )
def lowerCamelCase_ ( self : List[Any] ):
set_requires_grad(self.unet , __snake_case )
def lowerCamelCase_ ( self : Any , __snake_case : Any , __snake_case : Union[str, Any] , __snake_case : List[str] ):
# get the original timestep using init_timestep
UpperCAmelCase_ = min(int(num_inference_steps * strength ) , __snake_case )
UpperCAmelCase_ = max(num_inference_steps - init_timestep , 0 )
UpperCAmelCase_ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowerCamelCase_ ( self : int , __snake_case : Tuple , __snake_case : Tuple , __snake_case : Union[str, Any] , __snake_case : Optional[Any] , __snake_case : Optional[Any] , __snake_case : str=None ):
if not isinstance(__snake_case , torch.Tensor ):
raise ValueError(F'`image` has to be of type `torch.Tensor` but is {type(__snake_case )}' )
UpperCAmelCase_ = image.to(device=__snake_case , dtype=__snake_case )
if isinstance(__snake_case , __snake_case ):
UpperCAmelCase_ = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(__snake_case )
]
UpperCAmelCase_ = torch.cat(__snake_case , dim=0 )
else:
UpperCAmelCase_ = self.vae.encode(__snake_case ).latent_dist.sample(__snake_case )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
UpperCAmelCase_ = 0.18_215 * init_latents
UpperCAmelCase_ = init_latents.repeat_interleave(__snake_case , dim=0 )
UpperCAmelCase_ = randn_tensor(init_latents.shape , generator=__snake_case , device=__snake_case , dtype=__snake_case )
# get latents
UpperCAmelCase_ = self.scheduler.add_noise(__snake_case , __snake_case , __snake_case )
UpperCAmelCase_ = init_latents
return latents
def lowerCamelCase_ ( self : Dict , __snake_case : Dict ):
UpperCAmelCase_ = self.coca_transform(__snake_case ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
UpperCAmelCase_ = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
UpperCAmelCase_ = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''' , '''''' ).rstrip(''' .,''' )
def lowerCamelCase_ ( self : Optional[int] , __snake_case : Any , __snake_case : List[Any] ):
UpperCAmelCase_ = self.feature_extractor.preprocess(__snake_case )
UpperCAmelCase_ = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half()
UpperCAmelCase_ = self.clip_model.get_image_features(__snake_case )
UpperCAmelCase_ = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__snake_case )
UpperCAmelCase_ = image_embeddings_clip.repeat_interleave(__snake_case , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def lowerCamelCase_ ( self : Tuple , __snake_case : Tuple , __snake_case : List[Any] , __snake_case : Tuple , __snake_case : Optional[int] , __snake_case : Tuple , __snake_case : Optional[int] , __snake_case : Union[str, Any] , ):
UpperCAmelCase_ = latents.detach().requires_grad_()
UpperCAmelCase_ = self.scheduler.scale_model_input(__snake_case , __snake_case )
# predict the noise residual
UpperCAmelCase_ = self.unet(__snake_case , __snake_case , encoder_hidden_states=__snake_case ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
UpperCAmelCase_ = self.scheduler.alphas_cumprod[timestep]
UpperCAmelCase_ = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
UpperCAmelCase_ = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
UpperCAmelCase_ = torch.sqrt(__snake_case )
UpperCAmelCase_ = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , __snake_case ):
UpperCAmelCase_ = self.scheduler.sigmas[index]
UpperCAmelCase_ = latents - sigma * noise_pred
else:
raise ValueError(F'scheduler type {type(self.scheduler )} not supported' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
UpperCAmelCase_ = 1 / 0.18_215 * sample
UpperCAmelCase_ = self.vae.decode(__snake_case ).sample
UpperCAmelCase_ = (image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase_ = transforms.Resize(self.feature_extractor_size )(__snake_case )
UpperCAmelCase_ = self.normalize(__snake_case ).to(latents.dtype )
UpperCAmelCase_ = self.clip_model.get_image_features(__snake_case )
UpperCAmelCase_ = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__snake_case )
UpperCAmelCase_ = spherical_dist_loss(__snake_case , __snake_case ).mean() * clip_guidance_scale
UpperCAmelCase_ = -torch.autograd.grad(__snake_case , __snake_case )[0]
if isinstance(self.scheduler , __snake_case ):
UpperCAmelCase_ = latents.detach() + grads * (sigma**2)
UpperCAmelCase_ = noise_pred_original
else:
UpperCAmelCase_ = noise_pred_original - torch.sqrt(__snake_case ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self : Tuple , __snake_case : Union[torch.FloatTensor, PIL.Image.Image] , __snake_case : Union[torch.FloatTensor, PIL.Image.Image] , __snake_case : Optional[str] = None , __snake_case : Optional[str] = None , __snake_case : Optional[int] = 5_12 , __snake_case : Optional[int] = 5_12 , __snake_case : float = 0.6 , __snake_case : Optional[int] = 50 , __snake_case : Optional[float] = 7.5 , __snake_case : Optional[int] = 1 , __snake_case : float = 0.0 , __snake_case : Optional[float] = 1_00 , __snake_case : Optional[torch.Generator] = None , __snake_case : Optional[str] = "pil" , __snake_case : bool = True , __snake_case : float = 0.8 , __snake_case : float = 0.1 , __snake_case : float = 0.1 , ):
if isinstance(__snake_case , __snake_case ) and len(__snake_case ) != batch_size:
raise ValueError(F'You have passed {batch_size} batch_size, but only {len(__snake_case )} generators.' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.' )
if isinstance(__snake_case , torch.Generator ) and batch_size > 1:
UpperCAmelCase_ = [generator] + [None] * (batch_size - 1)
UpperCAmelCase_ = [
('''model''', self.coca_model is None),
('''tokenizer''', self.coca_tokenizer is None),
('''transform''', self.coca_transform is None),
]
UpperCAmelCase_ = [x[0] for x in coca_is_none if x[1]]
UpperCAmelCase_ = ''', '''.join(__snake_case )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(__snake_case ):
raise ValueError(
F'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
F'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
UpperCAmelCase_ = self.get_image_description(__snake_case )
if style_prompt is None:
if len(__snake_case ):
raise ValueError(
F'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
F' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
UpperCAmelCase_ = self.get_image_description(__snake_case )
# get prompt text embeddings for content and style
UpperCAmelCase_ = self.tokenizer(
__snake_case , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=__snake_case , return_tensors='''pt''' , )
UpperCAmelCase_ = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
UpperCAmelCase_ = self.tokenizer(
__snake_case , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=__snake_case , return_tensors='''pt''' , )
UpperCAmelCase_ = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
UpperCAmelCase_ = slerp(__snake_case , __snake_case , __snake_case )
# duplicate text embeddings for each generation per prompt
UpperCAmelCase_ = text_embeddings.repeat_interleave(__snake_case , dim=0 )
# set timesteps
UpperCAmelCase_ = '''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
UpperCAmelCase_ = {}
if accepts_offset:
UpperCAmelCase_ = 1
self.scheduler.set_timesteps(__snake_case , **__snake_case )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
UpperCAmelCase_ , UpperCAmelCase_ = self.get_timesteps(__snake_case , __snake_case , self.device )
UpperCAmelCase_ = timesteps[:1].repeat(__snake_case )
# Preprocess image
UpperCAmelCase_ = preprocess(__snake_case , __snake_case , __snake_case )
UpperCAmelCase_ = self.prepare_latents(
__snake_case , __snake_case , __snake_case , text_embeddings.dtype , self.device , __snake_case )
UpperCAmelCase_ = preprocess(__snake_case , __snake_case , __snake_case )
UpperCAmelCase_ = self.prepare_latents(
__snake_case , __snake_case , __snake_case , text_embeddings.dtype , self.device , __snake_case )
UpperCAmelCase_ = slerp(__snake_case , __snake_case , __snake_case )
if clip_guidance_scale > 0:
UpperCAmelCase_ = self.get_clip_image_embeddings(__snake_case , __snake_case )
UpperCAmelCase_ = self.get_clip_image_embeddings(__snake_case , __snake_case )
UpperCAmelCase_ = slerp(
__snake_case , __snake_case , __snake_case )
# 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.
UpperCAmelCase_ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
UpperCAmelCase_ = content_text_input.input_ids.shape[-1]
UpperCAmelCase_ = self.tokenizer([''''''] , padding='''max_length''' , max_length=__snake_case , return_tensors='''pt''' )
UpperCAmelCase_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
UpperCAmelCase_ = uncond_embeddings.repeat_interleave(__snake_case , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase_ = 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`.
UpperCAmelCase_ = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
UpperCAmelCase_ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
UpperCAmelCase_ = torch.randn(__snake_case , generator=__snake_case , device='''cpu''' , dtype=__snake_case ).to(
self.device )
else:
UpperCAmelCase_ = torch.randn(__snake_case , generator=__snake_case , device=self.device , dtype=__snake_case )
else:
if latents.shape != latents_shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
UpperCAmelCase_ = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
UpperCAmelCase_ = 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]
UpperCAmelCase_ = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCAmelCase_ = {}
if accepts_eta:
UpperCAmelCase_ = eta
# check if the scheduler accepts generator
UpperCAmelCase_ = '''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
UpperCAmelCase_ = generator
with self.progress_bar(total=__snake_case ):
for i, t in enumerate(__snake_case ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase_ = self.scheduler.scale_model_input(__snake_case , __snake_case )
# predict the noise residual
UpperCAmelCase_ = self.unet(__snake_case , __snake_case , encoder_hidden_states=__snake_case ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
UpperCAmelCase_ , UpperCAmelCase_ = noise_pred.chunk(2 )
UpperCAmelCase_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
UpperCAmelCase_ = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
UpperCAmelCase_ , UpperCAmelCase_ = self.cond_fn(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase_ = self.scheduler.step(__snake_case , __snake_case , __snake_case , **__snake_case ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
UpperCAmelCase_ = 1 / 0.18_215 * latents
UpperCAmelCase_ = self.vae.decode(__snake_case ).sample
UpperCAmelCase_ = (image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase_ = self.numpy_to_pil(__snake_case )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=__snake_case , nsfw_content_detected=__snake_case )
| 144
| 0
|
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : Tuple = len(UpperCamelCase )
__UpperCAmelCase : Dict = sum(UpperCamelCase )
__UpperCAmelCase : Tuple = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
__UpperCAmelCase : str = True
for i in range(1 , s + 1 ):
__UpperCAmelCase : Tuple = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
__UpperCAmelCase : str = dp[i][j - 1]
if arr[i - 1] <= j:
__UpperCAmelCase : Any = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
__UpperCAmelCase : Dict = s - 2 * j
break
return diff
| 487
|
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class a__ ( __magic_name__ ):
lowercase_ = ["image_processor", "feature_extractor"]
lowercase_ = "TvltImageProcessor"
lowercase_ = "TvltFeatureExtractor"
def __init__( self : Tuple , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Dict):
"""simple docstring"""
super().__init__(image_processor=UpperCamelCase_ , feature_extractor=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = image_processor
__UpperCAmelCase : Dict = feature_extractor
def __call__( self : Tuple , UpperCamelCase_ : str=None , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : str=None , UpperCamelCase_ : List[str]=False , UpperCamelCase_ : Optional[int]=False , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : str , ):
"""simple docstring"""
if images is None and audio is None:
raise ValueError("You need to specify either an `images` or `audio` input to process.")
__UpperCAmelCase : Optional[Any] = None
if images is not None:
__UpperCAmelCase : int = self.image_processor(UpperCamelCase_ , mask_pixel=UpperCamelCase_ , *UpperCamelCase_ , **UpperCamelCase_)
if images_mixed is not None:
__UpperCAmelCase : int = self.image_processor(UpperCamelCase_ , is_mixed=UpperCamelCase_ , *UpperCamelCase_ , **UpperCamelCase_)
if audio is not None:
__UpperCAmelCase : List[Any] = self.feature_extractor(
UpperCamelCase_ , *UpperCamelCase_ , sampling_rate=UpperCamelCase_ , mask_audio=UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : List[str] = {}
if audio is not None:
output_dict.update(UpperCamelCase_)
if images is not None:
output_dict.update(UpperCamelCase_)
if images_mixed_dict is not None:
output_dict.update(UpperCamelCase_)
return output_dict
@property
def a_ ( self : str):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.image_processor.model_input_names
__UpperCAmelCase : List[Any] = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))
| 487
| 1
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class _lowercase ( unittest.TestCase ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_=7 , UpperCamelCase_=3 , UpperCamelCase_=18 , UpperCamelCase_=30 , UpperCamelCase_=400 , UpperCamelCase_=True , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_=[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] , UpperCamelCase_=[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_=True , ):
__magic_name__ = size if size is not None else {'''height''': 224, '''width''': 224}
__magic_name__ = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
__magic_name__ = parent
__magic_name__ = batch_size
__magic_name__ = num_channels
__magic_name__ = image_size
__magic_name__ = min_resolution
__magic_name__ = max_resolution
__magic_name__ = do_resize
__magic_name__ = size
__magic_name__ = do_center_crop
__magic_name__ = crop_size
__magic_name__ = do_normalize
__magic_name__ = image_mean
__magic_name__ = image_std
__magic_name__ = do_convert_rgb
def lowerCAmelCase__ ( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def lowerCAmelCase__ ( self , UpperCamelCase_=False , UpperCamelCase_=False , UpperCamelCase_=False ):
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
__magic_name__ = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
255 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
__magic_name__ = []
for i in range(self.batch_size ):
__magic_name__ , __magic_name__ = np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(255 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
__magic_name__ = [Image.fromarray(np.moveaxis(UpperCamelCase_ , 0 , -1 ) ) for x in image_inputs]
if torchify:
__magic_name__ = [torch.from_numpy(UpperCamelCase_ ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class _lowercase ( __UpperCAmelCase , unittest.TestCase ):
_lowerCamelCase = ChineseCLIPImageProcessor if is_vision_available() else None
def lowerCAmelCase__ ( self ):
__magic_name__ = ChineseCLIPImageProcessingTester(self , do_center_crop=UpperCamelCase_ )
@property
def lowerCAmelCase__ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ ( self ):
__magic_name__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase_ , '''do_resize''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''size''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''do_center_crop''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''center_crop''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''do_normalize''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''image_mean''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''image_std''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''do_convert_rgb''' ) )
def lowerCAmelCase__ ( self ):
__magic_name__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 224, '''width''': 224} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
__magic_name__ = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def lowerCAmelCase__ ( self ):
pass
def lowerCAmelCase__ ( self ):
# Initialize image_processing
__magic_name__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__magic_name__ = self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image )
# Test not batched input
__magic_name__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__magic_name__ = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCAmelCase__ ( self ):
# Initialize image_processing
__magic_name__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__magic_name__ = self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase_ , numpify=UpperCamelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , np.ndarray )
# Test not batched input
__magic_name__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__magic_name__ = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCAmelCase__ ( self ):
# Initialize image_processing
__magic_name__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__magic_name__ = self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase_ , torchify=UpperCamelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , torch.Tensor )
# Test not batched input
__magic_name__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__magic_name__ = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
@require_torch
@require_vision
class _lowercase ( __UpperCAmelCase , unittest.TestCase ):
_lowerCamelCase = ChineseCLIPImageProcessor if is_vision_available() else None
def lowerCAmelCase__ ( self ):
__magic_name__ = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=UpperCamelCase_ )
__magic_name__ = 3
@property
def lowerCAmelCase__ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ ( self ):
__magic_name__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase_ , '''do_resize''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''size''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''do_center_crop''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''center_crop''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''do_normalize''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''image_mean''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''image_std''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''do_convert_rgb''' ) )
def lowerCAmelCase__ ( self ):
pass
def lowerCAmelCase__ ( self ):
# Initialize image_processing
__magic_name__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__magic_name__ = self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image )
# Test not batched input
__magic_name__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__magic_name__ = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 490
|
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=os.environ.get("LOGLEVEL", "INFO").upper(),
stream=sys.stdout,
)
__lowerCamelCase = logging.getLogger(__name__)
__lowerCamelCase = {"facebook/bart-base": BartForConditionalGeneration}
__lowerCamelCase = {"facebook/bart-base": BartTokenizer}
def lowercase ( ) -> List[str]:
__magic_name__ = argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.''' )
parser.add_argument(
'''--validation_file''' , type=__UpperCamelCase , default=__UpperCamelCase , help='''A csv or a json file containing the validation data.''' )
parser.add_argument(
'''--max_length''' , type=__UpperCamelCase , default=5 , help='''The maximum total input sequence length after tokenization.''' , )
parser.add_argument(
'''--num_beams''' , type=__UpperCamelCase , default=__UpperCamelCase , help=(
'''Number of beams to use for evaluation. This argument will be '''
'''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
) , )
parser.add_argument(
'''--model_name_or_path''' , type=__UpperCamelCase , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , required=__UpperCamelCase , )
parser.add_argument(
'''--config_name''' , type=__UpperCamelCase , default=__UpperCamelCase , help='''Pretrained config name or path if not the same as model_name''' , )
parser.add_argument(
'''--device''' , type=__UpperCamelCase , default='''cpu''' , help='''Device where the model will be run''' , )
parser.add_argument('''--output_file_path''' , type=__UpperCamelCase , default=__UpperCamelCase , help='''Where to store the final ONNX file.''' )
__magic_name__ = parser.parse_args()
return args
def lowercase ( __UpperCamelCase , __UpperCamelCase="cpu" ) -> int:
__magic_name__ = model_dict[model_name].from_pretrained(__UpperCamelCase ).to(__UpperCamelCase )
__magic_name__ = tokenizer_dict[model_name].from_pretrained(__UpperCamelCase )
if model_name in ["facebook/bart-base"]:
__magic_name__ = 0
__magic_name__ = None
__magic_name__ = 0
return huggingface_model, tokenizer
def lowercase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Any:
model.eval()
__magic_name__ = None
__magic_name__ = torch.jit.script(BARTBeamSearchGenerator(__UpperCamelCase ) )
with torch.no_grad():
__magic_name__ = '''My friends are cool but they eat too many carbs.'''
__magic_name__ = tokenizer([ARTICLE_TO_SUMMARIZE] , max_length=1024 , return_tensors='''pt''' ).to(model.device )
__magic_name__ = model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , num_beams=__UpperCamelCase , max_length=__UpperCamelCase , early_stopping=__UpperCamelCase , decoder_start_token_id=model.config.decoder_start_token_id , )
torch.onnx.export(
__UpperCamelCase , (
inputs['''input_ids'''],
inputs['''attention_mask'''],
num_beams,
max_length,
model.config.decoder_start_token_id,
) , __UpperCamelCase , opset_version=14 , input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id'''] , output_names=['''output_ids'''] , dynamic_axes={
'''input_ids''': {0: '''batch''', 1: '''seq'''},
'''output_ids''': {0: '''batch''', 1: '''seq_out'''},
} , example_outputs=__UpperCamelCase , )
logger.info('''Model exported to {}'''.format(__UpperCamelCase ) )
__magic_name__ = remove_dup_initializers(os.path.abspath(__UpperCamelCase ) )
logger.info('''Deduplicated and optimized model written to {}'''.format(__UpperCamelCase ) )
__magic_name__ = onnxruntime.InferenceSession(__UpperCamelCase )
__magic_name__ = ort_sess.run(
__UpperCamelCase , {
'''input_ids''': inputs['''input_ids'''].cpu().numpy(),
'''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
'''num_beams''': np.array(__UpperCamelCase ),
'''max_length''': np.array(__UpperCamelCase ),
'''decoder_start_token_id''': np.array(model.config.decoder_start_token_id ),
} , )
np.testing.assert_allclose(summary_ids.cpu().numpy() , ort_out[0] , rtol=1e-3 , atol=1e-3 )
logger.info('''Model outputs from torch and ONNX Runtime are similar.''' )
logger.info('''Success.''' )
def lowercase ( ) -> Any:
__magic_name__ = parse_args()
__magic_name__ = 5
__magic_name__ = 4
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO , )
logger.setLevel(logging.INFO )
transformers.utils.logging.set_verbosity_error()
__magic_name__ = torch.device(args.device )
__magic_name__ , __magic_name__ = load_model_tokenizer(args.model_name_or_path , __UpperCamelCase )
if model.config.decoder_start_token_id is None:
raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined''' )
model.to(__UpperCamelCase )
if args.max_length:
__magic_name__ = args.max_length
if args.num_beams:
__magic_name__ = args.num_beams
if args.output_file_path:
__magic_name__ = args.output_file_path
else:
__magic_name__ = '''BART.onnx'''
logger.info('''Exporting model to ONNX''' )
export_and_validate_model(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if __name__ == "__main__":
main()
| 490
| 1
|
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __snake_case ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
a__ = OmegaConf.load(UpperCamelCase )
a__ = torch.load(UpperCamelCase , map_location='''cpu''' )['''model''']
a__ = list(state_dict.keys() )
# extract state_dict for VQVAE
a__ = {}
a__ = '''first_stage_model.'''
