
"""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
lowerCAmelCase__				=					'src/transformers'


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


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase__				=					re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase__				=					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.
lowerCAmelCase__				=					re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase__				=					[
    ('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			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''',				__a     )
		return [m.group(0     ) for m in matches]



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		SCREAMING_SNAKE_CASE_    			=			{
		    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.
		SCREAMING_SNAKE_CASE_    			=			collections.defaultdict(__a     )
		SCREAMING_SNAKE_CASE_    			=			collections.defaultdict(__a     )
		SCREAMING_SNAKE_CASE_    			=			collections.defaultdict(__a     )

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

				if lookup_dict is not None:
						while len(__a     ) > 0:
								if attr_name in model_prefix_to_model_type:
										SCREAMING_SNAKE_CASE_    			=			True
										break
								# Try again after removing the last word in the name
								SCREAMING_SNAKE_CASE_    			=			''''''.join(camel_case_split(__a     )[:-1]     )

		SCREAMING_SNAKE_CASE_    			=			set(list(pt_models.keys()     ) + list(tf_models.keys()     ) + list(flax_models.keys()     )     )
		SCREAMING_SNAKE_CASE_    			=			list(__a     )
		all_models.sort()

		SCREAMING_SNAKE_CASE_    			=			{'''model_type''': all_models}
		SCREAMING_SNAKE_CASE_    			=			[pt_models[t] for t in all_models]
		SCREAMING_SNAKE_CASE_    			=			[tf_models[t] for t in all_models]
		SCREAMING_SNAKE_CASE_    			=			[flax_models[t] for t in all_models]

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

		SCREAMING_SNAKE_CASE_    			=			[processors[t] for t in all_models]

		return pd.DataFrame(__a     )



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

            # 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			(			__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			get_frameworks_table()
		SCREAMING_SNAKE_CASE_    			=			Dataset.from_pandas(__a     )

		SCREAMING_SNAKE_CASE_    			=			hf_hub_download(
		    '''huggingface/transformers-metadata''',				'''pipeline_tags.json''',				repo_type='''dataset''',				token=__a     )
		SCREAMING_SNAKE_CASE_    			=			Dataset.from_json(__a     )
		SCREAMING_SNAKE_CASE_    			=			{
		    tags_dataset[i]['''model_class''']: (tags_dataset[i]['''pipeline_tag'''], tags_dataset[i]['''auto_class'''])
		    for i in range(len(__a     )     )
		}
		SCREAMING_SNAKE_CASE_    			=			update_pipeline_and_auto_class_table(__a     )

		# Sort the model classes to avoid some nondeterministic updates to create false update commits.
		SCREAMING_SNAKE_CASE_    			=			sorted(table.keys()     )
		SCREAMING_SNAKE_CASE_    			=			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],
		    }     )
		SCREAMING_SNAKE_CASE_    			=			Dataset.from_pandas(__a     )

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

				if commit_sha is not None:
						SCREAMING_SNAKE_CASE_    			=			(
						    F'Update with commit {commit_sha}\n\nSee: '
						    F'https://github.com/huggingface/transformers/commit/{commit_sha}'
						)
				else:
						SCREAMING_SNAKE_CASE_    			=			'''Update'''

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



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
		SCREAMING_SNAKE_CASE_    			=			transformers_module.pipelines.SUPPORTED_TASKS
		SCREAMING_SNAKE_CASE_    			=			[]
		for key in pipeline_tasks:
				if key not in in_table:
						SCREAMING_SNAKE_CASE_    			=			pipeline_tasks[key]['''pt''']
						if isinstance(__a,				(list, tuple)     ):
								SCREAMING_SNAKE_CASE_    			=			model[0]
						SCREAMING_SNAKE_CASE_    			=			model.__name__
						if model not in in_table.values():
								missing.append(__a     )

		if len(__a     ) > 0:
				SCREAMING_SNAKE_CASE_    			=			''', '''.join(__a     )
				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__":
						lowerCAmelCase__				=					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.')
						lowerCAmelCase__				=					parser.parse_args()

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

"""simple docstring"""




import datasets

from .evaluate import evaluate


lowerCAmelCase__				=					'\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

lowerCAmelCase__				=					'\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'

lowerCAmelCase__				=					'\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the SQuAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_metric = datasets.load_metric("squad")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0}\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,      _KWARGS_DESCRIPTION )
class 			snake_case  (				datasets.Metric ):

							def _lowercase       (self     ):

									"""simple docstring"""







									return datasets.MetricInfo(
									    description=_DESCRIPTION							,							citation=_CITATION							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
									        {
									            '''predictions''': {'''id''': datasets.Value('''string'''     ), '''prediction_text''': datasets.Value('''string'''     )},
									            '''references''': {
									                '''id''': datasets.Value('''string'''     ),
									                '''answers''': datasets.features.Sequence(
									                    {
									                        '''text''': datasets.Value('''string'''     ),
									                        '''answer_start''': datasets.Value('''int32'''     ),
									                    }     ),
									            },
									        }     )							,							codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']							,							reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']							,							)







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
									SCREAMING_SNAKE_CASE_    			=			[
									    {
									        '''paragraphs''': [
									            {
									                '''qas''': [
									                    {
									                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
									                        '''id''': ref['''id'''],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									SCREAMING_SNAKE_CASE_    			=			evaluate(dataset=SCREAMING_SNAKE_CASE_							,							predictions=SCREAMING_SNAKE_CASE_     )
									return score