




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
UpperCamelCase								=	'src/transformers'


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
UpperCamelCase								=	[
    ('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 				_A  (		lowerCAmelCase_	:   Tuple     ):
     """simple docstring"""

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



def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     lowerCAmelCase__            =					{
         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.
     lowerCAmelCase__            =					collections.defaultdict(lowerCAmelCase_     )
     lowerCAmelCase__            =					collections.defaultdict(lowerCAmelCase_     )
     lowerCAmelCase__            =					collections.defaultdict(lowerCAmelCase_     )

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

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

     lowerCAmelCase__            =					set(list(pt_models.keys()     ) + list(tf_models.keys()     ) + list(flax_models.keys()     )     )
     lowerCAmelCase__            =					list(lowerCAmelCase_     )
     all_models.sort()

     lowerCAmelCase__            =					{"model_type": all_models}
     lowerCAmelCase__            =					[pt_models[t] for t in all_models]
     lowerCAmelCase__            =					[tf_models[t] for t in all_models]
     lowerCAmelCase__            =					[flax_models[t] for t in all_models]

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

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

     return pd.DataFrame(lowerCAmelCase_     )



def 				_A  (		lowerCAmelCase_	:   Optional[Any]     ):
     """simple docstring"""

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

            # 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 				_A  (		lowerCAmelCase_	:   Any  ,							lowerCAmelCase_	:   int     ):
     """simple docstring"""

     lowerCAmelCase__            =					get_frameworks_table()
     lowerCAmelCase__            =					Dataset.from_pandas(lowerCAmelCase_     )

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

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

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

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

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



def 				_A  (		):
     """simple docstring"""

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

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

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






'''simple docstring'''


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_snake_case : Any  				=						'\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\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'

_snake_case : Any  				=						'\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


_snake_case : List[Any]  				=						'\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'
def 						snake_case_       (UpperCamelCase			:    Tuple ):





							'''simple docstring'''


							def remove_articles(UpperCamelCase			:    Optional[int] ):
														_a      	=							re.compile(R'''\b(a|an|the)\b'''					,		re.UNICODE )
														return re.sub(UpperCamelCase					,		''' '''					,		UpperCamelCase )

							def white_space_fix(UpperCamelCase			:    Union[str, Any] ):
														return " ".join(text.split() )

							def remove_punc(UpperCamelCase			:    str ):
														_a      	=							set(string.punctuation )
														return "".join(ch for ch in text if ch not in exclude )

							def lower(UpperCamelCase			:    Tuple ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase ) ) ) )
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    Dict ):





							'''simple docstring'''


							return int(normalize_answer(UpperCamelCase ) == normalize_answer(UpperCamelCase ) )
def 						snake_case_       (UpperCamelCase			:    List[str]					,		UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							[any(compute_exact(UpperCamelCase					,		UpperCamelCase ) for ref in refs ) for pred, refs in zip(UpperCamelCase					,		UpperCamelCase )]
							return (sum(UpperCamelCase ) / len(UpperCamelCase )) * 100
def 						snake_case_       (UpperCamelCase			:    Any					,		UpperCamelCase			:    Union[str, Any]					,		UpperCamelCase			:    Dict					,		UpperCamelCase			:    Union[str, Any] ):





							'''simple docstring'''


							_a      	=							[rgram for rgrams in rgramslist for rgram in rgrams]
							_a      	=							Counter(UpperCamelCase )

							_a      	=							Counter(UpperCamelCase )
							_a      	=							Counter()
							for sgram, scount in sgramcounter.items():
														_a      	=							scount * numref

							_a      	=							Counter(UpperCamelCase )
							_a      	=							Counter()
							for cgram, ccount in cgramcounter.items():
														_a      	=							ccount * numref

							# KEEP
							_a      	=							sgramcounter_rep & cgramcounter_rep
							_a      	=							keepgramcounter_rep & rgramcounter
							_a      	=							sgramcounter_rep & rgramcounter

							_a      	=							0
							_a      	=							0
							for keepgram in keepgramcountergood_rep:
														keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
														# Fix an alleged bug [2] in the keep score computation.
														# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
														keeptmpscorea += keepgramcountergood_rep[keepgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							#      a target exactly.
							_a      	=							1
							_a      	=							1
							if len(UpperCamelCase ) > 0:
														_a      	=							keeptmpscorea / len(UpperCamelCase )
							if len(UpperCamelCase ) > 0:
														# Fix an alleged bug [2] in the keep score computation.
														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
														_a      	=							keeptmpscorea / sum(keepgramcounterall_rep.values() )
							_a      	=							0
							if keepscore_precision > 0 or keepscore_recall > 0:
														_a      	=							2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

							# DELETION
							_a      	=							sgramcounter_rep - cgramcounter_rep
							_a      	=							delgramcounter_rep - rgramcounter
							_a      	=							sgramcounter_rep - rgramcounter
							_a      	=							0
							_a      	=							0
							for delgram in delgramcountergood_rep:
														deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
														deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							# a target exactly.
							_a      	=							1
							if len(UpperCamelCase ) > 0:
														_a      	=							deltmpscorea / len(UpperCamelCase )

							# ADDITION
							_a      	=							set(UpperCamelCase ) - set(UpperCamelCase )
							_a      	=							set(UpperCamelCase ) & set(UpperCamelCase )
							_a      	=							set(UpperCamelCase ) - set(UpperCamelCase )

							_a      	=							0
							for addgram in addgramcountergood:
														addtmpscore += 1