for key in keys:
if key.startswith(UpperCamelCase ):
a__ = state_dict[key]
# extract state_dict for UNetLDM
a__ = {}
a__ = '''model.diffusion_model.'''
for key in keys:
if key.startswith(UpperCamelCase ):
a__ = state_dict[key]
a__ = config.model.params.first_stage_config.params
a__ = config.model.params.unet_config.params
a__ = VQModel(**UpperCamelCase ).eval()
vqvae.load_state_dict(UpperCamelCase )
a__ = UNetLDMModel(**UpperCamelCase ).eval()
unet.load_state_dict(UpperCamelCase )
a__ = DDIMScheduler(
timesteps=config.model.params.timesteps , beta_schedule='''scaled_linear''' , beta_start=config.model.params.linear_start , beta_end=config.model.params.linear_end , clip_sample=UpperCamelCase , )
a__ = LDMPipeline(UpperCamelCase , UpperCamelCase , UpperCamelCase )
pipeline.save_pretrained(UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('''--checkpoint_path''', type=str, required=True)
parser.add_argument('''--config_path''', type=str, required=True)
parser.add_argument('''--output_path''', type=str, required=True)
__lowerCAmelCase : str = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 158
|
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def __init__( self :Optional[int] , __magic_name__ :str , __magic_name__ :List[str]=13 , __magic_name__ :int=30 , __magic_name__ :Optional[Any]=2 , __magic_name__ :Optional[int]=3 , __magic_name__ :List[str]=True , __magic_name__ :Any=True , __magic_name__ :Union[str, Any]=32 , __magic_name__ :List[str]=5 , __magic_name__ :Optional[int]=4 , __magic_name__ :Union[str, Any]=37 , __magic_name__ :str="gelu" , __magic_name__ :Tuple=0.1 , __magic_name__ :List[str]=0.1 , __magic_name__ :List[str]=10 , __magic_name__ :Union[str, Any]=0.02 , ) -> Union[str, Any]:
'''simple docstring'''
a__ = parent
a__ = batch_size
a__ = image_size
a__ = patch_size
a__ = num_channels
a__ = is_training
a__ = use_labels
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__ = type_sequence_label_size
a__ = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
a__ = (image_size // patch_size) ** 2
a__ = num_patches + 1
def _UpperCamelCase ( self :Optional[Any] ) -> Any:
'''simple docstring'''
a__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a__ = ViTConfig(
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=__magic_name__ , initializer_range=self.initializer_range , )
return config, pixel_values
def _UpperCamelCase ( self :Union[str, Any] , __magic_name__ :Tuple , __magic_name__ :Dict ) -> List[str]:
'''simple docstring'''
a__ = FlaxViTModel(config=__magic_name__ )
a__ = model(__magic_name__ )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
a__ = (self.image_size, self.image_size)
a__ = (self.patch_size, self.patch_size)
a__ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def _UpperCamelCase ( self :str , __magic_name__ :Optional[int] , __magic_name__ :List[str] ) -> Optional[Any]:
'''simple docstring'''
a__ = self.type_sequence_label_size
a__ = FlaxViTForImageClassification(config=__magic_name__ )
a__ = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
a__ = 1
a__ = FlaxViTForImageClassification(__magic_name__ )
a__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
a__ = model(__magic_name__ )
def _UpperCamelCase ( self :str ) -> str:
'''simple docstring'''
a__ = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) ,
) = config_and_inputs
a__ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class SCREAMING_SNAKE_CASE ( lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
snake_case__ : List[Any] = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def _UpperCamelCase ( self :Dict ) -> None:
'''simple docstring'''
a__ = FlaxViTModelTester(self )
a__ = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def _UpperCamelCase ( self :str ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def _UpperCamelCase ( self :Union[str, Any] ) -> Tuple:
'''simple docstring'''
a__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def _UpperCamelCase ( self :Any ) -> Dict:
'''simple docstring'''
a__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
def _UpperCamelCase ( self :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
a__ , a__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ = model_class(__magic_name__ )
a__ = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__ = [*signature.parameters.keys()]
a__ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def _UpperCamelCase ( self :str ) -> Optional[Any]:
'''simple docstring'''
a__ , a__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
a__ = self._prepare_for_class(__magic_name__ , __magic_name__ )
a__ = model_class(__magic_name__ )
@jax.jit
def model_jitted(__magic_name__ :Dict , **__magic_name__ :Dict ):
return model(pixel_values=__magic_name__ , **__magic_name__ )
with self.subTest('''JIT Enabled''' ):
a__ = model_jitted(**__magic_name__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
a__ = model_jitted(**__magic_name__ ).to_tuple()
self.assertEqual(len(__magic_name__ ) , len(__magic_name__ ) )
for jitted_output, output in zip(__magic_name__ , __magic_name__ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def _UpperCamelCase ( self :List[Any] ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
a__ = model_class_name.from_pretrained('''google/vit-base-patch16-224''' )
a__ = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(__magic_name__ )
| 158
| 1
|
"""simple docstring"""
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
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################
SCREAMING_SNAKE_CASE_ = 16
SCREAMING_SNAKE_CASE_ = 32
def __snake_case ( _lowercase ,_lowercase = 16 ):
"""simple docstring"""
UpperCamelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
UpperCamelCase = load_dataset('''glue''' ,'''mrpc''' )
def tokenize_function(_lowercase ):
# 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(_lowercase ):
# 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_ )
UpperCamelCase = DataLoader(
tokenized_datasets['''validation'''] ,shuffle=lowerCAmelCase_ ,collate_fn=lowerCAmelCase_ ,batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
SCREAMING_SNAKE_CASE_ = mocked_dataloaders # noqa: F811
def __snake_case ( _lowercase ,_lowercase ):
"""simple docstring"""
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,lowerCAmelCase_ ) == "1":
UpperCamelCase = 2
# New Code #
UpperCamelCase = int(args.gradient_accumulation_steps )
UpperCamelCase = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=lowerCAmelCase_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''' )
# 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''' )
set_seed(lowerCAmelCase_ )
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) ,)
# 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 = accelerator.prepare(
lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
with LocalSGD(
accelerator=lowerCAmelCase_ ,model=lowerCAmelCase_ ,local_sgd_steps=lowerCAmelCase_ ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(lowerCAmelCase_ ):
# 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(lowerCAmelCase_ ):
UpperCamelCase = model(**lowerCAmelCase_ )
UpperCamelCase = output.loss
accelerator.backward(lowerCAmelCase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
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 = 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 __snake_case ( ):
"""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.''' ,)
# New Code #
parser.add_argument(
'''--gradient_accumulation_steps''' ,type=lowerCAmelCase_ ,default=1 ,help='''The number of minibatches to be ran before gradients are accumulated.''' ,)
parser.add_argument(
'''--local_sgd_steps''' ,type=lowerCAmelCase_ ,default=8 ,help='''Number of local SGD steps or None to disable local SGD''' )
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()
| 34
|
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
__SCREAMING_SNAKE_CASE = 'hf-internal-testing/tiny-random-bert'
__SCREAMING_SNAKE_CASE = os.path.join(TRANSFORMERS_CACHE, 'models--hf-internal-testing--tiny-random-bert')
__SCREAMING_SNAKE_CASE = '9b8c223d42b2188cb49d29af482996f9d0f3e5a6'
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : Optional[Any] =cached_file(__UpperCAmelCase , __UpperCAmelCase )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(__UpperCAmelCase ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(__UpperCAmelCase , __UpperCAmelCase ) ) )
with open(os.path.join(__UpperCAmelCase , 'refs' , 'main' ) ) as f:
SCREAMING_SNAKE_CASE_ : int =f.read()
self.assertEqual(__UpperCAmelCase , os.path.join(__UpperCAmelCase , 'snapshots' , __UpperCAmelCase , __UpperCAmelCase ) )
self.assertTrue(os.path.isfile(__UpperCAmelCase ) )
# File is cached at the same place the second time.
SCREAMING_SNAKE_CASE_ : Optional[int] =cached_file(__UpperCAmelCase , __UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
# Using a specific revision to test the full commit hash.
SCREAMING_SNAKE_CASE_ : Optional[int] =cached_file(__UpperCAmelCase , __UpperCAmelCase , revision='9b8c223' )
self.assertEqual(__UpperCAmelCase , os.path.join(__UpperCAmelCase , 'snapshots' , __UpperCAmelCase , __UpperCAmelCase ) )
def __lowerCamelCase ( self ):
with self.assertRaisesRegex(__UpperCAmelCase , 'is not a valid model identifier' ):
SCREAMING_SNAKE_CASE_ : Dict =cached_file('tiny-random-bert' , __UpperCAmelCase )
with self.assertRaisesRegex(__UpperCAmelCase , 'is not a valid git identifier' ):
SCREAMING_SNAKE_CASE_ : List[Any] =cached_file(__UpperCAmelCase , __UpperCAmelCase , revision='aaaa' )
with self.assertRaisesRegex(__UpperCAmelCase , 'does not appear to have a file named' ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] =cached_file(__UpperCAmelCase , 'conf' )
def __lowerCamelCase ( self ):
with self.assertRaisesRegex(__UpperCAmelCase , 'does not appear to have a file named' ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] =cached_file(__UpperCAmelCase , 'conf' )
with open(os.path.join(__UpperCAmelCase , 'refs' , 'main' ) ) as f:
SCREAMING_SNAKE_CASE_ : Any =f.read()
self.assertTrue(os.path.isfile(os.path.join(__UpperCAmelCase , '.no_exist' , __UpperCAmelCase , 'conf' ) ) )
SCREAMING_SNAKE_CASE_ : str =cached_file(__UpperCAmelCase , 'conf' , _raise_exceptions_for_missing_entries=__UpperCAmelCase )
self.assertIsNone(__UpperCAmelCase )
SCREAMING_SNAKE_CASE_ : Dict =cached_file(__UpperCAmelCase , 'conf' , local_files_only=__UpperCAmelCase , _raise_exceptions_for_missing_entries=__UpperCAmelCase )
self.assertIsNone(__UpperCAmelCase )
SCREAMING_SNAKE_CASE_ : str =mock.Mock()
SCREAMING_SNAKE_CASE_ : List[str] =500
SCREAMING_SNAKE_CASE_ : Tuple ={}
SCREAMING_SNAKE_CASE_ : str =HTTPError
SCREAMING_SNAKE_CASE_ : Optional[int] ={}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' , return_value=__UpperCAmelCase ) as mock_head:
SCREAMING_SNAKE_CASE_ : Union[str, Any] =cached_file(__UpperCAmelCase , 'conf' , _raise_exceptions_for_connection_errors=__UpperCAmelCase )
self.assertIsNone(__UpperCAmelCase )
# This check we did call the fake head request
mock_head.assert_called()
def __lowerCamelCase ( self ):
self.assertTrue(has_file('hf-internal-testing/tiny-bert-pt-only' , __UpperCAmelCase ) )
self.assertFalse(has_file('hf-internal-testing/tiny-bert-pt-only' , __UpperCAmelCase ) )
self.assertFalse(has_file('hf-internal-testing/tiny-bert-pt-only' , __UpperCAmelCase ) )
def __lowerCamelCase ( self ):
# `get_file_from_repo` returns None if the file does not exist
self.assertIsNone(get_file_from_repo('bert-base-cased' , 'ahah.txt' ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(__UpperCAmelCase , 'is not a valid model identifier' ):
get_file_from_repo('bert-base-case' , __UpperCAmelCase )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(__UpperCAmelCase , 'is not a valid git identifier' ):
get_file_from_repo('bert-base-cased' , __UpperCAmelCase , revision='ahaha' )
SCREAMING_SNAKE_CASE_ : Tuple =get_file_from_repo('bert-base-cased' , __UpperCAmelCase )
