



'''simple docstring'''




import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
a     :    Optional[int]      						=					"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
a     :    Tuple      						=					direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
a     :    Optional[int]      						=					re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
a     :    int      						=					re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
a     :    Tuple      						=					re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a     :    str      						=					[
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[int]:
							UpperCAmelCase  :	Tuple							  =	re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""			,       _lowercase	)
							return [m.group(0	) for m in matches]



def    __lowerCamelCase						(      )   ->    Optional[int]:
							UpperCAmelCase  :	List[Any]							  =	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							UpperCAmelCase  :	List[str]							  =	{
							    config.replace("""Config"""			,       """"""	): model_type for model_type, config in config_maping_names.items()
							}

							# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
							UpperCAmelCase  :	Tuple							  =	collections.defaultdict(_lowercase	)
							UpperCAmelCase  :	List[Any]							  =	collections.defaultdict(_lowercase	)
							UpperCAmelCase  :	List[Any]							  =	collections.defaultdict(_lowercase	)

							# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
							for attr_name in dir(_lowercase	):
														UpperCAmelCase  :	Optional[Any]							  =	None
														if _re_tf_models.match(_lowercase	) is not None:
																					UpperCAmelCase  :	Tuple							  =	tf_models
																					UpperCAmelCase  :	Any							  =	_re_tf_models.match(_lowercase	).groups()[0]
														elif _re_flax_models.match(_lowercase	) is not None:
																					UpperCAmelCase  :	Any							  =	flax_models
																					UpperCAmelCase  :	int							  =	_re_flax_models.match(_lowercase	).groups()[0]
														elif _re_pt_models.match(_lowercase	) is not None:
																					UpperCAmelCase  :	List[str]							  =	pt_models
																					UpperCAmelCase  :	Any							  =	_re_pt_models.match(_lowercase	).groups()[0]

														if lookup_dict is not None:
																					while len(_lowercase	) > 0:
																												if attr_name in model_prefix_to_model_type:
																																			UpperCAmelCase  :	List[str]							  =	True
																																			break
																												# Try again after removing the last word in the name
																												UpperCAmelCase  :	Dict							  =	"""""".join(camel_case_split(_lowercase	)[:-1]	)

							UpperCAmelCase  :	Optional[int]							  =	set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
							UpperCAmelCase  :	Tuple							  =	list(_lowercase	)
							all_models.sort()

							UpperCAmelCase  :	Dict							  =	{"""model_type""": all_models}
							UpperCAmelCase  :	Any							  =	[pt_models[t] for t in all_models]
							UpperCAmelCase  :	Tuple							  =	[tf_models[t] for t in all_models]
							UpperCAmelCase  :	Optional[int]							  =	[flax_models[t] for t in all_models]

							# Now let's use the auto-mapping names to make sure
							UpperCAmelCase  :	Dict							  =	{}
							for t in all_models:
														if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
																					UpperCAmelCase  :	List[Any]							  =	"""AutoProcessor"""
														elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
																					UpperCAmelCase  :	str							  =	"""AutoTokenizer"""
														elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
																					UpperCAmelCase  :	List[str]							  =	"""AutoFeatureExtractor"""
														else:
																					# Default to AutoTokenizer if a model has nothing, for backward compatibility.
																					UpperCAmelCase  :	int							  =	"""AutoTokenizer"""

							UpperCAmelCase  :	Dict							  =	[processors[t] for t in all_models]

							return pd.DataFrame(_lowercase	)



def    __lowerCamelCase						(      _lowercase	)   ->    Any:
							UpperCAmelCase  :	int							  =	[
							    transformers_module.models.auto.modeling_auto,
							    transformers_module.models.auto.modeling_tf_auto,
							    transformers_module.models.auto.modeling_flax_auto,
							]
							for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
														UpperCAmelCase  :	List[Any]							  =	[model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
														UpperCAmelCase  :	Union[str, Any]							  =	[auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
														# Loop through all three frameworks
														for module, cls, mapping in zip(_lowercase			,       _lowercase			,       _lowercase	):
																					# The type of pipeline may not exist in this framework
																					if not hasattr(_lowercase			,       _lowercase	):
																												continue
																					# First extract all model_names
																					UpperCAmelCase  :	Any							  =	[]
																					for name in getattr(_lowercase			,       _lowercase	).values():
																												if isinstance(_lowercase			,       _lowercase	):
																																			model_names.append(_lowercase	)
																												else:
																																			model_names.extend(list(_lowercase	)	)

