'''simple docstring'''


import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__lowerCAmelCase	:       List[str]						       =	"\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__lowerCAmelCase	:       str						       =	"\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

__lowerCAmelCase	:       int						       =	"\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"






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





							def       snake_case__						(      self				:							int	)							->	int:
								if version.parse(scb.__version__	) < version.parse('''1.4.12'''	):
									raise ImportWarning(
									    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
									    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''	)
								return datasets.MetricInfo(
								    description=_DESCRIPTION     ,		citation=_CITATION     ,		homepage='''https://github.com/mjpost/sacreBLEU#chrf--chrf'''     ,		inputs_description=_KWARGS_DESCRIPTION     ,		features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''string'''     ,		id='''sequence'''	),
								            '''references''': datasets.Sequence(datasets.Value('''string'''     ,		id='''sequence'''	)     ,		id='''references'''	),
								        }	)     ,		codebase_urls=['''https://github.com/mjpost/sacreBLEU#chrf--chrf''']     ,		reference_urls=[
								        '''https://github.com/m-popovic/chrF''',
								    ]     ,		)






							def       snake_case__						(      self				:							int     ,		__a				:							Optional[Any]     ,		__a				:							int     ,		__a				:							int = CHRF.CHAR_ORDER     ,		__a				:							int = CHRF.WORD_ORDER     ,		__a				:							int = CHRF.BETA     ,		__a				:							bool = False     ,		__a				:							bool = False     ,		__a				:							bool = False     ,		)							->	Tuple:
								__UpperCAmelCase							=			len(references[0]	)
								if any(len(__a	) != references_per_prediction for refs in references	):
									raise ValueError('''Sacrebleu requires the same number of references for each prediction'''	)
								__UpperCAmelCase							=			[[refs[i] for refs in references] for i in range(__a	)]

								__UpperCAmelCase							=			CHRF(__a     ,		__a     ,		__a     ,		__a     ,		__a     ,		__a	)
								__UpperCAmelCase							=			sb_chrf.corpus_score(__a     ,		__a	)

								return {
								    "score": output.score,
								    "char_order": output.char_order,
								    "word_order": output.word_order,
								    "beta": output.beta,
								}

'''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	:       List[Any]						       =	"src/transformers"


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


# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase	:       int						       =	re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__lowerCAmelCase	:       List[Any]						       =	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	:       List[str]						       =	re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCAmelCase	:       Optional[int]						       =	[
    ("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					(		UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''    ,					UpperCamelCase__       )
	return [m.group(0       ) for m in matches]





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	__UpperCAmelCase							=			{
	    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							=			collections.defaultdict(UpperCamelCase__       )
	__UpperCAmelCase							=			collections.defaultdict(UpperCamelCase__       )
	__UpperCAmelCase							=			collections.defaultdict(UpperCamelCase__       )

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

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

	__UpperCAmelCase							=			set(list(pt_models.keys()       ) + list(tf_models.keys()       ) + list(flax_models.keys()       )       )
	__UpperCAmelCase							=			list(UpperCamelCase__       )
	all_models.sort()

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

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

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

	return pd.DataFrame(UpperCamelCase__       )





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


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

            # 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					(		UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	__UpperCAmelCase							=			get_frameworks_table()
	__UpperCAmelCase							=			Dataset.from_pandas(UpperCamelCase__       )

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

	# Sort the model classes to avoid some nondeterministic updates to create false update commits.
	__UpperCAmelCase							=			sorted(table.keys()       )
	__UpperCAmelCase							=			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							=			Dataset.from_pandas(UpperCamelCase__       )

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

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

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





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
	__UpperCAmelCase							=			transformers_module.pipelines.SUPPORTED_TASKS
	__UpperCAmelCase							=			[]
	for key in pipeline_tasks:
		if key not in in_table:
			__UpperCAmelCase							=			pipeline_tasks[key]['''pt''']
			if isinstance(UpperCamelCase__    ,					(list, tuple)       ):
				__UpperCAmelCase							=			model[0]
			__UpperCAmelCase							=			model.__name__
			if model not in in_table.values():
				missing.append(UpperCamelCase__       )

	if len(UpperCamelCase__       ) > 0:
		__UpperCAmelCase							=			''', '''.join(UpperCamelCase__       )
		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	:       Union[str, 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.")
					__lowerCAmelCase	:       Tuple						       =	parser.parse_args()

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