# The name is the cached name which is not very easy to test, so instead we load the content.
SCREAMING_SNAKE_CASE_ : List[str] =json.loads(open(__UpperCAmelCase , 'r' ).read() )
self.assertEqual(config['hidden_size'] , 768 )
def __lowerCamelCase ( self ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE_ : int =Path(__UpperCAmelCase ) / 'a.txt'
filename.touch()
self.assertEqual(get_file_from_repo(__UpperCAmelCase , 'a.txt' ) , str(__UpperCAmelCase ) )
self.assertIsNone(get_file_from_repo(__UpperCAmelCase , 'b.txt' ) )
| 220
| 0
|
'''simple docstring'''
import math
import os
import sys
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : List[Any] = """"""
try:
with open(lowerCamelCase__ , """rb""" ) as binary_file:
A_ : Dict = binary_file.read()
for dat in data:
A_ : int = f'{dat:08b}'
result += curr_byte
return result
except OSError:
print("""File not accessible""" )
sys.exit()
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
lexicon.pop(lowerCamelCase__ )
A_ : Dict = last_match_id
if math.loga(lowerCamelCase__ ).is_integer():
for curr_key in lexicon:
A_ : int = """0""" + lexicon[curr_key]
A_ : Union[str, Any] = bin(lowerCamelCase__ )[2:]
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : Union[str, Any] = {"""0""": """0""", """1""": """1"""}
A_, A_ : Any = """""", """"""
A_ : Optional[int] = len(lowerCamelCase__ )
for i in range(len(lowerCamelCase__ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
A_ : Optional[int] = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
index += 1
A_ : List[str] = """"""
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
A_ : int = lexicon[curr_string]
result += last_match_id
return result
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Any = os.path.getsize(lowerCamelCase__ )
A_ : Dict = bin(lowerCamelCase__ )[2:]
A_ : Dict = len(lowerCamelCase__ )
return "0" * (length_length - 1) + file_length_binary + compressed
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : List[str] = 8
try:
with open(lowerCamelCase__ , """wb""" ) as opened_file:
A_ : str = [
to_write[i : i + byte_length]
for i in range(0 , len(lowerCamelCase__ ) , lowerCamelCase__ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append("""10000000""" )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(lowerCamelCase__ , 2 ).to_bytes(1 , byteorder="""big""" ) )
except OSError:
print("""File not accessible""" )
sys.exit()
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Union[str, Any] = read_file_binary(lowerCamelCase__ )
A_ : Union[str, Any] = compress_data(lowerCamelCase__ )
A_ : int = add_file_length(lowerCamelCase__ , lowerCamelCase__ )
write_file_binary(lowerCamelCase__ , lowerCamelCase__ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 686
|
'''simple docstring'''
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : int = []
A_ : int = set({"""(""", """[""", """{"""} )
A_ : Union[str, Any] = set({""")""", """]""", """}"""} )
A_ : Tuple = {"""{""": """}""", """[""": """]""", """(""": """)"""}
for i in range(len(lowerCamelCase__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(lowerCamelCase__ ) == 0 or (len(lowerCamelCase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(lowerCamelCase__ ) == 0
def a ( ):
'''simple docstring'''
A_ : int = input("""Enter sequence of brackets: """ )
if is_balanced(lowerCamelCase__ ):
print(lowerCamelCase__ , """is balanced""" )
else:
print(lowerCamelCase__ , """is not balanced""" )
if __name__ == "__main__":
main()
| 686
| 1
|
'''simple docstring'''
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__lowercase : str = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ImportWarning(
'''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
)
if version.parse(pyarrow.__version__).major < 8:
raise ImportWarning(
'''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
'''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
)
del platform
del pyarrow
del version
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
__lowercase : List[str] = concatenate_datasets
__lowercase : Optional[Any] = DownloadConfig
__lowercase : Optional[Any] = DownloadManager
__lowercase : Any = DownloadMode
__lowercase : Optional[Any] = DownloadConfig
__lowercase : Tuple = DownloadMode
__lowercase : List[str] = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
| 422
|
'''simple docstring'''
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowercase_ ( _lowercase , _lowercase=False ) -> Dict:
'''simple docstring'''
lowerCamelCase_ : Tuple = OmegaConf.load(_lowercase )
if display:
print(yaml.dump(OmegaConf.to_container(_lowercase ) ) )
return config
def lowercase_ ( _lowercase , _lowercase=None , _lowercase=None ) -> Optional[int]:
'''simple docstring'''
if conf_path is None:
lowerCamelCase_ : int = '''./model_checkpoints/vqgan_only.yaml'''
lowerCamelCase_ : Dict = load_config(_lowercase , display=_lowercase )
lowerCamelCase_ : List[str] = VQModel(**config.model.params )
if ckpt_path is None:
lowerCamelCase_ : int = '''./model_checkpoints/vqgan_only.pt'''
lowerCamelCase_ : Union[str, Any] = torch.load(_lowercase , map_location=_lowercase )
if ".ckpt" in ckpt_path:
lowerCamelCase_ : str = sd['''state_dict''']
model.load_state_dict(_lowercase , strict=_lowercase )
model.to(_lowercase )
del sd
return model
def lowercase_ ( _lowercase , _lowercase ) -> List[str]:
'''simple docstring'''
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ : Any = model.encode(_lowercase )
print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}""" )
lowerCamelCase_ : Any = model.decode(_lowercase )
return xrec
def lowercase_ ( _lowercase , _lowercase=False ) -> Any:
'''simple docstring'''
lowerCamelCase_, lowerCamelCase_ : Any = string.rsplit('''.''' , 1 )
if reload:
lowerCamelCase_ : int = importlib.import_module(_lowercase )
importlib.reload(_lowercase )
return getattr(importlib.import_module(_lowercase , package=_lowercase ) , cls )
def lowercase_ ( _lowercase ) -> List[str]:
'''simple docstring'''
if "target" not in config:
raise KeyError('''Expected key `target` to instantiate.''' )
return get_obj_from_str(config['''target'''] )(**config.get('''params''' , {} ) )
def lowercase_ ( _lowercase , _lowercase , _lowercase=True , _lowercase=True ) -> Any:
'''simple docstring'''
lowerCamelCase_ : int = instantiate_from_config(_lowercase )
if sd is not None:
model.load_state_dict(_lowercase )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def lowercase_ ( _lowercase , _lowercase , _lowercase , _lowercase ) -> Tuple:
'''simple docstring'''
if ckpt:
lowerCamelCase_ : List[Any] = torch.load(_lowercase , map_location='''cpu''' )
lowerCamelCase_ : int = pl_sd['''global_step''']
print(F"""loaded model from global step {global_step}.""" )
else:
lowerCamelCase_ : Optional[int] = {'''state_dict''': None}
lowerCamelCase_ : str = None
lowerCamelCase_ : Any = load_model_from_config(config.model , pl_sd['''state_dict'''] , gpu=_lowercase , eval_mode=_lowercase )['''model''']
return model, global_step
| 422
| 1
|
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(lowercase__ ) )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(lowercase__ ) )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(lowercase__ ) )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
self.assertTrue(is_safetensors_compatible(lowercase__ ) )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
# Removed: 'text_encoder/model.safetensors',
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertFalse(is_safetensors_compatible(lowercase__ ) )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
SCREAMING_SNAKE_CASE_ : Dict = "fp16"
self.assertTrue(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = [
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
SCREAMING_SNAKE_CASE_ : str = "fp16"
self.assertTrue(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
SCREAMING_SNAKE_CASE_ : Optional[Any] = "fp16"
self.assertTrue(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "fp16"
self.assertFalse(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = [
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
]
SCREAMING_SNAKE_CASE_ : Optional[Any] = "fp16"
self.assertTrue(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
SCREAMING_SNAKE_CASE_ : str = "fp16"
self.assertTrue(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
# 'text_encoder/model.fp16.safetensors',
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
SCREAMING_SNAKE_CASE_ : Dict = "fp16"
self.assertFalse(is_safetensors_compatible(lowercase__ , variant=lowercase__ ) )
| 68
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
'umberto-commoncrawl-cased-v1': (
'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
),
'umberto-wikipedia-uncased-v1': (
'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
),
}
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
_A = "camembert"
def __init__( self , lowercase__=3_0522 , lowercase__=768 , lowercase__=12 , lowercase__=12 , lowercase__=3072 , lowercase__="gelu" , lowercase__=0.1 , lowercase__=0.1 , lowercase__=512 , lowercase__=2 , lowercase__=0.02 , lowercase__=1e-12 , lowercase__=1 , lowercase__=0 , lowercase__=2 , lowercase__="absolute" , lowercase__=True , lowercase__=None , **lowercase__ , ):
"""simple docstring"""
super().__init__(pad_token_id=lowercase__ , bos_token_id=lowercase__ , eos_token_id=lowercase__ , **lowercase__ )
SCREAMING_SNAKE_CASE_ : Tuple = vocab_size
SCREAMING_SNAKE_CASE_ : str = hidden_size
SCREAMING_SNAKE_CASE_ : str = num_hidden_layers
SCREAMING_SNAKE_CASE_ : Dict = num_attention_heads
SCREAMING_SNAKE_CASE_ : Any = hidden_act
SCREAMING_SNAKE_CASE_ : Optional[int] = intermediate_size
SCREAMING_SNAKE_CASE_ : Optional[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE_ : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE_ : Tuple = initializer_range
SCREAMING_SNAKE_CASE_ : List[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE_ : List[Any] = position_embedding_type
SCREAMING_SNAKE_CASE_ : Any = use_cache
SCREAMING_SNAKE_CASE_ : Optional[int] = classifier_dropout
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
@property
def __lowerCamelCase ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE_ : Optional[int] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE_ : Any = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 68
| 1
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Dict = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
SCREAMING_SNAKE_CASE : Dict = [
"small",
"small-base",
"medium",
"medium-base",
"intermediate",
"intermediate-base",
"large",
"large-base",
"xlarge",
"xlarge-base",
]
SCREAMING_SNAKE_CASE : int = {
"vocab_file": {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
"funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
"funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
"funnel-transformer/medium-base": (
"https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
),
"funnel-transformer/intermediate": (
"https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
),
"funnel-transformer/intermediate-base": (
"https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
),
"funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
"funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
"funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
"funnel-transformer/xlarge-base": (
"https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
"funnel-transformer/small-base": (
"https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
),
"funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
"funnel-transformer/medium-base": (
"https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
),
"funnel-transformer/intermediate": (
"https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
),
"funnel-transformer/intermediate-base": (
"https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
),
"funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
"funnel-transformer/large-base": (
"https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
),
"funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
"funnel-transformer/xlarge-base": (
"https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
),
},
}
SCREAMING_SNAKE_CASE : Any = {F'''funnel-transformer/{name}''': 512 for name in _model_names}
SCREAMING_SNAKE_CASE : Tuple = {F'''funnel-transformer/{name}''': {"do_lower_case": True} for name in _model_names}
class UpperCamelCase ( __a ):
a__ :Dict = VOCAB_FILES_NAMES
a__ :Dict = PRETRAINED_VOCAB_FILES_MAP
a__ :Dict = PRETRAINED_INIT_CONFIGURATION
a__ :str = FunnelTokenizer
a__ :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ :int = 2
def __init__(self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase="<unk>" , __UpperCamelCase="<sep>" , __UpperCamelCase="<pad>" , __UpperCamelCase="<cls>" , __UpperCamelCase="<mask>" , __UpperCamelCase="<s>" , __UpperCamelCase="</s>" , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase="##" , **__UpperCamelCase , ) -> List[Any]:
super().__init__(
__UpperCamelCase , tokenizer_file=__UpperCamelCase , do_lower_case=__UpperCamelCase , unk_token=__UpperCamelCase , sep_token=__UpperCamelCase , pad_token=__UpperCamelCase , cls_token=__UpperCamelCase , mask_token=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , clean_text=__UpperCamelCase , tokenize_chinese_chars=__UpperCamelCase , strip_accents=__UpperCamelCase , wordpieces_prefix=__UpperCamelCase , **__UpperCamelCase , )
UpperCamelCase_ : List[str] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , __UpperCamelCase ) != do_lower_case
or normalizer_state.get("""strip_accents""" , __UpperCamelCase ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , __UpperCamelCase ) != tokenize_chinese_chars
):
UpperCamelCase_ : List[Any] = getattr(__UpperCamelCase , normalizer_state.pop("""type""" ) )
UpperCamelCase_ : Union[str, Any] = do_lower_case
UpperCamelCase_ : Tuple = strip_accents
UpperCamelCase_ : Dict = tokenize_chinese_chars
UpperCamelCase_ : Union[str, Any] = normalizer_class(**__UpperCamelCase )
UpperCamelCase_ : Dict = do_lower_case
def A_ (self , __UpperCamelCase , __UpperCamelCase=None ) -> Dict:
UpperCamelCase_ : Any = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A_ (self , __UpperCamelCase , __UpperCamelCase = None ) -> List[int]:
UpperCamelCase_ : Optional[Any] = [self.sep_token_id]
UpperCamelCase_ : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def A_ (self , __UpperCamelCase , __UpperCamelCase = None ) -> Tuple[str]:
UpperCamelCase_ : int = self._tokenizer.model.save(__UpperCamelCase , name=__UpperCamelCase )
return tuple(__UpperCamelCase )
| 635
|
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
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Union[str, Any] = "▁"
SCREAMING_SNAKE_CASE : Dict = {"vocab_file": "spiece.model"}
SCREAMING_SNAKE_CASE : List[Any] = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
SCREAMING_SNAKE_CASE : Any = {
"google/reformer-crime-and-punishment": 524288,
}
class UpperCamelCase ( __a ):
a__ :Optional[Any] = VOCAB_FILES_NAMES
a__ :List[Any] = PRETRAINED_VOCAB_FILES_MAP
a__ :Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ :List[Any] = ['''input_ids''', '''attention_mask''']
def __init__(self , __UpperCamelCase , __UpperCamelCase="</s>" , __UpperCamelCase="<unk>" , __UpperCamelCase=[] , __UpperCamelCase = None , **__UpperCamelCase , ) -> None:
UpperCamelCase_ : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=__UpperCamelCase , unk_token=__UpperCamelCase , additional_special_tokens=__UpperCamelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCamelCase , )
UpperCamelCase_ : int = vocab_file
UpperCamelCase_ : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCamelCase )
@property
def A_ (self ) -> Dict:
return self.sp_model.get_piece_size()
def A_ (self ) -> Dict[str, int]:
UpperCamelCase_ : str = {self.convert_ids_to_tokens(__UpperCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__(self ) -> Any:
UpperCamelCase_ : Dict = self.__dict__.copy()
UpperCamelCase_ : List[str] = None
return state
def __setstate__(self , __UpperCamelCase ) -> List[Any]:
UpperCamelCase_ : List[Any] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCamelCase_ : Any = {}
UpperCamelCase_ : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def A_ (self , __UpperCamelCase ) -> List[str]:
return self.sp_model.encode(__UpperCamelCase , out_type=__UpperCamelCase )
def A_ (self , __UpperCamelCase ) -> Union[str, Any]:
return self.sp_model.piece_to_id(__UpperCamelCase )
def A_ (self , __UpperCamelCase ) -> Dict:
if index < self.sp_model.get_piece_size():
UpperCamelCase_ : Union[str, Any] = self.sp_model.IdToPiece(__UpperCamelCase )
return token
def A_ (self , __UpperCamelCase ) -> int:
UpperCamelCase_ : Dict = []
UpperCamelCase_ : Union[str, Any] = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(__UpperCamelCase ) + token
UpperCamelCase_ : Optional[Any] = []
else:
current_sub_tokens.append(__UpperCamelCase )
out_string += self.sp_model.decode(__UpperCamelCase )
return out_string.strip()
def A_ (self , __UpperCamelCase , __UpperCamelCase = None ) -> Tuple[str]:
if not os.path.isdir(__UpperCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase_ : Tuple = os.path.join(
__UpperCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCamelCase , """wb""" ) as fi:
UpperCamelCase_ : int = self.sp_model.serialized_model_proto()
fi.write(__UpperCamelCase )
return (out_vocab_file,)
| 635
| 1
|
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self : Optional[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict=7 , UpperCAmelCase__ : Dict=3 , UpperCAmelCase__ : Optional[int]=30 , UpperCAmelCase__ : Any=400 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : int=None , UpperCAmelCase__ : List[Any]=True , UpperCAmelCase__ : Union[str, Any]=[0.5, 0.5, 0.5] , UpperCAmelCase__ : str=[0.5, 0.5, 0.5] , UpperCAmelCase__ : Any=True , UpperCAmelCase__ : Optional[int]=1 / 255 , UpperCAmelCase__ : List[str]=True , ):
'''simple docstring'''
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowercase : str =size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
lowercase : str =parent
lowercase : Dict =batch_size
lowercase : Union[str, Any] =num_channels
lowercase : int =min_resolution
lowercase : str =max_resolution
lowercase : List[str] =do_resize
lowercase : Dict =size
lowercase : Optional[Any] =do_normalize
lowercase : List[Any] =image_mean
lowercase : Optional[int] =image_std
lowercase : Optional[int] =do_rescale
lowercase : int =rescale_factor
lowercase : int =do_pad
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def lowerCamelCase_ ( self : str , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[Any]=False ):
'''simple docstring'''
if not batched:
lowercase : List[str] =image_inputs[0]
if isinstance(UpperCAmelCase__ , Image.Image ):
lowercase , lowercase : str =image.size
else:
lowercase , lowercase : List[str] =image.shape[1], image.shape[2]
if w < h:
lowercase : Optional[Any] =int(self.size['''shortest_edge'''] * h / w )
lowercase : Dict =self.size['''shortest_edge''']
elif w > h:
lowercase : str =self.size['''shortest_edge''']
lowercase : Optional[int] =int(self.size['''shortest_edge'''] * w / h )
else:
lowercase : str =self.size['''shortest_edge''']
lowercase : Union[str, Any] =self.size['''shortest_edge''']
else:
lowercase : Union[str, Any] =[]
for image in image_inputs:
lowercase , lowercase : Any =self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase : Optional[int] =max(UpperCAmelCase__ , key=lambda UpperCAmelCase__ : item[0] )[0]
lowercase : Dict =max(UpperCAmelCase__ , key=lambda UpperCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( lowercase__ , unittest.TestCase ):
lowerCamelCase_ = ConditionalDetrImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : Any =ConditionalDetrImageProcessingTester(self )
@property
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Optional[Any] =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase__ , '''image_mean''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''image_std''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''do_normalize''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''do_resize''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''size''' ) )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
lowercase : List[Any] =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , UpperCAmelCase__ )
lowercase : str =self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCAmelCase__ )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
# Initialize image_processing
lowercase : Union[str, Any] =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase : List[Any] =prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , Image.Image )
# Test not batched input
lowercase : int =image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase , lowercase : Optional[int] =self.image_processor_tester.get_expected_values(UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase , lowercase : Optional[int] =self.image_processor_tester.get_expected_values(UpperCAmelCase__ , batched=UpperCAmelCase__ )
lowercase : Any =image_processing(UpperCAmelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
# Initialize image_processing
lowercase : Dict =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase : Union[str, Any] =prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , numpify=UpperCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , np.ndarray )
# Test not batched input
lowercase : List[str] =image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase , lowercase : Dict =self.image_processor_tester.get_expected_values(UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase : Optional[Any] =image_processing(UpperCAmelCase__ , return_tensors='''pt''' ).pixel_values