            # Add pipeline tag and auto model class for those models
																					table.update({model_name: (pipeline_tag, cls) for model_name in model_names}	)

							return table



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    List[str]:
							UpperCAmelCase  :	Optional[Any]							  =	get_frameworks_table()
							UpperCAmelCase  :	Optional[Any]							  =	Dataset.from_pandas(_lowercase	)

							UpperCAmelCase  :	Union[str, Any]							  =	hf_hub_download(
							    """huggingface/transformers-metadata"""			,       """pipeline_tags.json"""			,       repo_type="""dataset"""			,       token=_lowercase	)
							UpperCAmelCase  :	Tuple							  =	Dataset.from_json(_lowercase	)
							UpperCAmelCase  :	Union[str, Any]							  =	{
							    tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
							    for i in range(len(_lowercase	)	)
							}
							UpperCAmelCase  :	List[Any]							  =	update_pipeline_and_auto_class_table(_lowercase	)

							# Sort the model classes to avoid some nondeterministic updates to create false update commits.
							UpperCAmelCase  :	Any							  =	sorted(table.keys()	)
							UpperCAmelCase  :	Any							  =	pd.DataFrame(
							    {
							        """model_class""": model_classes,
							        """pipeline_tag""": [table[m][0] for m in model_classes],
							        """auto_class""": [table[m][1] for m in model_classes],
							    }	)
							UpperCAmelCase  :	int							  =	Dataset.from_pandas(_lowercase	)

							with tempfile.TemporaryDirectory() as tmp_dir:
														frameworks_dataset.to_json(os.path.join(_lowercase			,       """frameworks.json"""	)	)
														tags_dataset.to_json(os.path.join(_lowercase			,       """pipeline_tags.json"""	)	)

														if commit_sha is not None:
																					UpperCAmelCase  :	Optional[Any]							  =	(
																					    F'''Update with commit {commit_sha}\n\nSee: '''
																					    F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
																					)
														else:
																					UpperCAmelCase  :	Optional[int]							  =	"""Update"""

														upload_folder(
														    repo_id="""huggingface/transformers-metadata"""			,       folder_path=_lowercase			,       repo_type="""dataset"""			,       token=_lowercase			,       commit_message=_lowercase			,       )



def    __lowerCamelCase						(      )   ->    Optional[Any]:
							UpperCAmelCase  :	Tuple							  =	{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
							UpperCAmelCase  :	Tuple							  =	transformers_module.pipelines.SUPPORTED_TASKS
							UpperCAmelCase  :	Dict							  =	[]
							for key in pipeline_tasks:
														if key not in in_table:
																					UpperCAmelCase  :	Union[str, Any]							  =	pipeline_tasks[key]["""pt"""]
																					if isinstance(_lowercase			,       (list, tuple)	):
																												UpperCAmelCase  :	int							  =	model[0]
																					UpperCAmelCase  :	Union[str, Any]							  =	model.__name__
																					if model not in in_table.values():
																												missing.append(_lowercase	)

							if len(_lowercase	) > 0:
														UpperCAmelCase  :	Any							  =	""", """.join(_lowercase	)
														raise ValueError(
														    """The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
														    F'''`utils/update_metadata.py`: {msg}. Please add them!'''	)


if __name__ == "__main__":
							a     :    Any      						=					argparse.ArgumentParser()
							parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
							parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
							parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
							a     :    Optional[int]      						=					parser.parse_args()

							if args.check_only:
														check_pipeline_tags()
							else:
														update_metadata(args.token, args.commit_sha)






'''simple docstring'''




def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							UpperCAmelCase  :	Union[str, Any]							  =	set()

							# Replace all the whitespace in our sentence
							UpperCAmelCase  :	List[str]							  =	input_str.replace(""" """			,       """"""	)
							for alpha in input_str:
														if "a" <= alpha.lower() <= "z":
																					frequency.add(alpha.lower()	)
							return len(_lowercase	) == 2_6



def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							UpperCAmelCase  :	Tuple							  =	[False] * 2_6
							for char in input_str:
														if char.islower():
																					UpperCAmelCase  :	Any							  =	True
														elif char.isupper():
																					UpperCAmelCase  :	Union[str, Any]							  =	True
							return all(_lowercase	)



def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							return len({char for char in input_str.lower() if char.isalpha()}	) == 2_6



def    __lowerCamelCase						(      )   ->    None:
							from timeit import timeit

							UpperCAmelCase  :	str							  =	"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
							print(timeit("""is_pangram()"""			,       setup=_lowercase	)	)
							print(timeit("""is_pangram_faster()"""			,       setup=_lowercase	)	)
							print(timeit("""is_pangram_fastest()"""			,       setup=_lowercase	)	)
							# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
							# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							benchmark()

