





"""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
__magic_name__       :					int						=			"""src/transformers"""


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


# Regexes that match TF/Flax/PT model names.
__magic_name__       :					Dict						=			re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__magic_name__       :					Tuple						=			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.
__magic_name__       :					Tuple						=			re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__magic_name__       :					Tuple						=			[
    ("""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      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Any				   =			re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""							,     SCREAMING_SNAKE_CASE     )
						return [m.group(0     ) for m in matches]

def      UpperCamelCase							():
						UpperCamelCase				:		Union[str, 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				:		Dict				   =			collections.defaultdict(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Union[str, Any]				   =			collections.defaultdict(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Dict				   =			collections.defaultdict(SCREAMING_SNAKE_CASE     )

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

												if lookup_dict is not None:
																		while len(SCREAMING_SNAKE_CASE     ) > 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				:		int				   =			"""""".join(camel_case_split(SCREAMING_SNAKE_CASE     )[:-1]     )

						UpperCamelCase				:		Any				   =			set(list(pt_models.keys()     ) + list(tf_models.keys()     ) + list(flax_models.keys()     )     )
						UpperCamelCase				:		List[Any]				   =			list(SCREAMING_SNAKE_CASE     )
						all_models.sort()

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

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

						UpperCamelCase				:		int				   =			[processors[t] for t in all_models]

						return pd.DataFrame(SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[Any]				   =			[
						    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				:		Any				   =			[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
												UpperCamelCase				:		str				   =			[auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
												# Loop through all three frameworks
												for module, cls, mapping in zip(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
																		# The type of pipeline may not exist in this framework
																		if not hasattr(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
																								continue
																		# First extract all model_names
																		UpperCamelCase				:		List[str]				   =			[]
																		for name in getattr(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ).values():
																								if isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
																														model_names.append(SCREAMING_SNAKE_CASE     )
																								else:
																														model_names.extend(list(SCREAMING_SNAKE_CASE     )     )

            # 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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[Any]				   =			get_frameworks_table()
						UpperCamelCase				:		str				   =			Dataset.from_pandas(SCREAMING_SNAKE_CASE     )

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

						# Sort the model classes to avoid some nondeterministic updates to create false update commits.
						UpperCamelCase				:		Tuple				   =			sorted(table.keys()     )
						UpperCamelCase				:		str				   =			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				:		Tuple				   =			Dataset.from_pandas(SCREAMING_SNAKE_CASE     )

						with tempfile.TemporaryDirectory() as tmp_dir:
												frameworks_dataset.to_json(os.path.join(SCREAMING_SNAKE_CASE							,     """frameworks.json"""     )     )
												tags_dataset.to_json(os.path.join(SCREAMING_SNAKE_CASE							,     """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=SCREAMING_SNAKE_CASE							,     repo_type="""dataset"""							,     token=SCREAMING_SNAKE_CASE							,     commit_message=SCREAMING_SNAKE_CASE							,     )

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

						if len(SCREAMING_SNAKE_CASE     ) > 0:
												UpperCamelCase				:		Union[str, Any]				   =			""", """.join(SCREAMING_SNAKE_CASE     )
												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__":
		__magic_name__       :					Dict						=			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.""")
		__magic_name__       :					List[Any]						=			parser.parse_args()

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










"""simple docstring"""


from bisect import bisect
from itertools import accumulate

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Dict				   =			sorted(zip(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )							,     key=lambda SCREAMING_SNAKE_CASE     : x[0] / x[1]							,     reverse=SCREAMING_SNAKE_CASE     )
						UpperCamelCase   ,						UpperCamelCase				:		int				   =			[i[0] for i in r], [i[1] for i in r]
						UpperCamelCase				:		Optional[Any]				   =			list(accumulate(SCREAMING_SNAKE_CASE     )     )
						UpperCamelCase				:		Optional[Any]				   =			bisect(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						return (
						    0
						    if k == 0
						    else sum(vl[:k]     ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
						    if k != n
						    else sum(vl[:k]     )
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()