lowercase , lowercase : Optional[int] =self.image_processor_tester.get_expected_values(UpperCAmelCase__ , batched=UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
# Initialize image_processing
lowercase : Optional[int] =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase : Dict =prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , torchify=UpperCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , torch.Tensor )
# Test not batched input
lowercase : int =image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase , lowercase : List[str] =self.image_processor_tester.get_expected_values(UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase : Any =image_processing(UpperCAmelCase__ , return_tensors='''pt''' ).pixel_values
lowercase , lowercase : Optional[int] =self.image_processor_tester.get_expected_values(UpperCAmelCase__ , batched=UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
# prepare image and target
lowercase : Optional[Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
lowercase : Tuple =json.loads(f.read() )
lowercase : List[str] ={'''image_id''': 39769, '''annotations''': target}
# encode them
lowercase : Optional[Any] =ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''' )
lowercase : Dict =image_processing(images=UpperCAmelCase__ , annotations=UpperCAmelCase__ , return_tensors='''pt''' )
# verify pixel values
lowercase : Union[str, Any] =torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCAmelCase__ )
lowercase : str =torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCAmelCase__ , atol=1E-4 ) )
# verify area
lowercase : int =torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCAmelCase__ ) )
# verify boxes
lowercase : List[Any] =torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCAmelCase__ )
lowercase : List[str] =torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCAmelCase__ , atol=1E-3 ) )
# verify image_id
lowercase : Tuple =torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCAmelCase__ ) )
# verify is_crowd
lowercase : Tuple =torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCAmelCase__ ) )
# verify class_labels
lowercase : Union[str, Any] =torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCAmelCase__ ) )
# verify orig_size
lowercase : int =torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCAmelCase__ ) )
# verify size
lowercase : List[str] =torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCAmelCase__ ) )
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
# prepare image, target and masks_path
lowercase : List[Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
lowercase : Dict =json.loads(f.read() )
lowercase : List[str] ={'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
lowercase : Any =pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
lowercase : Union[str, Any] =ConditionalDetrImageProcessor(format='''coco_panoptic''' )
lowercase : Optional[Any] =image_processing(images=UpperCAmelCase__ , annotations=UpperCAmelCase__ , masks_path=UpperCAmelCase__ , return_tensors='''pt''' )
# verify pixel values
lowercase : List[Any] =torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCAmelCase__ )
lowercase : int =torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCAmelCase__ , atol=1E-4 ) )
# verify area
lowercase : int =torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCAmelCase__ ) )
# verify boxes
lowercase : str =torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCAmelCase__ )
lowercase : List[Any] =torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCAmelCase__ , atol=1E-3 ) )
# verify image_id
lowercase : Union[str, Any] =torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCAmelCase__ ) )
# verify is_crowd
lowercase : Dict =torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCAmelCase__ ) )
# verify class_labels
lowercase : Any =torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCAmelCase__ ) )
# verify masks
lowercase : Optional[Any] =822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , UpperCAmelCase__ )
# verify orig_size
lowercase : Optional[Any] =torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCAmelCase__ ) )
# verify size
lowercase : Optional[int] =torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCAmelCase__ ) )
| 88
|
'''simple docstring'''
import math
def _lowerCAmelCase ( __magic_name__ : float , __magic_name__ : float ) -> float:
if (
not isinstance(__magic_name__ , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('''power_factor must be a valid float value between -1 and 1.''' )
return apparent_power * power_factor
def _lowerCAmelCase ( __magic_name__ : float , __magic_name__ : float ) -> float:
if (
not isinstance(__magic_name__ , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('''power_factor must be a valid float value between -1 and 1.''' )
return apparent_power * math.sqrt(1 - power_factor**2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 88
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
lowerCAmelCase: List[Any] = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}
class a__( lowerCamelCase__ ):
lowercase__ = """albert"""
def __init__( self : Dict , __snake_case : str=3_00_00 , __snake_case : Any=1_28 , __snake_case : List[str]=40_96 , __snake_case : List[str]=12 , __snake_case : Dict=1 , __snake_case : str=64 , __snake_case : Union[str, Any]=1_63_84 , __snake_case : Optional[int]=1 , __snake_case : Union[str, Any]="gelu_new" , __snake_case : Union[str, Any]=0 , __snake_case : Any=0 , __snake_case : Tuple=5_12 , __snake_case : int=2 , __snake_case : Tuple=0.02 , __snake_case : List[Any]=1e-1_2 , __snake_case : Optional[Any]=0.1 , __snake_case : Tuple="absolute" , __snake_case : Union[str, Any]=0 , __snake_case : List[Any]=2 , __snake_case : List[Any]=3 , **__snake_case : int , ):
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
a : Any = vocab_size
a : List[str] = embedding_size
a : Any = hidden_size
a : Union[str, Any] = num_hidden_layers
a : Optional[int] = num_hidden_groups
a : Tuple = num_attention_heads
a : Union[str, Any] = inner_group_num
a : List[Any] = hidden_act
a : Optional[Any] = intermediate_size
a : Any = hidden_dropout_prob
a : Any = attention_probs_dropout_prob
a : List[Any] = max_position_embeddings
a : Optional[int] = type_vocab_size
a : int = initializer_range
a : Tuple = layer_norm_eps
a : Optional[Any] = classifier_dropout_prob
a : List[str] = position_embedding_type
class a__( lowerCamelCase__ ):
@property
def lowercase_ ( self : Optional[int] ):
if self.task == "multiple-choice":
a : Union[str, Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
a : Any = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 526
|
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase: Tuple = logging.get_logger(__name__)
lowerCAmelCase: Dict = '▁'
lowerCAmelCase: Dict = {
'vocab_file': 'vocab.json',
'spm_file': 'sentencepiece.bpe.model',
}
lowerCAmelCase: int = {
'vocab_file': {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
),
},
'spm_file': {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
)
},
}
lowerCAmelCase: Any = {
'facebook/s2t-small-librispeech-asr': 1_0_2_4,
}
lowerCAmelCase: Optional[int] = ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']
lowerCAmelCase: List[Any] = {'mustc': MUSTC_LANGS}
class a__( lowerCamelCase__ ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = MAX_MODEL_INPUT_SIZES
lowercase__ = ["""input_ids""", """attention_mask"""]
lowercase__ = []
def __init__( self : int , __snake_case : int , __snake_case : Optional[Any] , __snake_case : Optional[Any]="<s>" , __snake_case : Optional[int]="</s>" , __snake_case : Union[str, Any]="<pad>" , __snake_case : str="<unk>" , __snake_case : Dict=False , __snake_case : int=False , __snake_case : str=None , __snake_case : Optional[int]=None , __snake_case : Optional[Dict[str, Any]] = None , **__snake_case : Union[str, Any] , ):
a : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__snake_case , eos_token=__snake_case , unk_token=__snake_case , pad_token=__snake_case , do_upper_case=__snake_case , do_lower_case=__snake_case , tgt_lang=__snake_case , lang_codes=__snake_case , sp_model_kwargs=self.sp_model_kwargs , **__snake_case , )
a : Tuple = do_upper_case
a : Optional[Any] = do_lower_case
a : List[str] = load_json(__snake_case )
a : Dict = {v: k for k, v in self.encoder.items()}
a : int = spm_file
a : Tuple = load_spm(__snake_case , self.sp_model_kwargs )
if lang_codes is not None:
a : Any = lang_codes
a : str = LANGUAGES[lang_codes]
a : Tuple = [F"""<lang:{lang}>""" for lang in self.langs]
a : str = {lang: self.sp_model.PieceToId(F"""<lang:{lang}>""" ) for lang in self.langs}
a : Optional[Any] = self.lang_tokens
a : Union[str, Any] = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
a : List[str] = {}
@property
def lowercase_ ( self : Optional[Any] ):
return len(self.encoder )
@property
def lowercase_ ( self : int ):
return self._tgt_lang
@tgt_lang.setter
def lowercase_ ( self : int , __snake_case : Optional[int] ):
a : Union[str, Any] = new_tgt_lang
self.set_tgt_lang_special_tokens(__snake_case )
def lowercase_ ( self : str , __snake_case : str ):
a : int = self.lang_code_to_id[tgt_lang]
a : int = [lang_code_id]
def lowercase_ ( self : Optional[int] , __snake_case : str ):
return self.sp_model.encode(__snake_case , out_type=__snake_case )
def lowercase_ ( self : List[str] , __snake_case : List[Any] ):
return self.encoder.get(__snake_case , self.encoder[self.unk_token] )
def lowercase_ ( self : List[Any] , __snake_case : int ):
return self.decoder.get(__snake_case , self.unk_token )
def lowercase_ ( self : Union[str, Any] , __snake_case : List[str] ):
a : List[Any] = []
a : List[Any] = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
a : Union[str, Any] = self.sp_model.decode(__snake_case )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
a : Optional[int] = []
else:
current_sub_tokens.append(__snake_case )
a : Tuple = self.sp_model.decode(__snake_case )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def lowercase_ ( self : int , __snake_case : List[Any] , __snake_case : List[str]=None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def lowercase_ ( self : Optional[int] , __snake_case : List[int] , __snake_case : Optional[List[int]] = None , __snake_case : bool = False ):
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 )
a : Optional[int] = [1] * len(self.prefix_tokens )
a : Any = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(__snake_case )) + suffix_ones
return prefix_ones + ([0] * len(__snake_case )) + ([0] * len(__snake_case )) + suffix_ones
def lowercase_ ( self : Union[str, Any] ):
a : Union[str, Any] = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Any ):
a : List[str] = self.__dict__.copy()
a : Union[str, Any] = None
return state
def __setstate__( self : str , __snake_case : Dict ):
a : Dict = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
a : int = {}
a : Any = load_spm(self.spm_file , self.sp_model_kwargs )
def lowercase_ ( self : str , __snake_case : str , __snake_case : Optional[str] = None ):
a : Union[str, Any] = Path(__snake_case )
assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
a : Any = save_dir / (
(filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
)
a : List[Any] = save_dir / (
(filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
)
save_json(self.encoder , __snake_case )
if os.path.abspath(self.spm_file ) != os.path.abspath(__snake_case ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , __snake_case )
elif not os.path.isfile(self.spm_file ):
with open(__snake_case , 'wb' ) as fi:
a : Tuple = self.sp_model.serialized_model_proto()
fi.write(__snake_case )
return (str(__snake_case ), str(__snake_case ))
def lowerCamelCase__ ( _A , _A ):
a : List[Any] = sentencepiece.SentencePieceProcessor(**_A )
spm.Load(str(_A ) )
return spm
def lowerCamelCase__ ( _A ):
with open(_A , 'r' ) as f:
return json.load(_A )
def lowerCamelCase__ ( _A , _A ):
with open(_A , 'w' ) as f:
json.dump(_A , _A , indent=2 )
| 526
| 1
|
'''simple docstring'''
from __future__ import annotations
lowerCAmelCase__ : Union[str, Any] = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _a ( __lowerCAmelCase : list[list[int]] , __lowerCAmelCase : list[int] , __lowerCAmelCase : list[int] , __lowerCAmelCase : int , __lowerCAmelCase : list[list[int]] , ):
"""simple docstring"""
snake_case__ : Tuple = [
[0 for col in range(len(grid[0] ) )] for row in range(len(__lowerCAmelCase ) )
] # the reference grid
snake_case__ : str = 1
snake_case__ : Tuple = [
[0 for col in range(len(grid[0] ) )] for row in range(len(__lowerCAmelCase ) )
] # the action grid
snake_case__ : Optional[int] = init[0]
snake_case__ : int = init[1]
snake_case__ : Dict = 0
snake_case__ : Optional[int] = g + heuristic[x][y] # cost from starting cell to destination cell
snake_case__ : List[Any] = [[f, g, x, y]]
snake_case__ : Dict = False # flag that is set when search is complete
snake_case__ : int = False # flag set if we can't find expand
while not found and not resign:
if len(__lowerCAmelCase ) == 0:
raise ValueError('''Algorithm is unable to find solution''' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
snake_case__ : Union[str, Any] = cell.pop()
snake_case__ : str = next_cell[2]
snake_case__ : Union[str, Any] = next_cell[3]
snake_case__ : List[str] = next_cell[1]
if x == goal[0] and y == goal[1]:
snake_case__ : Optional[int] = True
else:
for i in range(len(__lowerCAmelCase ) ): # to try out different valid actions
snake_case__ : str = x + DIRECTIONS[i][0]
snake_case__ : Tuple = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(__lowerCAmelCase ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
snake_case__ : Union[str, Any] = g + cost
snake_case__ : str = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
snake_case__ : Union[str, Any] = 1
snake_case__ : Union[str, Any] = i
snake_case__ : Union[str, Any] = []
snake_case__ : Union[str, Any] = goal[0]
snake_case__ : Any = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
snake_case__ : Dict = x - DIRECTIONS[action[x][y]][0]
snake_case__ : Optional[int] = y - DIRECTIONS[action[x][y]][1]
snake_case__ : Union[str, Any] = xa
snake_case__ : List[Any] = ya
invpath.append([x, y] )
snake_case__ : Optional[int] = []
for i in range(len(__lowerCAmelCase ) ):
path.append(invpath[len(__lowerCAmelCase ) - 1 - i] )
return path, action
if __name__ == "__main__":
lowerCAmelCase__ : List[Any] = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
lowerCAmelCase__ : Union[str, Any] = [0, 0]
# all coordinates are given in format [y,x]
lowerCAmelCase__ : Optional[Any] = [len(grid) - 1, len(grid[0]) - 1]
lowerCAmelCase__ : Optional[int] = 1
# the cost map which pushes the path closer to the goal
lowerCAmelCase__ : Optional[Any] = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
lowerCAmelCase__ : List[str] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
lowerCAmelCase__ : int = 99
lowerCAmelCase__ , lowerCAmelCase__ : Any = search(grid, init, goal, cost, heuristic)
print("""ACTION MAP""")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 502
|
'''simple docstring'''
def _a ( __lowerCAmelCase : float , __lowerCAmelCase : float , __lowerCAmelCase : int ):
"""simple docstring"""
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >= 0''' )
if years_to_repay <= 0 or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise Exception('''Years to repay must be an integer > 0''' )
# Yearly rate is divided by 12 to get monthly rate
snake_case__ : int = rate_per_annum / 12
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
snake_case__ : Tuple = years_to_repay * 12
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 502
| 1
|
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __magic_name__ ( lowerCAmelCase ):
def lowerCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase : int =self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(snake_case , 'embed_dim'))
self.parent.assertTrue(hasattr(snake_case , 'num_heads'))
class __magic_name__ :
def __init__( self , snake_case , snake_case=1_3 , snake_case=6_4 , snake_case=3 , snake_case=[1_6, 4_8, 9_6] , snake_case=[1, 3, 6] , snake_case=[1, 2, 1_0] , snake_case=[7, 3, 3] , snake_case=[4, 2, 2] , snake_case=[2, 1, 1] , snake_case=[2, 2, 2] , snake_case=[False, False, True] , snake_case=[0.0, 0.0, 0.0] , snake_case=0.02 , snake_case=1E-1_2 , snake_case=True , snake_case=True , snake_case=2 , ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Dict =parent
_UpperCAmelCase : Tuple =batch_size
_UpperCAmelCase : List[str] =image_size
_UpperCAmelCase : Tuple =patch_sizes
_UpperCAmelCase : Any =patch_stride
_UpperCAmelCase : Optional[int] =patch_padding
_UpperCAmelCase : Tuple =is_training
_UpperCAmelCase : Any =use_labels
_UpperCAmelCase : Tuple =num_labels
_UpperCAmelCase : int =num_channels
_UpperCAmelCase : List[Any] =embed_dim
_UpperCAmelCase : List[Any] =num_heads
_UpperCAmelCase : Optional[int] =stride_kv
_UpperCAmelCase : Optional[Any] =depth
_UpperCAmelCase : Optional[int] =cls_token
_UpperCAmelCase : List[Any] =attention_drop_rate
_UpperCAmelCase : List[str] =initializer_range
_UpperCAmelCase : Tuple =layer_norm_eps
def lowerCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCAmelCase : str =None
if self.use_labels:
# create a random int32 tensor of given shape
_UpperCAmelCase : Optional[Any] =ids_tensor([self.batch_size] , self.num_labels)
_UpperCAmelCase : Dict =self.get_config()
return config, pixel_values, labels
def lowerCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCAmelCase ( self , snake_case , snake_case , snake_case) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : str =TFCvtModel(config=snake_case)
_UpperCAmelCase : Optional[int] =model(snake_case , training=snake_case)
_UpperCAmelCase : Union[str, Any] =(self.image_size, self.image_size)
_UpperCAmelCase , _UpperCAmelCase : List[Any] =image_size[0], image_size[1]
for i in range(len(self.depth)):
_UpperCAmelCase : Optional[int] =floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
_UpperCAmelCase : Optional[Any] =floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width))
def lowerCAmelCase ( self , snake_case , snake_case , snake_case) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : List[str] =self.num_labels
_UpperCAmelCase : Optional[int] =TFCvtForImageClassification(snake_case)
_UpperCAmelCase : List[Any] =model(snake_case , labels=snake_case , training=snake_case)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def lowerCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Any =config_and_inputs
_UpperCAmelCase : List[Any] ={'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class __magic_name__ ( lowerCAmelCase ,lowerCAmelCase ,unittest.TestCase ):
UpperCAmelCase =(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
UpperCAmelCase =(
{"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
if is_tf_available()
else {}
)
UpperCAmelCase =False
UpperCAmelCase =False
UpperCAmelCase =False
UpperCAmelCase =False
UpperCAmelCase =False
def lowerCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =TFCvtModelTester(self)
_UpperCAmelCase : Tuple =TFCvtConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=3_7)
def lowerCAmelCase ( self) -> List[str]:
'''simple docstring'''
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason='Cvt does not output attentions')
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
pass
@unittest.skip(reason='Cvt does not use inputs_embeds')
def lowerCAmelCase ( self) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='Cvt does not support input and output embeddings')
def lowerCAmelCase ( self) -> List[str]:
'''simple docstring'''
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('GPU')) == 0 , reason='TF does not support backprop for grouped convolutions on CPU.' , )
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('GPU')) == 0 , reason='TF does not support backprop for grouped convolutions on CPU.' , )
@slow
def lowerCAmelCase ( self) -> str:
'''simple docstring'''
super().test_keras_fit()
@unittest.skip(reason='Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8')
def lowerCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =tf.keras.mixed_precision.Policy('mixed_float16')
tf.keras.mixed_precision.set_global_policy(snake_case)
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy('float32')
def lowerCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : Dict =model_class(snake_case)
_UpperCAmelCase : Tuple =inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase : List[Any] =[*signature.parameters.keys()]
_UpperCAmelCase : Optional[Any] =['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case)
def lowerCAmelCase ( self) -> List[Any]:
'''simple docstring'''
def check_hidden_states_output(snake_case , snake_case , snake_case):
_UpperCAmelCase : Dict =model_class(snake_case)
_UpperCAmelCase : List[Any] =model(**self._prepare_for_class(snake_case , snake_case))
_UpperCAmelCase : int =outputs.hidden_states
_UpperCAmelCase : Optional[Any] =len(self.model_tester.depth)
self.assertEqual(len(snake_case) , snake_case)
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:]) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase , _UpperCAmelCase : List[str] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : Dict =True
check_hidden_states_output(snake_case , snake_case , snake_case)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase : List[Any] =True
check_hidden_states_output(snake_case , snake_case , snake_case)
def lowerCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case)
def lowerCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case)
@slow
def lowerCAmelCase ( self) -> str:
'''simple docstring'''
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[Any] =TFCvtModel.from_pretrained(snake_case)
self.assertIsNotNone(snake_case)
def lowerCamelCase__ ( ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class __magic_name__ ( unittest.TestCase ):
@cached_property
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])
@slow
def lowerCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : Any =TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])
_UpperCAmelCase : Union[str, Any] =self.default_image_processor
_UpperCAmelCase : Dict =prepare_img()
_UpperCAmelCase : Tuple =image_processor(images=snake_case , return_tensors='tf')
# forward pass
_UpperCAmelCase : Optional[Any] =model(**snake_case)
# verify the logits
_UpperCAmelCase : int =tf.TensorShape((1, 1_0_0_0))
self.assertEqual(outputs.logits.shape , snake_case)
_UpperCAmelCase : Dict =tf.constant([0.92_85, 0.90_15, -0.31_50])
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , snake_case , atol=1E-4))
| 446
|
'''simple docstring'''
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 __magic_name__ ( lowerCAmelCase ,unittest.TestCase ):
UpperCAmelCase =KandinskyImgaImgPipeline
UpperCAmelCase =["prompt", "image_embeds", "negative_image_embeds", "image"]
UpperCAmelCase =[
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
]
UpperCAmelCase =[
"generator",
"height",
"width",
"strength",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
UpperCAmelCase =False
@property
def lowerCAmelCase ( self) -> str:
'''simple docstring'''
return 3_2
@property
def lowerCAmelCase ( self) -> List[str]:
'''simple docstring'''
return 3_2
@property
def lowerCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
return self.time_input_dim
@property
def lowerCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCAmelCase ( self) -> Tuple:
'''simple docstring'''
return 1_0_0
@property
def lowerCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : int =XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
return tokenizer
@property
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
torch.manual_seed(0)
_UpperCAmelCase : Optional[Any] =MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
_UpperCAmelCase : List[Any] =MultilingualCLIP(snake_case)
_UpperCAmelCase : List[Any] =text_encoder.eval()
return text_encoder
@property
def lowerCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0)
_UpperCAmelCase : Union[str, Any] ={
'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 : Tuple =UNetaDConditionModel(**snake_case)
return model
@property
def lowerCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return {
"block_out_channels": [3_2, 6_4],
"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": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCAmelCase ( self) -> int:
'''simple docstring'''
torch.manual_seed(0)
_UpperCAmelCase : List[Any] =VQModel(**self.dummy_movq_kwargs)
return model
def lowerCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase : Tuple =self.dummy_text_encoder
_UpperCAmelCase : Any =self.dummy_tokenizer
_UpperCAmelCase : List[str] =self.dummy_unet
_UpperCAmelCase : Union[str, Any] =self.dummy_movq
_UpperCAmelCase : List[str] ={
'num_train_timesteps': 1_0_0_0,
'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,
}
_UpperCAmelCase : Dict =DDIMScheduler(**snake_case)
_UpperCAmelCase : Optional[int] ={
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def lowerCAmelCase ( self , snake_case , snake_case=0) -> int:
'''simple docstring'''
_UpperCAmelCase : List[Any] =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(snake_case)).to(snake_case)
_UpperCAmelCase : Tuple =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1)).to(snake_case)
# create init_image
_UpperCAmelCase : Dict =floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(snake_case)).to(snake_case)
_UpperCAmelCase : Optional[int] =image.cpu().permute(0 , 2 , 3 , 1)[0]
_UpperCAmelCase : List[Any] =Image.fromarray(np.uinta(snake_case)).convert('RGB').resize((2_5_6, 2_5_6))
if str(snake_case).startswith('mps'):
_UpperCAmelCase : str =torch.manual_seed(snake_case)
else:
_UpperCAmelCase : Optional[Any] =torch.Generator(device=snake_case).manual_seed(snake_case)
_UpperCAmelCase : str ={
'prompt': 'horse',
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 6_4,
'width': 6_4,
'num_inference_steps': 1_0,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def lowerCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCAmelCase : List[Any] ='cpu'
_UpperCAmelCase : Dict =self.get_dummy_components()
_UpperCAmelCase : Optional[Any] =self.pipeline_class(**snake_case)
_UpperCAmelCase : List[str] =pipe.to(snake_case)
pipe.set_progress_bar_config(disable=snake_case)
_UpperCAmelCase : List[Any] =pipe(**self.get_dummy_inputs(snake_case))
_UpperCAmelCase : List[str] =output.images
_UpperCAmelCase : Any =pipe(
**self.get_dummy_inputs(snake_case) , return_dict=snake_case , )[0]
_UpperCAmelCase : Any =image[0, -3:, -3:, -1]
_UpperCAmelCase : Tuple =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
_UpperCAmelCase : List[Any] =np.array(
[0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33])
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 __magic_name__ ( unittest.TestCase ):
def lowerCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Tuple =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_img2img_frog.npy')
_UpperCAmelCase : List[Any] =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
_UpperCAmelCase : Union[str, Any] ='A red cartoon frog, 4k'
_UpperCAmelCase : Union[str, Any] =KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa)
pipe_prior.to(snake_case)
_UpperCAmelCase : Optional[Any] =KandinskyImgaImgPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1' , torch_dtype=torch.floataa)
_UpperCAmelCase : Tuple =pipeline.to(snake_case)
pipeline.set_progress_bar_config(disable=snake_case)
_UpperCAmelCase : List[str] =torch.Generator(device='cpu').manual_seed(0)
_UpperCAmelCase , _UpperCAmelCase : List[Any] =pipe_prior(
snake_case , generator=snake_case , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
_UpperCAmelCase : str =pipeline(
snake_case , image=snake_case , image_embeds=snake_case , negative_image_embeds=snake_case , generator=snake_case , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , strength=0.2 , output_type='np' , )
_UpperCAmelCase : Any =output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(snake_case , snake_case)
| 446
| 1
|
'''simple docstring'''
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
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=64 , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=5 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=37 , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=5_12 , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=0.0_2 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=None , ) -> Dict:
__lowerCAmelCase : List[Any] = parent
__lowerCAmelCase : Dict = batch_size
__lowerCAmelCase : Any = seq_length
__lowerCAmelCase : int = is_training
__lowerCAmelCase : Tuple = use_input_mask
__lowerCAmelCase : Tuple = use_token_type_ids
__lowerCAmelCase : Dict = use_labels
__lowerCAmelCase : List[Any] = vocab_size
__lowerCAmelCase : Tuple = hidden_size
__lowerCAmelCase : Dict = embedding_size
__lowerCAmelCase : Union[str, Any] = num_hidden_layers
__lowerCAmelCase : Any = num_attention_heads
__lowerCAmelCase : str = intermediate_size
__lowerCAmelCase : Optional[Any] = hidden_act
__lowerCAmelCase : Union[str, Any] = hidden_dropout_prob
__lowerCAmelCase : Optional[int] = attention_probs_dropout_prob
__lowerCAmelCase : str = max_position_embeddings
__lowerCAmelCase : Tuple = type_vocab_size
__lowerCAmelCase : int = type_sequence_label_size
__lowerCAmelCase : List[Any] = initializer_range
__lowerCAmelCase : List[Any] = num_labels
__lowerCAmelCase : Union[str, Any] = num_choices
__lowerCAmelCase : int = scope
def snake_case ( self ) -> Any:
__lowerCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase : Union[str, Any] = None
if self.use_input_mask:
__lowerCAmelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase : Union[str, Any] = None
if self.use_token_type_ids:
__lowerCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase : List[str] = None
__lowerCAmelCase : int = None
__lowerCAmelCase : Dict = None
if self.use_labels:
__lowerCAmelCase : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCAmelCase : str = ids_tensor([self.batch_size] , self.num_choices )
__lowerCAmelCase : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case ( self ) -> int:
return 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 , embedding_size=self.embedding_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=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )
def snake_case ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Any:
__lowerCAmelCase : Optional[Any] = MobileBertModel(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : List[Any] = model(SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = model(SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE )
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 snake_case ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Any:
__lowerCAmelCase : List[Any] = MobileBertForMaskedLM(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : Tuple = model(SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
__lowerCAmelCase : Optional[Any] = MobileBertForNextSentencePrediction(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : Optional[int] = model(
SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def snake_case ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[int]:
__lowerCAmelCase : Tuple = MobileBertForPreTraining(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : Optional[int] = model(
SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE , next_sentence_label=SCREAMING_SNAKE_CASE , )
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 snake_case ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[int]:
__lowerCAmelCase : Dict = MobileBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : str = model(
SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , start_positions=SCREAMING_SNAKE_CASE , end_positions=SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
__lowerCAmelCase : List[str] = self.num_labels
__lowerCAmelCase : List[Any] = MobileBertForSequenceClassification(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
__lowerCAmelCase : List[Any] = self.num_labels
__lowerCAmelCase : Any = MobileBertForTokenClassification(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
__lowerCAmelCase : Any = self.num_choices
__lowerCAmelCase : List[Any] = MobileBertForMultipleChoice(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : Tuple = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase : Optional[int] = 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 : Optional[int] = model(
SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case ( self ) -> List[Any]:
__lowerCAmelCase : Any = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) : Optional[int] = config_and_inputs
__lowerCAmelCase : Optional[Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase__ ( a , a , unittest.TestCase ):
'''simple docstring'''
_snake_case = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_snake_case = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_snake_case = True
def snake_case ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ) -> Tuple:
__lowerCAmelCase : Tuple = super()._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , return_labels=SCREAMING_SNAKE_CASE )
if return_labels:
if model_class in get_values(SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Any = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE )
return inputs_dict
def snake_case ( self ) -> Optional[int]:
__lowerCAmelCase : Optional[Any] = MobileBertModelTester(self )
__lowerCAmelCase : str = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE , hidden_size=37 )
def snake_case ( self ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def snake_case ( self ) -> Any:
__lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*SCREAMING_SNAKE_CASE )
def snake_case ( self ) -> List[Any]:
__lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*SCREAMING_SNAKE_CASE )
def snake_case ( self ) -> Tuple:
__lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*SCREAMING_SNAKE_CASE )
def snake_case ( self ) -> Optional[Any]:
__lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*SCREAMING_SNAKE_CASE )
def snake_case ( self ) -> Any:
__lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*SCREAMING_SNAKE_CASE )
def snake_case ( self ) -> Optional[Any]:
__lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*SCREAMING_SNAKE_CASE )
def snake_case ( self ) -> List[Any]:
__lowerCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*SCREAMING_SNAKE_CASE )
def snake_case ( self ) -> Dict:
__lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*SCREAMING_SNAKE_CASE )
def A ( _UpperCAmelCase : Tuple ) -> Union[str, Any]:
'''simple docstring'''
return torch.tensor(
_UpperCAmelCase ,dtype=torch.long ,device=_UpperCAmelCase ,)
A_ = 1e-3
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case ( self ) -> List[Any]:
__lowerCAmelCase : str = MobileBertModel.from_pretrained('google/mobilebert-uncased' ).to(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]] )
with torch.no_grad():
__lowerCAmelCase : List[str] = model(SCREAMING_SNAKE_CASE )[0]
__lowerCAmelCase : Optional[Any] = torch.Size((1, 9, 5_12) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = torch.tensor(
[
[
[-2.4_73_65_26e07, 8.2_69_16_56e04, 1.6_52_18_38e05],
[-5.7_54_17_04e-01, 3.9_05_60_22e00, 4.4_01_15_07e00],
[2.6_04_73_59e00, 1.5_67_76_52e00, -1.7_32_41_88e-01],
]
] , device=SCREAMING_SNAKE_CASE , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
__lowerCAmelCase : List[str] = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
__lowerCAmelCase : Optional[Any] = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 123
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
class UpperCamelCase__ ( a ):
'''simple docstring'''
_snake_case = '''gpt_bigcode'''
_snake_case = ['''past_key_values''']
_snake_case = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , SCREAMING_SNAKE_CASE=5_02_57 , SCREAMING_SNAKE_CASE=10_24 , SCREAMING_SNAKE_CASE=7_68 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE="gelu_pytorch_tanh" , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=1e-5 , SCREAMING_SNAKE_CASE=0.0_2 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=5_02_56 , SCREAMING_SNAKE_CASE=5_02_56 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , **SCREAMING_SNAKE_CASE , ) -> Optional[Any]:
__lowerCAmelCase : str = vocab_size
__lowerCAmelCase : List[Any] = n_positions
__lowerCAmelCase : List[Any] = n_embd
__lowerCAmelCase : Optional[Any] = n_layer
__lowerCAmelCase : Any = n_head
__lowerCAmelCase : int = n_inner
__lowerCAmelCase : str = activation_function
__lowerCAmelCase : Dict = resid_pdrop
__lowerCAmelCase : Dict = embd_pdrop
__lowerCAmelCase : Union[str, Any] = attn_pdrop
__lowerCAmelCase : Dict = layer_norm_epsilon
__lowerCAmelCase : Union[str, Any] = initializer_range
__lowerCAmelCase : int = scale_attn_weights
__lowerCAmelCase : Dict = use_cache
__lowerCAmelCase : Tuple = attention_softmax_in_fpaa
__lowerCAmelCase : Tuple = scale_attention_softmax_in_fpaa
__lowerCAmelCase : Optional[Any] = multi_query
__lowerCAmelCase : List[str] = bos_token_id
__lowerCAmelCase : Optional[Any] = eos_token_id
super().__init__(bos_token_id=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
| 123
| 1
|
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import GLPNImageProcessor
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Any ,A : Optional[int] ,A : Optional[int]=7 ,A : Optional[Any]=3 ,A : List[str]=18 ,A : Any=30 ,A : Tuple=4_00 ,A : Union[str, Any]=True ,A : Optional[Any]=32 ,A : Union[str, Any]=True ,):
__A = parent
__A = batch_size
__A = num_channels
__A = image_size
__A = min_resolution
__A = max_resolution
__A = do_resize
__A = size_divisor
__A = do_rescale
def UpperCamelCase_ ( self : Union[str, Any] ):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = GLPNImageProcessor if is_vision_available() else None
def UpperCamelCase_ ( self : int ):
__A = GLPNImageProcessingTester(self )
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase_ ( self : Any ):
__A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A ,"do_resize" ) )
self.assertTrue(hasattr(A ,"size_divisor" ) )
self.assertTrue(hasattr(A ,"resample" ) )
self.assertTrue(hasattr(A ,"do_rescale" ) )
def UpperCamelCase_ ( self : str ):
pass
def UpperCamelCase_ ( self : Dict ):
# Initialize image_processing
__A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__A = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A ,Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
__A = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def UpperCamelCase_ ( self : Optional[Any] ):
# Initialize image_processing
__A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__A = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,numpify=A )
for image in image_inputs:
self.assertIsInstance(A ,np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
__A = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def UpperCamelCase_ ( self : int ):
# Initialize image_processing
__A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__A = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,torchify=A )
for image in image_inputs:
self.assertIsInstance(A ,torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
__A = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 55
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_: Tuple =logging.get_logger(__name__)
def lowerCAmelCase_ ( snake_case_ : Union[str, Any] ) -> int:
'''simple docstring'''
UpperCAmelCase_ = "huggingface/label-files"
UpperCAmelCase_ = "imagenet-1k-id2label.json"
UpperCAmelCase_ = json.load(open(hf_hub_download(snake_case_ , snake_case_ , repo_type="dataset" ) , "r" ) )
UpperCAmelCase_ = {int(snake_case_ ): v for k, v in idalabel.items()}
UpperCAmelCase_ = {v: k for k, v in idalabel.items()}
UpperCAmelCase_ = "std_conv" if "bit" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
UpperCAmelCase_ = BitConfig(
conv_layer=snake_case_ , num_labels=10_00 , idalabel=snake_case_ , labelaid=snake_case_ , )
return config
def lowerCAmelCase_ ( snake_case_ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
if "stem.conv" in name:
UpperCAmelCase_ = name.replace("stem.conv" , "bit.embedder.convolution" )
if "blocks" in name:
UpperCAmelCase_ = name.replace("blocks" , "layers" )
if "head.fc" in name:
UpperCAmelCase_ = name.replace("head.fc" , "classifier.1" )
if name.startswith("norm" ):
UpperCAmelCase_ = "bit." + name
if "bit" not in name and "classifier" not in name:
UpperCAmelCase_ = "bit.encoder." + name
return name
def lowerCAmelCase_ ( ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
UpperCAmelCase_ = Image.open(requests.get(snake_case_ , stream=snake_case_ ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( snake_case_ : Tuple , snake_case_ : Optional[Any] , snake_case_ : int=False ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_ = get_config(snake_case_ )
# load original model from timm
UpperCAmelCase_ = create_model(snake_case_ , pretrained=snake_case_ )
timm_model.eval()
# load state_dict of original model
UpperCAmelCase_ = timm_model.state_dict()
for key in state_dict.copy().keys():
UpperCAmelCase_ = state_dict.pop(snake_case_ )
UpperCAmelCase_ = val.squeeze() if "head" in key else val
# load HuggingFace model
UpperCAmelCase_ = BitForImageClassification(snake_case_ )
model.eval()
model.load_state_dict(snake_case_ )
# create image processor
UpperCAmelCase_ = create_transform(**resolve_data_config({} , model=snake_case_ ) )
UpperCAmelCase_ = transform.transforms
UpperCAmelCase_ = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
UpperCAmelCase_ = BitImageProcessor(
do_resize=snake_case_ , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=snake_case_ , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=snake_case_ , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
UpperCAmelCase_ = prepare_img()
UpperCAmelCase_ = transform(snake_case_ ).unsqueeze(0 )
UpperCAmelCase_ = processor(snake_case_ , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(snake_case_ , snake_case_ )
# verify logits
with torch.no_grad():
UpperCAmelCase_ = model(snake_case_ )
UpperCAmelCase_ = outputs.logits
print("Logits:" , logits[0, :3] )
print("Predicted class:" , model.config.idalabel[logits.argmax(-1 ).item()] )
UpperCAmelCase_ = timm_model(snake_case_ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(snake_case_ , outputs.logits , atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(snake_case_ ).mkdir(exist_ok=snake_case_ )
print(f"""Saving model {model_name} and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(snake_case_ )
processor.save_pretrained(snake_case_ )
if push_to_hub:
print(f"""Pushing model {model_name} and processor to the hub""" )
model.push_to_hub(f"""ybelkada/{model_name}""" )
processor.push_to_hub(f"""ybelkada/{model_name}""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_: int =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='resnetv2_50x1_bitm',
type=str,
help='Name of the BiT timm 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 to push the model to the hub.',
)
SCREAMING_SNAKE_CASE_: Union[str, Any] =parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 78
| 0
|
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
A = "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n"
A = "\nArgs:\n predictions (`list` of `int`): Predicted class labels, as returned by a model.\n references (`list` of `int`): Ground truth labels.\n return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n Example 1-A simple example using only predictions and references.\n >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n >>> print(round(results['pearsonr'], 2))\n -0.74\n\n Example 2-The same as Example 1, but that also returns the `p-value`.\n >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n >>> print(sorted(list(results.keys())))\n ['p-value', 'pearsonr']\n >>> print(round(results['pearsonr'], 2))\n -0.74\n >>> print(round(results['p-value'], 2))\n 0.15\n"
A = "\n@article{2020SciPy-NMeth,\nauthor = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Antonio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\njournal = {Nature Methods},\nyear = {2020},\nvolume = {17},\npages = {261--272},\nadsurl = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class __snake_case ( datasets.Metric):
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'predictions': datasets.Value('float' ),
'references': datasets.Value('float' ),
} ), reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html'], )
def UpperCAmelCase_ ( self, A, A, A=False ):
"""simple docstring"""
if return_pvalue:
lowerCamelCase : Optional[Any] = pearsonr(lowercase_, lowercase_ )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(lowercase_, lowercase_ )[0] )}
| 708
|
'''simple docstring'''
A = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A = ['a', 'b', 'c', 'd', 'e']
def UpperCAmelCase ( UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[Any]):
lowerCamelCase : List[Any] = start
# add current to visited
visited.append(UpperCAmelCase__)
lowerCamelCase : List[str] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
lowerCamelCase : Any = topological_sort(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__)
# if all neighbors visited add current to sort
sort.append(UpperCAmelCase__)
# if all vertices haven't been visited select a new one to visit
if len(UpperCAmelCase__) != len(UpperCAmelCase__):
for vertice in vertices:
if vertice not in visited:
lowerCamelCase : List[str] = topological_sort(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__)
# return sort
return sort
if __name__ == "__main__":
A = topological_sort('a', [], [])
print(sort)
| 449
| 0
|
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
UpperCamelCase : Tuple = 'true'
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : Union[str, Any]=82 , __lowerCAmelCase : Tuple=16 ):
set_seed(42 )
lowerCamelCase__ = RegressionModel()
lowerCamelCase__ = deepcopy(__lowerCAmelCase )
lowerCamelCase__ = RegressionDataset(length=__lowerCAmelCase )
lowerCamelCase__ = DataLoader(__lowerCAmelCase , batch_size=__lowerCAmelCase )
model.to(accelerator.device )
lowerCamelCase__ , lowerCamelCase__ = accelerator.prepare(__lowerCAmelCase , __lowerCAmelCase )
return model, ddp_model, dataloader
def A__ ( __lowerCAmelCase : Accelerator , __lowerCAmelCase : Optional[Any]=False ):
lowerCamelCase__ = AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""" )
lowerCamelCase__ = load_dataset("""glue""" , """mrpc""" , split="""validation""" )
def tokenize_function(__lowerCAmelCase : str ):
lowerCamelCase__ = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=__lowerCAmelCase , max_length=__lowerCAmelCase )
return outputs
with accelerator.main_process_first():
lowerCamelCase__ = dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
lowerCamelCase__ = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(__lowerCAmelCase : Dict ):
if use_longest:
return tokenizer.pad(__lowerCAmelCase , padding="""longest""" , return_tensors="""pt""" )
return tokenizer.pad(__lowerCAmelCase , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return DataLoader(__lowerCAmelCase , shuffle=__lowerCAmelCase , collate_fn=__lowerCAmelCase , batch_size=16 )
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any] ):
lowerCamelCase__ = Accelerator(dispatch_batches=__lowerCAmelCase , split_batches=__lowerCAmelCase )
lowerCamelCase__ = get_dataloader(__lowerCAmelCase , not dispatch_batches )
lowerCamelCase__ = AutoModelForSequenceClassification.from_pretrained(
"""hf-internal-testing/mrpc-bert-base-cased""" , return_dict=__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ = accelerator.prepare(__lowerCAmelCase , __lowerCAmelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def A__ ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : str ):
lowerCamelCase__ = []
for batch in dataloader:
lowerCamelCase__ , lowerCamelCase__ = batch.values()
with torch.no_grad():
lowerCamelCase__ = model(__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
lowerCamelCase__ , lowerCamelCase__ = [], []
for logit, targ in logits_and_targets:
logits.append(__lowerCAmelCase )
targs.append(__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ = torch.cat(__lowerCAmelCase ), torch.cat(__lowerCAmelCase )
return logits, targs
def A__ ( __lowerCAmelCase : Accelerator , __lowerCAmelCase : Tuple=82 , __lowerCAmelCase : str=False , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : int=16 ):
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = get_basic_setup(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ = generate_predictions(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
assert (
len(__lowerCAmelCase ) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(__lowerCAmelCase )}'''
def A__ ( __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False ):
lowerCamelCase__ = evaluate.load("""glue""" , """mrpc""" )
lowerCamelCase__ , lowerCamelCase__ = get_mrpc_setup(__lowerCAmelCase , __lowerCAmelCase )
# First do baseline
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = setup["""no"""]
model.to(__lowerCAmelCase )
model.eval()
for batch in dataloader:
batch.to(__lowerCAmelCase )
with torch.inference_mode():
lowerCamelCase__ = model(**__lowerCAmelCase )
lowerCamelCase__ = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=__lowerCAmelCase , references=batch["""labels"""] )
lowerCamelCase__ = metric.compute()
# Then do distributed
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = setup["""ddp"""]
model.eval()
for batch in dataloader:
with torch.inference_mode():
lowerCamelCase__ = model(**__lowerCAmelCase )
lowerCamelCase__ = outputs.logits.argmax(dim=-1 )
lowerCamelCase__ = batch["""labels"""]
lowerCamelCase__ , lowerCamelCase__ = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=__lowerCAmelCase , references=__lowerCAmelCase )
lowerCamelCase__ = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def A__ ( ):
lowerCamelCase__ = Accelerator(split_batches=__lowerCAmelCase , dispatch_batches=__lowerCAmelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("""**Testing gather_for_metrics**""" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(__lowerCAmelCase , __lowerCAmelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("""**Test torch metrics**""" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
lowerCamelCase__ = Accelerator(split_batches=__lowerCAmelCase , dispatch_batches=__lowerCAmelCase )
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(__lowerCAmelCase , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("""**Test last batch is not dropped when perfectly divisible**""" )
lowerCamelCase__ = Accelerator()
test_torch_metrics(__lowerCAmelCase , 512 )
accelerator.state._reset_state()
def A__ ( __lowerCAmelCase : List[Any] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 50
|
import sys
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
__A : List[Any] = "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append("importlib_metadata")
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
require_version_core(deps[pkg])
else:
raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> Union[str, Any]:
"""simple docstring"""
require_version(deps[pkg] , _SCREAMING_SNAKE_CASE )
| 27
| 0
|
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
A_ : str = list(__A )
A_ : Optional[Any] = list(__A )
A_ : str = 0
for i in range(len(__A ) ):
if lista[i] != lista[i]:
count += 1
A_ : Union[str, Any] = '''_'''
if count > 1:
return False
else:
return "".join(__A )
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
A_ : int = []
while True:
A_ : Optional[int] = ['''$'''] * len(__A )
A_ : int = []
for i in range(len(__A ) ):
for j in range(i + 1 ,len(__A ) ):
A_ : str = compare_string(binary[i] ,binary[j] )
if k is False:
A_ : List[str] = '''*'''
A_ : Any = '''*'''
temp.append("""X""" )
for i in range(len(__A ) ):
if checka[i] == "$":
pi.append(binary[i] )
if len(__A ) == 0:
return pi
A_ : Union[str, Any] = list(set(__A ) )
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
A_ : str = []
for minterm in minterms:
A_ : Dict = ''''''
for _ in range(__A ):
A_ : Union[str, Any] = str(minterm % 2 ) + string
minterm //= 2
temp.append(__A )
return temp
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
A_ : Dict = list(__A )
A_ : Tuple = list(__A )
A_ : int = 0
for i in range(len(__A ) ):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
A_ : Optional[Any] = []
A_ : List[str] = [0] * len(__A )
for i in range(len(chart[0] ) ):
A_ : int = 0
A_ : List[Any] = -1
for j in range(len(__A ) ):
if chart[j][i] == 1:
count += 1
A_ : List[Any] = j
if count == 1:
A_ : int = 1
for i in range(len(__A ) ):
if select[i] == 1:
for j in range(len(chart[0] ) ):
if chart[i][j] == 1:
for k in range(len(__A ) ):
A_ : Any = 0
temp.append(prime_implicants[i] )
while True:
A_ : Any = 0
A_ : Any = -1
A_ : List[str] = 0
for i in range(len(__A ) ):
A_ : List[Any] = chart[i].count(1 )
if count_n > max_n:
A_ : Dict = count_n
A_ : Union[str, Any] = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem] )
for i in range(len(chart[0] ) ):
if chart[rem][i] == 1:
for j in range(len(__A ) ):
A_ : int = 0
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
A_ : List[Any] = [[0 for x in range(len(__A ) )] for x in range(len(__A ) )]
for i in range(len(__A ) ):
A_ : Union[str, Any] = prime_implicants[i].count("""_""" )
for j in range(len(__A ) ):
if is_for_table(prime_implicants[i] ,binary[j] ,__A ):
A_ : Union[str, Any] = 1
return chart
def _lowerCAmelCase ( ):
'''simple docstring'''
A_ : Union[str, Any] = int(input("""Enter the no. of variables\n""" ) )
A_ : Dict = [
float(__A )
for x in input(
"""Enter the decimal representation of Minterms \'Spaces Separated\'\n""" ).split()
]
A_ : int = decimal_to_binary(__A ,__A )
A_ : Tuple = check(__A )
print("""Prime Implicants are:""" )
print(__A )
A_ : Optional[Any] = prime_implicant_chart(__A ,__A )
A_ : Tuple = selection(__A ,__A )
print("""Essential Prime Implicants are:""" )
print(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 708
|
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,):
'''simple docstring'''
A_ , A_ : int = coefficient_matrix.shape
A_ , A_ : Tuple = constant_matrix.shape
if rowsa != colsa:
A_ : int = f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
raise ValueError(_lowerCAmelCase )
if colsa != 1:
A_ : List[Any] = f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
raise ValueError(_lowerCAmelCase )
if rowsa != rowsa:
A_ : str = (
"""Coefficient and constant matrices dimensions must be nxn and nx1 but """
f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
)
raise ValueError(_lowerCAmelCase )
if len(_lowerCAmelCase ) != rowsa:
A_ : Any = (
"""Number of initial values must be equal to number of rows in coefficient """
f"""matrix but received {len(_lowerCAmelCase )} and {rowsa}"""
)
raise ValueError(_lowerCAmelCase )
if iterations <= 0:
raise ValueError("""Iterations must be at least 1""" )
A_ : NDArray[floataa] = np.concatenate(
(coefficient_matrix, constant_matrix) ,axis=1 )
A_ , A_ : str = table.shape
strictly_diagonally_dominant(_lowerCAmelCase )
# Iterates the whole matrix for given number of times
for _ in range(_lowerCAmelCase ):
A_ : Union[str, Any] = []
for row in range(_lowerCAmelCase ):
A_ : str = 0
for col in range(_lowerCAmelCase ):
if col == row:
A_ : Optional[Any] = table[row][col]
elif col == cols - 1:
A_ : List[str] = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
A_ : str = (temp + val) / denom
new_val.append(_lowerCAmelCase )
A_ : List[str] = new_val
return [float(_lowerCAmelCase ) for i in new_val]
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
A_ , A_ : str = table.shape
A_ : Any = True
for i in range(0 ,_lowerCAmelCase ):
A_ : Optional[Any] = 0
for j in range(0 ,cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("""Coefficient matrix is not strictly diagonally dominant""" )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 481
| 0
|
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class __A( unittest.TestCase ):
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=4 , ) -> Union[str, Any]:
'''simple docstring'''
__a = parent
__a = batch_size
__a = seq_length
__a = is_training
__a = use_attention_mask
__a = use_token_type_ids
__a = use_labels
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = type_vocab_size
__a = type_sequence_label_size
__a = initializer_range
__a = num_choices
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a = None
if self.use_attention_mask:
__a = random_attention_mask([self.batch_size, self.seq_length] )
__a = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=__UpperCamelCase , )
return config, input_ids, attention_mask
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = self.prepare_config_and_inputs()
__a , __a , __a = config_and_inputs
__a = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class __A( _lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = FlaxDistilBertModelTester(self )
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__a = model_class_name.from_pretrained('''distilbert-base-uncased''' )
__a = model(np.ones((1, 1) ) )
self.assertIsNotNone(__UpperCamelCase )
@require_flax
class __A( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased''' )
__a = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
__a = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__a = model(__UpperCamelCase , attention_mask=__UpperCamelCase )[0]
__a = (1, 11, 768)
self.assertEqual(output.shape , __UpperCamelCase )
__a = np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , __UpperCamelCase , atol=1E-4 ) )
| 219
|
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__snake_case :str =False
class lowerCAmelCase__ ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
def __UpperCamelCase ( self : Dict ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
A = VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' , torch_dtype=torch.floataa )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
A = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
A = torch.manual_seed(0 )
A = pipe.dual_guided(
prompt='first prompt' , image=__UpperCamelCase , text_to_image_strength=0.7_5 , generator=__UpperCamelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__UpperCamelCase )
A = VersatileDiffusionPipeline.from_pretrained(__UpperCamelCase , torch_dtype=torch.floataa )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
A = generator.manual_seed(0 )
A = pipe.dual_guided(
prompt='first prompt' , image=__UpperCamelCase , text_to_image_strength=0.7_5 , generator=__UpperCamelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def __UpperCamelCase ( self : Tuple ) -> List[str]:
A = VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' , torch_dtype=torch.floataa )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
A = 'cyberpunk 2077'
A = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
A = torch.manual_seed(0 )
A = pipe.dual_guided(
prompt=__UpperCamelCase , image=__UpperCamelCase , text_to_image_strength=0.7_5 , generator=__UpperCamelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' , ).images
A = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
A = np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
A = 'A painting of a squirrel eating a burger '
A = torch.manual_seed(0 )
A = pipe.text_to_image(
prompt=__UpperCamelCase , generator=__UpperCamelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' ).images
A = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
A = np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
A = pipe.image_variation(__UpperCamelCase , generator=__UpperCamelCase , output_type='numpy' ).images
A = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
A = np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 106
| 0
|
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : str = LayoutLMTokenizer
UpperCamelCase_ : List[Any] = LayoutLMTokenizerFast
UpperCamelCase_ : Dict = True
UpperCamelCase_ : Any = True
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
A = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def _SCREAMING_SNAKE_CASE ( self : int , **snake_case__ : Union[str, Any] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Tuple ):
A = """UNwant\u00E9d,running"""
A = """unwanted, running"""
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
A = self.tokenizer_class(self.vocab_file )
A = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(snake_case__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , [7, 4, 5, 10, 8, 9] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
pass
| 720
|
"""simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = MobileBertConfig.from_json_file(lowerCamelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = MobileBertForPreTraining(lowerCamelCase__ )
# Load weights from tf checkpoint
lowerCAmelCase__ = load_tf_weights_in_mobilebert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--mobilebert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained MobileBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 674
| 0
|
'''simple docstring'''
import requests
__lowerCAmelCase = """""" # <-- Put your OpenWeatherMap appid here!
__lowerCAmelCase = """https://api.openweathermap.org/data/2.5/"""
def UpperCAmelCase_ (__a : str = "Chicago" , __a : str = APPID ):
"""simple docstring"""
return requests.get(URL_BASE + 'weather' , params=locals() ).json()
def UpperCAmelCase_ (__a : str = "Kolkata, India" , __a : str = APPID ):
"""simple docstring"""
return requests.get(URL_BASE + 'forecast' , params=locals() ).json()
def UpperCAmelCase_ (__a : float = 55.68 , __a : float = 12.57 , __a : str = APPID ):
"""simple docstring"""
return requests.get(URL_BASE + 'onecall' , params=locals() ).json()
if __name__ == "__main__":
from pprint import pprint
while True:
__lowerCAmelCase = input("""Enter a location:""").strip()
if location:
pprint(current_weather(location))
else:
break
| 229
|
'''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
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"""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 UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
__UpperCAmelCase : int = '''mobilenet_v2'''
def __init__( self : Optional[int] ,_a : str=3 ,_a : Dict=224 ,_a : Tuple=1.0 ,_a : List[Any]=8 ,_a : int=8 ,_a : Dict=6 ,_a : Tuple=32 ,_a : str=True ,_a : str=True ,_a : int="relu6" ,_a : Tuple=True ,_a : List[str]=0.8 ,_a : Dict=0.02 ,_a : Tuple=0.001 ,_a : Optional[Any]=255 ,**_a : List[Any] ,):
'''simple docstring'''
super().__init__(**_a )
if depth_multiplier <= 0:
raise ValueError('depth_multiplier must be greater than zero.' )
_a : Union[str, Any] = num_channels
_a : List[Any] = image_size
_a : Optional[Any] = depth_multiplier
_a : Union[str, Any] = depth_divisible_by
_a : Tuple = min_depth
_a : Union[str, Any] = expand_ratio
_a : Union[str, Any] = output_stride
_a : Union[str, Any] = first_layer_is_expansion
_a : Optional[Any] = finegrained_output
_a : Any = hidden_act
_a : int = tf_padding
_a : List[str] = classifier_dropout_prob
_a : List[Any] = initializer_range
_a : Union[str, Any] = layer_norm_eps
_a : Any = semantic_loss_ignore_index
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
__UpperCAmelCase : str = version.parse('''1.11''' )
@property
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
return OrderedDict([('pixel_values', {0: 'batch'})] )
@property
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([('logits', {0: 'batch'})] )
else:
return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})] )
@property
def __lowercase ( self : List[str] ):
'''simple docstring'''
return 1E-4
| 229
| 1
|
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class _a :
"""simple docstring"""
def __init__( self: str , __lowerCamelCase: List[Any] , __lowerCamelCase: List[Any]=13 , __lowerCamelCase: str=7 , __lowerCamelCase: Any=True , __lowerCamelCase: Tuple=True , __lowerCamelCase: Optional[int]=True , __lowerCamelCase: int=True , __lowerCamelCase: Optional[int]=99 , __lowerCamelCase: int=64 , __lowerCamelCase: List[str]=32 , __lowerCamelCase: Optional[int]=5 , __lowerCamelCase: List[Any]=4 , __lowerCamelCase: Dict=37 , __lowerCamelCase: str="gelu" , __lowerCamelCase: str=0.1 , __lowerCamelCase: List[str]=0.1 , __lowerCamelCase: List[Any]=512 , __lowerCamelCase: Tuple=16 , __lowerCamelCase: List[Any]=2 , __lowerCamelCase: Tuple=0.02 , __lowerCamelCase: Tuple=3 , __lowerCamelCase: Tuple=4 , __lowerCamelCase: Tuple=None , ):
'''simple docstring'''
UpperCamelCase__: Optional[Any] = parent
UpperCamelCase__: Tuple = batch_size
UpperCamelCase__: Union[str, Any] = seq_length
UpperCamelCase__: int = is_training
UpperCamelCase__: str = use_input_mask
UpperCamelCase__: Optional[Any] = use_token_type_ids
UpperCamelCase__: Any = use_labels
UpperCamelCase__: List[str] = vocab_size
UpperCamelCase__: Optional[int] = hidden_size
UpperCamelCase__: List[Any] = embedding_size
UpperCamelCase__: Optional[Any] = num_hidden_layers
UpperCamelCase__: List[str] = num_attention_heads
UpperCamelCase__: List[str] = intermediate_size
UpperCamelCase__: Union[str, Any] = hidden_act
UpperCamelCase__: Tuple = hidden_dropout_prob
UpperCamelCase__: List[str] = attention_probs_dropout_prob
UpperCamelCase__: Tuple = max_position_embeddings
UpperCamelCase__: Tuple = type_vocab_size
UpperCamelCase__: Optional[int] = type_sequence_label_size
UpperCamelCase__: Optional[Any] = initializer_range
UpperCamelCase__: str = num_labels
UpperCamelCase__: int = num_choices
UpperCamelCase__: Union[str, Any] = scope
def UpperCAmelCase_ ( self: List[Any] ):
'''simple docstring'''
UpperCamelCase__: Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase__: Dict = None
if self.use_input_mask:
UpperCamelCase__: Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase__: int = None
if self.use_token_type_ids:
UpperCamelCase__: Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase__: Optional[Any] = None
UpperCamelCase__: int = None
UpperCamelCase__: Union[str, Any] = None
if self.use_labels:
UpperCamelCase__: Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase__: str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase__: Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase__: Optional[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase_ ( self: int ):
'''simple docstring'''
return MegatronBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCamelCase , initializer_range=self.initializer_range , )
def UpperCAmelCase_ ( self: int , __lowerCamelCase: int , __lowerCamelCase: List[Any] , __lowerCamelCase: Tuple , __lowerCamelCase: Dict , __lowerCamelCase: Optional[int] , __lowerCamelCase: List[Any] , __lowerCamelCase: Optional[Any] ):
'''simple docstring'''
UpperCamelCase__: Tuple = MegatronBertModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCamelCase__: Optional[int] = model(__lowerCamelCase , attention_mask=__lowerCamelCase , token_type_ids=__lowerCamelCase )
UpperCamelCase__: int = model(__lowerCamelCase , token_type_ids=__lowerCamelCase )
UpperCamelCase__: Optional[int] = model(__lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCAmelCase_ ( self: str , __lowerCamelCase: List[Any] , __lowerCamelCase: Dict , __lowerCamelCase: List[str] , __lowerCamelCase: Union[str, Any] , __lowerCamelCase: Dict , __lowerCamelCase: str , __lowerCamelCase: Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__: List[Any] = MegatronBertForMaskedLM(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCamelCase__: Tuple = model(__lowerCamelCase , attention_mask=__lowerCamelCase , token_type_ids=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ ( self: Tuple , __lowerCamelCase: List[Any] , __lowerCamelCase: int , __lowerCamelCase: Optional[Any] , __lowerCamelCase: Any , __lowerCamelCase: Any , __lowerCamelCase: List[Any] , __lowerCamelCase: Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__: List[Any] = MegatronBertForCausalLM(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCamelCase__: List[Any] = model(__lowerCamelCase , attention_mask=__lowerCamelCase , token_type_ids=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ ( self: int , __lowerCamelCase: Optional[Any] , __lowerCamelCase: Optional[Any] , __lowerCamelCase: Union[str, Any] , __lowerCamelCase: List[str] , __lowerCamelCase: int , __lowerCamelCase: List[str] , __lowerCamelCase: Optional[Any] ):
'''simple docstring'''
UpperCamelCase__: Union[str, Any] = MegatronBertForNextSentencePrediction(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCamelCase__: Dict = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , token_type_ids=__lowerCamelCase , labels=__lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def UpperCAmelCase_ ( self: List[str] , __lowerCamelCase: Union[str, Any] , __lowerCamelCase: int , __lowerCamelCase: Optional[int] , __lowerCamelCase: Dict , __lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: Tuple ):
'''simple docstring'''
UpperCamelCase__: Union[str, Any] = MegatronBertForPreTraining(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCamelCase__: int = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , token_type_ids=__lowerCamelCase , labels=__lowerCamelCase , next_sentence_label=__lowerCamelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def UpperCAmelCase_ ( self: List[Any] , __lowerCamelCase: str , __lowerCamelCase: Optional[Any] , __lowerCamelCase: Optional[Any] , __lowerCamelCase: List[Any] , __lowerCamelCase: List[Any] , __lowerCamelCase: Dict , __lowerCamelCase: Optional[int] ):
'''simple docstring'''
UpperCamelCase__: Any = MegatronBertForQuestionAnswering(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCamelCase__: Optional[Any] = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , token_type_ids=__lowerCamelCase , start_positions=__lowerCamelCase , end_positions=__lowerCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase_ ( self: int , __lowerCamelCase: Any , __lowerCamelCase: List[str] , __lowerCamelCase: int , __lowerCamelCase: str , __lowerCamelCase: Dict , __lowerCamelCase: int , __lowerCamelCase: List[Any] ):
'''simple docstring'''
UpperCamelCase__: str = self.num_labels
UpperCamelCase__: List[Any] = MegatronBertForSequenceClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCamelCase__: Optional[Any] = model(__lowerCamelCase , attention_mask=__lowerCamelCase , token_type_ids=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self: Optional[Any] , __lowerCamelCase: Union[str, Any] , __lowerCamelCase: List[Any] , __lowerCamelCase: str , __lowerCamelCase: Optional[int] , __lowerCamelCase: int , __lowerCamelCase: str , __lowerCamelCase: Optional[Any] ):
'''simple docstring'''
UpperCamelCase__: str = self.num_labels
UpperCamelCase__: Optional[int] = MegatronBertForTokenClassification(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCamelCase__: int = model(__lowerCamelCase , attention_mask=__lowerCamelCase , token_type_ids=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase_ ( self: List[str] , __lowerCamelCase: Optional[Any] , __lowerCamelCase: List[Any] , __lowerCamelCase: str , __lowerCamelCase: int , __lowerCamelCase: str , __lowerCamelCase: List[str] , __lowerCamelCase: Optional[Any] ):
'''simple docstring'''
UpperCamelCase__: Dict = self.num_choices
UpperCamelCase__: Dict = MegatronBertForMultipleChoice(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCamelCase__: Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase__: Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase__: Optional[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase__: List[str] = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , token_type_ids=__lowerCamelCase , labels=__lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase_ ( self: Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__: Dict = self.prepare_config_and_inputs()
(
(
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) ,
): Optional[Any] = config_and_inputs
UpperCamelCase__: Any = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _a ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
UpperCamelCase__ = (
{
"""feature-extraction""": MegatronBertModel,
"""fill-mask""": MegatronBertForMaskedLM,
"""question-answering""": MegatronBertForQuestionAnswering,
"""text-classification""": MegatronBertForSequenceClassification,
"""text-generation""": MegatronBertForCausalLM,
"""token-classification""": MegatronBertForTokenClassification,
"""zero-shot""": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase__ = True
# test_resize_embeddings = False
UpperCamelCase__ = False
def UpperCAmelCase_ ( self: Union[str, Any] , __lowerCamelCase: Optional[Any] , __lowerCamelCase: int , __lowerCamelCase: Dict=False ):
'''simple docstring'''
UpperCamelCase__: int = super()._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
if return_labels:
if model_class in get_values(__lowerCamelCase ):
UpperCamelCase__: Union[str, Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCamelCase )
UpperCamelCase__: Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCamelCase )
return inputs_dict
def UpperCAmelCase_ ( self: List[Any] ):
'''simple docstring'''
UpperCamelCase__: Union[str, Any] = MegatronBertModelTester(self )
UpperCamelCase__: int = ConfigTester(self , config_class=__lowerCamelCase , hidden_size=37 )
def UpperCAmelCase_ ( self: List[str] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self: List[Any] ):
'''simple docstring'''
UpperCamelCase__: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*__lowerCamelCase )
def UpperCAmelCase_ ( self: Optional[int] ):
'''simple docstring'''
UpperCamelCase__: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__lowerCamelCase )
def UpperCAmelCase_ ( self: Optional[int] ):
'''simple docstring'''
UpperCamelCase__: Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__lowerCamelCase )
def UpperCAmelCase_ ( self: Tuple ):
'''simple docstring'''
UpperCamelCase__: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__lowerCamelCase )
def UpperCAmelCase_ ( self: Tuple ):
'''simple docstring'''
UpperCamelCase__: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*__lowerCamelCase )
def UpperCAmelCase_ ( self: int ):
'''simple docstring'''
UpperCamelCase__: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*__lowerCamelCase )
def UpperCAmelCase_ ( self: Any ):
'''simple docstring'''
UpperCamelCase__: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__lowerCamelCase )
def UpperCAmelCase_ ( self: Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*__lowerCamelCase )
def lowerCAmelCase_ ( A_):
return torch.tensor(
A_ ,dtype=torch.long ,device=A_ ,)
A__: List[str] = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
class _a ( unittest.TestCase):
"""simple docstring"""
@slow
@unittest.skip("Model is not available." )
def UpperCAmelCase_ ( self: str ):
'''simple docstring'''
UpperCamelCase__: Optional[Any] = "nvidia/megatron-bert-uncased-345m"
if "MYDIR" in os.environ:
UpperCamelCase__: Union[str, Any] = os.path.join(os.environ["MYDIR"] , __lowerCamelCase )
UpperCamelCase__: Optional[Any] = MegatronBertModel.from_pretrained(__lowerCamelCase )
model.to(__lowerCamelCase )
model.half()
UpperCamelCase__: List[str] = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
UpperCamelCase__: Dict = model(__lowerCamelCase )[0]
UpperCamelCase__: Optional[int] = torch.Size((1, 9, 1024) )
self.assertEqual(output.shape , __lowerCamelCase )
UpperCamelCase__: Optional[int] = [-0.6_040, -0.2_517, -0.1_025, 0.3_420, -0.6_758, -0.0_017, -0.1_089, -0.1_990, 0.5_728]
for ii in range(3 ):
for jj in range(3 ):
UpperCamelCase__: Optional[Any] = output[0, ii, jj]
UpperCamelCase__: Any = expected[3 * ii + jj]
UpperCamelCase__: Optional[int] = "ii={} jj={} a={} b={}".format(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
self.assertTrue(math.isclose(__lowerCamelCase , __lowerCamelCase , rel_tol=__lowerCamelCase , abs_tol=__lowerCamelCase ) , msg=__lowerCamelCase )
| 221
|
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
A__: Union[str, Any] = [
'''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 lowerCAmelCase_ ( A_ ,A_=None):
require_version(deps[pkg] ,A_)
| 221
| 1
|
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def __UpperCAmelCase ( UpperCAmelCase=32, UpperCAmelCase=10, UpperCAmelCase=100, UpperCAmelCase=1026, UpperCAmelCase=True, UpperCAmelCase="data/tokenized_stories_train_wikitext103.jbl", UpperCAmelCase="igf_context_pairs.jbl", )-> Tuple:
"""simple docstring"""
set_seed(3 )
# generate train_data and objective_set
lowercase ,lowercase = generate_datasets(
UpperCAmelCase, UpperCAmelCase, number=UpperCAmelCase, min_len=1026, trim=UpperCAmelCase )
# keeps model same across runs
set_seed(4 )
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
lowercase = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
# load pretrained model
lowercase = load_gpta('''gpt2''' ).to(UpperCAmelCase )
print('''computing perplexity on objective set''' )
lowercase = compute_perplexity(UpperCAmelCase, UpperCAmelCase, UpperCAmelCase ).item()
print('''perplexity on objective set:''', UpperCAmelCase )
# collect igf pairs and save to file demo.jbl
collect_objective_set(UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase=15, UpperCAmelCase=128, UpperCAmelCase=100, UpperCAmelCase="igf_model.pt", )-> List[str]:
"""simple docstring"""
set_seed(42 )
# Load pre-trained model
lowercase = GPTaLMHeadModel.from_pretrained('''gpt2''' )
# Initialize secondary learner to use embedding weights of model
lowercase = SecondaryLearner(UpperCAmelCase )
# Train secondary learner
lowercase = train_secondary_learner(
UpperCAmelCase, UpperCAmelCase, max_epochs=UpperCAmelCase, batch_size=UpperCAmelCase, eval_freq=100, igf_model_path=UpperCAmelCase, )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase=32, UpperCAmelCase=1000, UpperCAmelCase=16, UpperCAmelCase=1.0, UpperCAmelCase=recopy_gpta, UpperCAmelCase=None, UpperCAmelCase=10, UpperCAmelCase="gpt2_finetuned.pt", )-> Optional[int]:
"""simple docstring"""
lowercase = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
lowercase = RandomSampler(UpperCAmelCase )
lowercase = DataLoader(UpperCAmelCase, sampler=UpperCAmelCase )
lowercase = max_steps // (len(UpperCAmelCase )) + 1
lowercase = 0
lowercase = torch.zeros((1, context_len), dtype=torch.long, device=UpperCAmelCase )
lowercase ,lowercase ,lowercase = recopy_model(UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
model.train()
if secondary_learner is not None:
secondary_learner.to(UpperCAmelCase )
secondary_learner.eval()
lowercase = []
lowercase = 0
lowercase = []
lowercase = []
# Compute the performance of the transformer model at the beginning
lowercase = compute_perplexity(UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
test_perps.append(UpperCAmelCase )
print('''Test perplexity, step''', UpperCAmelCase, ''':''', UpperCAmelCase )
for epoch in range(int(UpperCAmelCase ) ):
for step, example in enumerate(UpperCAmelCase ):
torch.cuda.empty_cache()
lowercase = random.randint(0, example.size(2 ) - context_len - 1 )
lowercase = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
lowercase = model(UpperCAmelCase, labels=UpperCAmelCase )
lowercase = True
if secondary_learner is not None:
lowercase = secondary_learner.forward(
torch.tensor(UpperCAmelCase, dtype=torch.long, device=UpperCAmelCase ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(UpperCAmelCase ) )
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
lowercase = -1
if predicted_q < threshold:
lowercase = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
lowercase = outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
lowercase = 0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters(), 3.0 )
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
lowercase = compute_perplexity(UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
test_perps.append(UpperCAmelCase )
print('''Test perplexity, step''', UpperCAmelCase, ''':''', UpperCAmelCase )
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict(), UpperCAmelCase )
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
lowercase = argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task''' )
# Required parameters
parser.add_argument(
'''--data_dir''', default=UpperCAmelCase, type=UpperCAmelCase, required=UpperCAmelCase, help='''The input data dir. Should contain data files for WikiText.''', )
parser.add_argument(
'''--model_name_or_path''', default=UpperCAmelCase, type=UpperCAmelCase, required=UpperCAmelCase, help='''Path to pretrained model or model identifier from huggingface.co/models''', )
parser.add_argument(
'''--data_file''', type=UpperCAmelCase, default=UpperCAmelCase, help=(
'''A jbl file containing tokenized data which can be split as objective dataset, '''
'''train_dataset and test_dataset.'''
), )
parser.add_argument(
'''--igf_data_file''', type=UpperCAmelCase, default=UpperCAmelCase, help='''A jbl file containing the context and information gain pairs to train secondary learner.''', )
parser.add_argument(
'''--output_dir''', default=UpperCAmelCase, type=UpperCAmelCase, required=UpperCAmelCase, help='''The output directory where the final fine-tuned model is stored.''', )
parser.add_argument(
'''--tokenizer_name''', default=UpperCAmelCase, type=UpperCAmelCase, help='''Pretrained tokenizer name or path if not the same as model_name''', )
parser.add_argument('''--seed''', type=UpperCAmelCase, default=UpperCAmelCase, help='''A seed for reproducible training.''' )
parser.add_argument(
'''--context_len''', default=32, type=UpperCAmelCase, help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
), )
parser.add_argument(
'''--size_objective_set''', default=100, type=UpperCAmelCase, help='''number of articles that are long enough to be used as our objective set''', )
parser.add_argument(
'''--eval_freq''', default=100, type=UpperCAmelCase, help='''secondary model evaluation is triggered at eval_freq''' )
parser.add_argument('''--max_steps''', default=1000, type=UpperCAmelCase, help='''To calculate training epochs''' )
parser.add_argument(
'''--secondary_learner_batch_size''', default=128, type=UpperCAmelCase, help='''batch size of training data for secondary learner''', )
parser.add_argument(
'''--batch_size''', default=16, type=UpperCAmelCase, help='''batch size of training data of language model(gpt2) ''' )
parser.add_argument(
'''--eval_interval''', default=10, type=UpperCAmelCase, help=(
'''decay the selectivity of our secondary learner filter from'''
'''1 standard deviation above average to 1 below average after 10 batches'''
), )
parser.add_argument(
'''--number''', default=100, type=UpperCAmelCase, help='''The number of examples split to be used as objective_set/test_data''' )
parser.add_argument(
'''--min_len''', default=1026, type=UpperCAmelCase, help='''The minimum length of the article to be used as objective set''' )
parser.add_argument(
'''--secondary_learner_max_epochs''', default=15, type=UpperCAmelCase, help='''number of epochs to train secondary learner''' )
parser.add_argument('''--trim''', default=UpperCAmelCase, type=UpperCAmelCase, help='''truncate the example if it exceeds context length''' )
parser.add_argument(
'''--threshold''', default=1.0, type=UpperCAmelCase, help=(
'''The threshold value used by secondary learner to filter the train_data and allow only'''
''' informative data as input to the model'''
), )
parser.add_argument('''--finetuned_model_name''', default='''gpt2_finetuned.pt''', type=UpperCAmelCase, help='''finetuned_model_name''' )
parser.add_argument(
'''--recopy_model''', default=UpperCAmelCase, type=UpperCAmelCase, help='''Reset the model to the original pretrained GPT-2 weights after each iteration''', )
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32, max_steps=10, size_objective_set=100, min_len=1026, trim=UpperCAmelCase, data_file='''data/tokenized_stories_train_wikitext103.jbl''', igf_data_file='''igf_context_pairs.jbl''', )
# Load train data for secondary learner
lowercase = joblib.load('''data/IGF_values.jbl''' )
# Train secondary learner
lowercase = training_secondary_learner(
UpperCAmelCase, secondary_learner_max_epochs=15, secondary_learner_batch_size=128, eval_freq=100, igf_model_path='''igf_model.pt''', )
# load pretrained gpt2 model
lowercase = GPTaLMHeadModel.from_pretrained('''gpt2''' )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
lowercase ,lowercase = generate_datasets(
context_len=32, file='''data/tokenized_stories_train_wikitext103.jbl''', number=100, min_len=1026, trim=UpperCAmelCase )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, context_len=32, max_steps=1000, batch_size=16, threshold=1.0, recopy_model=UpperCAmelCase, secondary_learner=UpperCAmelCase, eval_interval=10, finetuned_model_name='''gpt2_finetuned.pt''', )
if __name__ == "__main__":
main()
| 604
|
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def __UpperCAmelCase ( UpperCAmelCase )-> Optional[int]:
"""simple docstring"""
if isinstance(UpperCAmelCase, collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __lowercase :
def __a ( self : Dict , __lowerCamelCase : List[str] , __lowerCamelCase : Tuple ) -> Optional[int]:
'''simple docstring'''
pass
def __a ( self : Dict ) -> Tuple:
'''simple docstring'''
pass
def __a ( self : str ) -> Optional[int]:
'''simple docstring'''
pass
def __a ( self : int , __lowerCamelCase : int , __lowerCamelCase : Tuple , __lowerCamelCase : Dict , __lowerCamelCase : Dict , __lowerCamelCase : Any=None , **__lowerCamelCase : Any ) -> Optional[int]:
'''simple docstring'''
lowercase = VisionTextDualEncoderConfig.from_vision_text_configs(__lowerCamelCase , __lowerCamelCase )
lowercase = TFVisionTextDualEncoderModel(__lowerCamelCase )
lowercase = model(input_ids=__lowerCamelCase , pixel_values=__lowerCamelCase , attention_mask=__lowerCamelCase )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], config.projection_dim) )
def __a ( self : Any , __lowerCamelCase : Optional[int] , __lowerCamelCase : Any , __lowerCamelCase : Tuple , __lowerCamelCase : List[str] , __lowerCamelCase : Optional[Any]=None , **__lowerCamelCase : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
lowercase ,lowercase = self.get_vision_text_model(__lowerCamelCase , __lowerCamelCase )
lowercase = TFVisionTextDualEncoderModel(vision_model=__lowerCamelCase , text_model=__lowerCamelCase )
lowercase = model(input_ids=__lowerCamelCase , pixel_values=__lowerCamelCase , attention_mask=__lowerCamelCase )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __a ( self : Optional[int] , __lowerCamelCase : Dict , __lowerCamelCase : List[str] , __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] , __lowerCamelCase : str=None , **__lowerCamelCase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
lowercase ,lowercase = self.get_vision_text_model(__lowerCamelCase , __lowerCamelCase )
lowercase = {'''vision_model''': vision_model, '''text_model''': text_model}
lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__lowerCamelCase )
lowercase = model(input_ids=__lowerCamelCase , pixel_values=__lowerCamelCase , attention_mask=__lowerCamelCase )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __a ( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Dict , __lowerCamelCase : Optional[Any]=None , **__lowerCamelCase : Tuple ) -> Any:
'''simple docstring'''
lowercase ,lowercase = self.get_vision_text_model(__lowerCamelCase , __lowerCamelCase )
lowercase = TFVisionTextDualEncoderModel(vision_model=__lowerCamelCase , text_model=__lowerCamelCase )
lowercase = model(input_ids=__lowerCamelCase , pixel_values=__lowerCamelCase , attention_mask=__lowerCamelCase )
lowercase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__lowerCamelCase )
lowercase = TFVisionTextDualEncoderModel.from_pretrained(__lowerCamelCase )
lowercase = model(input_ids=__lowerCamelCase , pixel_values=__lowerCamelCase , attention_mask=__lowerCamelCase )
lowercase = after_output[0].numpy()
lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__lowerCamelCase , 1E-5 )
def __a ( self : List[str] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Tuple=None , **__lowerCamelCase : Dict ) -> Union[str, Any]:
'''simple docstring'''
lowercase ,lowercase = self.get_vision_text_model(__lowerCamelCase , __lowerCamelCase )
lowercase = TFVisionTextDualEncoderModel(vision_model=__lowerCamelCase , text_model=__lowerCamelCase )
lowercase = model(
input_ids=__lowerCamelCase , pixel_values=__lowerCamelCase , attention_mask=__lowerCamelCase , output_attentions=__lowerCamelCase )
lowercase = output.vision_model_output.attentions
self.assertEqual(len(__lowerCamelCase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase = to_atuple(vision_model.config.image_size )
lowercase = to_atuple(vision_model.config.patch_size )
lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase = output.text_model_output.attentions
self.assertEqual(len(__lowerCamelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __a ( self : Any , __lowerCamelCase : np.ndarray , __lowerCamelCase : np.ndarray , __lowerCamelCase : float ) -> Optional[Any]:
'''simple docstring'''
lowercase = np.abs((a - b) ).max()
self.assertLessEqual(__lowerCamelCase , __lowerCamelCase , f'Difference between torch and flax is {diff} (>= {tol}).' )
def __a ( self : str ) -> Dict:
'''simple docstring'''
lowercase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**__lowerCamelCase )
def __a ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
lowercase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**__lowerCamelCase )
def __a ( self : List[Any] ) -> Dict:
'''simple docstring'''
lowercase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**__lowerCamelCase )
def __a ( self : List[Any] ) -> str:
'''simple docstring'''
lowercase = self.prepare_config_and_inputs()
self.check_save_load(**__lowerCamelCase )
def __a ( self : List[Any] ) -> List[str]:
'''simple docstring'''
lowercase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**__lowerCamelCase )
@slow
def __a ( self : List[Any] ) -> Dict:
'''simple docstring'''
lowercase ,lowercase = self.get_pretrained_model_and_inputs()
lowercase = model_a(**__lowerCamelCase )
lowercase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(__lowerCamelCase )
lowercase = TFVisionTextDualEncoderModel.from_pretrained(__lowerCamelCase )
lowercase = model_a(**__lowerCamelCase )
lowercase = after_outputs[0].numpy()
lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__lowerCamelCase , 1E-5 )
@require_tf
class __lowercase ( _A , unittest.TestCase ):
def __a ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'''hf-internal-testing/tiny-random-vit''' , '''hf-internal-testing/tiny-random-bert''' )
lowercase = 13
lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowercase = random_attention_mask([batch_size, 4] )
lowercase = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def __a ( self : int , __lowerCamelCase : Tuple , __lowerCamelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
lowercase = TFViTModel(__lowerCamelCase , name='''vision_model''' )
lowercase = TFBertModel(__lowerCamelCase , name='''text_model''' )
return vision_model, text_model
def __a ( self : Optional[int] ) -> Dict:
'''simple docstring'''
lowercase = TFViTModelTester(self )
lowercase = TFBertModelTester(self )
lowercase = vit_model_tester.prepare_config_and_inputs()
lowercase = bert_model_tester.prepare_config_and_inputs()
lowercase ,lowercase ,lowercase = vision_config_and_inputs
(
(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowercase ( _A , unittest.TestCase ):
def __a ( self : int ) -> str:
'''simple docstring'''
lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'''Rocketknight1/tiny-random-deit-tf''' , '''hf-internal-testing/tiny-random-roberta''' )
lowercase = 13
lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowercase = random_attention_mask([batch_size, 4] )
lowercase = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def __a ( self : Optional[int] , __lowerCamelCase : Dict , __lowerCamelCase : List[str] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict=None , **__lowerCamelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
lowercase ,lowercase = self.get_vision_text_model(__lowerCamelCase , __lowerCamelCase )
lowercase = TFVisionTextDualEncoderModel(vision_model=__lowerCamelCase , text_model=__lowerCamelCase )
lowercase = model(
input_ids=__lowerCamelCase , pixel_values=__lowerCamelCase , attention_mask=__lowerCamelCase , output_attentions=__lowerCamelCase )
lowercase = output.vision_model_output.attentions
self.assertEqual(len(__lowerCamelCase ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
lowercase = to_atuple(vision_model.config.image_size )
lowercase = to_atuple(vision_model.config.patch_size )
lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase = output.text_model_output.attentions
self.assertEqual(len(__lowerCamelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __a ( self : Optional[int] , __lowerCamelCase : str , __lowerCamelCase : Optional[Any] ) -> str:
'''simple docstring'''
lowercase = TFDeiTModel(__lowerCamelCase , name='''vision_model''' )
lowercase = TFRobertaModel(__lowerCamelCase , name='''text_model''' )
return vision_model, text_model
def __a ( self : Any ) -> str:
'''simple docstring'''
lowercase = TFDeiTModelTester(self )
lowercase = TFRobertaModelTester(self )
lowercase = vit_model_tester.prepare_config_and_inputs()
lowercase = bert_model_tester.prepare_config_and_inputs()
lowercase ,lowercase ,lowercase = vision_config_and_inputs
(
(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __lowercase ( _A , unittest.TestCase ):
def __a ( self : Optional[int] ) -> str:
'''simple docstring'''
lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'''Rocketknight1/tiny-random-clip-tf''' , '''hf-internal-testing/tiny-random-bert''' )
lowercase = 13
lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowercase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowercase = random_attention_mask([batch_size, 4] )
lowercase = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def __a ( self : List[str] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Tuple ) -> str:
'''simple docstring'''
lowercase = TFCLIPVisionModel(__lowerCamelCase , name='''vision_model''' )
lowercase = TFBertModel(__lowerCamelCase , name='''text_model''' )
return vision_model, text_model
def __a ( self : Tuple ) -> str:
'''simple docstring'''
lowercase = TFCLIPVisionModelTester(self )
lowercase = TFBertModelTester(self )
lowercase = clip_model_tester.prepare_config_and_inputs()
lowercase = bert_model_tester.prepare_config_and_inputs()
lowercase ,lowercase = vision_config_and_inputs
(
(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,(
lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __lowercase ( unittest.TestCase ):
@slow
def __a ( self : List[str] ) -> int:
'''simple docstring'''
lowercase = TFVisionTextDualEncoderModel.from_pretrained(
'''clip-italian/clip-italian''' , logit_scale_init_value=1.0 , from_pt=__lowerCamelCase )
lowercase = VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''' )
lowercase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
lowercase = processor(
text=['''una foto di un gatto''', '''una foto di un cane'''] , images=__lowerCamelCase , padding=__lowerCamelCase , return_tensors='''np''' )
lowercase = model(**__lowerCamelCase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
lowercase = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , __lowerCamelCase , atol=1E-3 ) )
| 604
| 1
|
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
__snake_case : List[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCamelCase__ ( UpperCAmelCase__):
'''simple docstring'''
def __init__( self , A , A , A , A , A , A , A , A , A , ) ->Any:
super().__init__()
if hasattr(scheduler.config , 'steps_offset' ) and scheduler.config.steps_offset != 1:
UpperCAmelCase__ :int = (
F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
' file'
)
deprecate('steps_offset!=1' , '1.0.0' , __a , standard_warn=__a )
UpperCAmelCase__ :List[Any] = dict(scheduler.config )
UpperCAmelCase__ :Optional[int] = 1
UpperCAmelCase__ :Dict = FrozenDict(__a )
if hasattr(scheduler.config , 'skip_prk_steps' ) and scheduler.config.skip_prk_steps is False:
UpperCAmelCase__ :List[str] = (
F"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
' Hub, it would be very nice if you could open a Pull request for the'
' `scheduler/scheduler_config.json` file'
)
deprecate('skip_prk_steps not set' , '1.0.0' , __a , standard_warn=__a )
UpperCAmelCase__ :Union[str, Any] = dict(scheduler.config )
UpperCAmelCase__ :List[str] = True
UpperCAmelCase__ :Any = FrozenDict(__a )
if safety_checker is None:
logger.warning(
F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
' results in services or applications open to the public. Both the diffusers team and Hugging Face'
' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .' )
self.register_modules(
segmentation_model=__a , segmentation_processor=__a , vae=__a , text_encoder=__a , tokenizer=__a , unet=__a , scheduler=__a , safety_checker=__a , feature_extractor=__a , )
def A__ ( self , A = "auto" ) ->Union[str, Any]:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase__ :str = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__a )
def A__ ( self ) ->Union[str, Any]:
self.enable_attention_slicing(__a )
def A__ ( self ) ->Dict:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
UpperCAmelCase__ :str = torch.device('cuda' )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(__a , __a )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def A__ ( self ) ->Tuple:
if self.device != torch.device('meta' ) or not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(__a , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
def __call__( self , A , A , 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 , ) ->Union[str, Any]:
UpperCAmelCase__ :int = self.segmentation_processor(
text=[text] , images=[image] , padding='max_length' , return_tensors='pt' ).to(self.device )
UpperCAmelCase__ :str = self.segmentation_model(**__a )
UpperCAmelCase__ :Optional[Any] = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
UpperCAmelCase__ :Optional[int] = self.numpy_to_pil(__a )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
UpperCAmelCase__ :List[str] = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=__a , image=__a , mask_image=__a , height=__a , width=__a , num_inference_steps=__a , guidance_scale=__a , negative_prompt=__a , num_images_per_prompt=__a , eta=__a , generator=__a , latents=__a , output_type=__a , return_dict=__a , callback=__a , callback_steps=__a , )
| 712
|
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
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.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class UpperCamelCase__ ( UpperCAmelCase__):
'''simple docstring'''
def A__ ( self ) ->Union[str, Any]:
UpperCAmelCase__ :List[Any] = tempfile.mkdtemp()
UpperCAmelCase__ :Union[str, Any] = 8
# DPR tok
UpperCAmelCase__ :List[Any] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
UpperCAmelCase__ :str = os.path.join(self.tmpdirname , 'dpr_tokenizer' )
os.makedirs(A , exist_ok=A )
UpperCAmelCase__ :Tuple = os.path.join(A , 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
UpperCAmelCase__ :Optional[Any] = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
UpperCAmelCase__ :List[Any] = dict(zip(A , range(len(A ) ) ) )
UpperCAmelCase__ :Dict = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
UpperCAmelCase__ :Optional[Any] = {'unk_token': '<unk>'}
UpperCAmelCase__ :int = os.path.join(self.tmpdirname , 'bart_tokenizer' )
os.makedirs(A , exist_ok=A )
UpperCAmelCase__ :Any = os.path.join(A , BART_VOCAB_FILES_NAMES['vocab_file'] )
UpperCAmelCase__ :Optional[int] = os.path.join(A , BART_VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(A ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(A ) )
def A__ ( self ) ->DPRQuestionEncoderTokenizer:
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'dpr_tokenizer' ) )
def A__ ( self ) ->DPRContextEncoderTokenizer:
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'dpr_tokenizer' ) )
def A__ ( self ) ->BartTokenizer:
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'bart_tokenizer' ) )
def A__ ( self ) ->Dict:
shutil.rmtree(self.tmpdirname )
def A__ ( self ) ->Any:
UpperCAmelCase__ :int = Dataset.from_dict(
{
'id': ['0', '1'],
'text': ['foo', 'bar'],
'title': ['Foo', 'Bar'],
'embeddings': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('embeddings' , string_factory='Flat' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def A__ ( self ) ->List[Any]:
UpperCAmelCase__ :Any = self.get_dummy_dataset()
UpperCAmelCase__ :Dict = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('transformers.models.rag.retrieval_rag.load_dataset' ) as mock_load_dataset:
UpperCAmelCase__ :int = dataset
UpperCAmelCase__ :List[str] = RagRetriever(
A , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def A__ ( self , A ) ->List[Any]:
UpperCAmelCase__ :Optional[Any] = self.get_dummy_dataset()
UpperCAmelCase__ :Optional[Any] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='custom' , )
if from_disk:
UpperCAmelCase__ :Union[str, Any] = os.path.join(self.tmpdirname , 'dataset' )
UpperCAmelCase__ :int = os.path.join(self.tmpdirname , 'index.faiss' )
dataset.get_index('embeddings' ).save(os.path.join(self.tmpdirname , 'index.faiss' ) )
dataset.drop_index('embeddings' )
dataset.save_to_disk(os.path.join(self.tmpdirname , 'dataset' ) )
del dataset
UpperCAmelCase__ :Optional[Any] = RagRetriever(
A , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
UpperCAmelCase__ :List[str] = RagRetriever(
A , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , A ) , )
return retriever
def A__ ( self ) ->str:
UpperCAmelCase__ :Union[str, Any] = Dataset.from_dict(
{
'id': ['0', '1'],
'text': ['foo', 'bar'],
'title': ['Foo', 'Bar'],
'embeddings': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('embeddings' , string_factory='Flat' , metric_type=faiss.METRIC_INNER_PRODUCT )
UpperCAmelCase__ :Optional[int] = os.path.join(self.tmpdirname , 'hf_bert_base.hnswSQ8_correct_phi_128.c_index' )
dataset.save_faiss_index('embeddings' , index_file_name + '.index.dpr' )
pickle.dump(dataset['id'] , open(index_file_name + '.index_meta.dpr' , 'wb' ) )
UpperCAmelCase__ :int = os.path.join(self.tmpdirname , 'psgs_w100.tsv.pkl' )
UpperCAmelCase__ :List[str] = {sample['id']: [sample['text'], sample['title']] for sample in dataset}
pickle.dump(A , open(A , 'wb' ) )
UpperCAmelCase__ :List[str] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='legacy' , index_path=self.tmpdirname , )
UpperCAmelCase__ :Dict = RagRetriever(
A , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def A__ ( self ) ->Optional[Any]:
UpperCAmelCase__ :Optional[int] = 1
UpperCAmelCase__ :Tuple = self.get_dummy_canonical_hf_index_retriever()
UpperCAmelCase__ :List[str] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ :int = retriever.retrieve(A , n_docs=A )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(A ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['embeddings', 'id', 'text', 'title'] )
self.assertEqual(len(doc_dicts[0]['id'] ) , A )
self.assertEqual(doc_dicts[0]['id'][0] , '1' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['id'][0] , '0' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A__ ( self ) ->Dict:
UpperCAmelCase__ :List[Any] = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('transformers.models.rag.retrieval_rag.load_dataset' ) as mock_load_dataset:
UpperCAmelCase__ :List[str] = self.get_dummy_dataset()
retriever.save_pretrained(A )
UpperCAmelCase__ :List[Any] = RagRetriever.from_pretrained(A )
self.assertIsInstance(A , A )
UpperCAmelCase__ :Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase__ :List[Any] = retriever.retrieve(A , n_docs=1 )
self.assertTrue(out is not None )
def A__ ( self ) ->Tuple:
UpperCAmelCase__ :Tuple = 1
UpperCAmelCase__ :Union[str, Any] = self.get_dummy_custom_hf_index_retriever(from_disk=A )
UpperCAmelCase__ :Any = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ :List[str] = retriever.retrieve(A , n_docs=A )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(A ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['embeddings', 'id', 'text', 'title'] )
self.assertEqual(len(doc_dicts[0]['id'] ) , A )
self.assertEqual(doc_dicts[0]['id'][0] , '1' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['id'][0] , '0' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A__ ( self ) ->Tuple:
UpperCAmelCase__ :str = self.get_dummy_custom_hf_index_retriever(from_disk=A )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(A )
UpperCAmelCase__ :List[Any] = RagRetriever.from_pretrained(A )
self.assertIsInstance(A , A )
UpperCAmelCase__ :int = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase__ :str = retriever.retrieve(A , n_docs=1 )
self.assertTrue(out is not None )
def A__ ( self ) ->Tuple:
UpperCAmelCase__ :Any = 1
UpperCAmelCase__ :Optional[Any] = self.get_dummy_custom_hf_index_retriever(from_disk=A )
UpperCAmelCase__ :Dict = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ :Any = retriever.retrieve(A , n_docs=A )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(A ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['embeddings', 'id', 'text', 'title'] )
self.assertEqual(len(doc_dicts[0]['id'] ) , A )
self.assertEqual(doc_dicts[0]['id'][0] , '1' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['id'][0] , '0' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A__ ( self ) ->Dict:
UpperCAmelCase__ :Optional[int] = self.get_dummy_custom_hf_index_retriever(from_disk=A )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(A )
UpperCAmelCase__ :int = RagRetriever.from_pretrained(A )
self.assertIsInstance(A , A )
UpperCAmelCase__ :Optional[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase__ :Union[str, Any] = retriever.retrieve(A , n_docs=1 )
self.assertTrue(out is not None )
def A__ ( self ) ->List[str]:
UpperCAmelCase__ :str = 1
UpperCAmelCase__ :List[str] = self.get_dummy_legacy_index_retriever()
UpperCAmelCase__ :str = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ :Dict = retriever.retrieve(A , n_docs=A )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(A ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['text', 'title'] )
self.assertEqual(len(doc_dicts[0]['text'] ) , A )
self.assertEqual(doc_dicts[0]['text'][0] , 'bar' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['text'][0] , 'foo' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A__ ( self ) ->List[str]:
UpperCAmelCase__ :Any = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(A )
UpperCAmelCase__ :str = RagRetriever.from_pretrained(A )
self.assertIsInstance(A , A )
UpperCAmelCase__ :List[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase__ :int = retriever.retrieve(A , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def A__ ( self ) ->List[str]:
import torch
UpperCAmelCase__ :Optional[Any] = 1
UpperCAmelCase__ :Optional[int] = self.get_dummy_canonical_hf_index_retriever()
UpperCAmelCase__ :Union[str, Any] = [[5, 7], [10, 11]]
UpperCAmelCase__ :Any = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase__ :List[str] = retriever(A , A , prefix=retriever.config.generator.prefix , n_docs=A )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ :List[Any] = (
out['context_input_ids'],
out['context_attention_mask'],
out['retrieved_doc_embeds'],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(A , A )
self.assertIsInstance(A , A )
self.assertIsInstance(A , np.ndarray )
UpperCAmelCase__ :List[str] = retriever(
A , A , prefix=retriever.config.generator.prefix , n_docs=A , return_tensors='pt' , )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ :List[str] = ( # noqa: F841
out['context_input_ids'],
out['context_attention_mask'],
out['retrieved_doc_embeds'],
out['doc_ids'],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(A , torch.Tensor )
self.assertIsInstance(A , torch.Tensor )
self.assertIsInstance(A , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def A__ ( self ) ->List[Any]:
UpperCAmelCase__ :Tuple = self.get_dpr_ctx_encoder_tokenizer()
UpperCAmelCase__ :Tuple = 1
UpperCAmelCase__ :Optional[int] = self.get_dummy_custom_hf_index_retriever(from_disk=A )
retriever.set_ctx_encoder_tokenizer(A )
UpperCAmelCase__ :Tuple = [[5, 7], [10, 11]]
UpperCAmelCase__ :Any = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCAmelCase__ :Any = retriever(A , A , prefix=retriever.config.generator.prefix , n_docs=A )
self.assertEqual(
len(A ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask') ) , A ) # check for doc token related keys in dictionary.
| 433
| 0
|
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