							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							# a target exactly.
							_a      	=							1
							_a      	=							1
							if len(UpperCamelCase ) > 0:
														_a      	=							addtmpscore / len(UpperCamelCase )
							if len(UpperCamelCase ) > 0:
														_a      	=							addtmpscore / len(UpperCamelCase )
							_a      	=							0
							if addscore_precision > 0 or addscore_recall > 0:
														_a      	=							2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)
def 						snake_case_       (UpperCamelCase			:    Union[str, Any]					,		UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							len(UpperCamelCase )

							_a      	=							ssent.split(''' ''' )
							_a      	=							csent.split(''' ''' )
							_a      	=							[]
							_a      	=							[]
							_a      	=							[]
							_a      	=							[]
							_a      	=							[]
							_a      	=							[]

							_a      	=							[]
							_a      	=							[]
							_a      	=							[]
							_a      	=							[]
							for rsent in rsents:
														_a      	=							rsent.split(''' ''' )
														_a      	=							[]
														_a      	=							[]
														_a      	=							[]
														ragramslist.append(UpperCamelCase )
														for i in range(0					,		len(UpperCamelCase ) - 1 ):
																					if i < len(UpperCamelCase ) - 1:
																												_a      	=							ragrams[i] + ''' ''' + ragrams[i + 1]
																												ragrams.append(UpperCamelCase )
																					if i < len(UpperCamelCase ) - 2:
																												_a      	=							ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
																												ragrams.append(UpperCamelCase )
																					if i < len(UpperCamelCase ) - 3:
																												_a      	=							ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
																												ragrams.append(UpperCamelCase )
														ragramslist.append(UpperCamelCase )
														ragramslist.append(UpperCamelCase )
														ragramslist.append(UpperCamelCase )

							for i in range(0					,		len(UpperCamelCase ) - 1 ):
														if i < len(UpperCamelCase ) - 1:
																					_a      	=							sagrams[i] + ''' ''' + sagrams[i + 1]
																					sagrams.append(UpperCamelCase )
														if i < len(UpperCamelCase ) - 2:
																					_a      	=							sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
																					sagrams.append(UpperCamelCase )
														if i < len(UpperCamelCase ) - 3:
																					_a      	=							sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
																					sagrams.append(UpperCamelCase )

							for i in range(0					,		len(UpperCamelCase ) - 1 ):
														if i < len(UpperCamelCase ) - 1:
																					_a      	=							cagrams[i] + ''' ''' + cagrams[i + 1]
																					cagrams.append(UpperCamelCase )
														if i < len(UpperCamelCase ) - 2:
																					_a      	=							cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
																					cagrams.append(UpperCamelCase )
														if i < len(UpperCamelCase ) - 3:
																					_a      	=							cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
																					cagrams.append(UpperCamelCase )

							((_a)	,				(_a)	,				(_a))      	=							SARIngram(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							((_a)	,				(_a)	,				(_a))      	=							SARIngram(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							((_a)	,				(_a)	,				(_a))      	=							SARIngram(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							((_a)	,				(_a)	,				(_a))      	=							SARIngram(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							_a      	=							sum([keepascore, keepascore, keepascore, keepascore] ) / 4
							_a      	=							sum([delascore, delascore, delascore, delascore] ) / 4
							_a      	=							sum([addascore, addascore, addascore, addascore] ) / 4
							_a      	=							(avgkeepscore + avgdelscore + avgaddscore) / 3
							return finalscore
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    bool = True					,		UpperCamelCase			:    str = "13a"					,		UpperCamelCase			:    bool = True ):





							'''simple docstring'''


							if lowercase:
														_a      	=							sentence.lower()

							if tokenizer in ["13a", "intl"]:
														if version.parse(sacrebleu.__version__ ).major >= 2:
																					_a      	=							sacrebleu.metrics.bleu._get_tokenizer(UpperCamelCase )()(UpperCamelCase )
														else:
																					_a      	=							sacrebleu.TOKENIZERS[tokenizer]()(UpperCamelCase )
							elif tokenizer == "moses":
														_a      	=							sacremoses.MosesTokenizer().tokenize(UpperCamelCase					,		return_str=UpperCamelCase					,		escape=UpperCamelCase )
							elif tokenizer == "penn":
														_a      	=							sacremoses.MosesTokenizer().penn_tokenize(UpperCamelCase					,		return_str=UpperCamelCase )
							else:
														_a      	=							sentence

							if not return_str:
														_a      	=							normalized_sent.split()

							return normalized_sent
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Dict ):





							'''simple docstring'''


							if not (len(UpperCamelCase ) == len(UpperCamelCase ) == len(UpperCamelCase )):
														raise ValueError('''Sources length must match predictions and references lengths.''' )
							_a      	=							0
							for src, pred, refs in zip(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase ):
														sari_score += SARIsent(normalize(UpperCamelCase )					,		normalize(UpperCamelCase )					,		[normalize(UpperCamelCase ) for sent in refs] )
							_a      	=							sari_score / len(UpperCamelCase )
							return 100 * sari_score
def 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    Tuple					,		UpperCamelCase			:    List[str]="exp"					,		UpperCamelCase			:    List[Any]=None					,		UpperCamelCase			:    Optional[int]=False					,		UpperCamelCase			:    Union[str, Any]=False					,		UpperCamelCase			:    Optional[int]=False					,		):





							'''simple docstring'''


							_a      	=							len(references[0] )
							if any(len(UpperCamelCase ) != references_per_prediction for refs in references ):
														raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
							_a      	=							[[refs[i] for refs in references] for i in range(UpperCamelCase )]
							_a      	=							sacrebleu.corpus_bleu(
							    UpperCamelCase					,		UpperCamelCase					,		smooth_method=UpperCamelCase					,		smooth_value=UpperCamelCase					,		force=UpperCamelCase					,		lowercase=UpperCamelCase					,		use_effective_order=UpperCamelCase					,		)
							return output.score

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







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				Dict:


														"""simple docstring"""


														return datasets.MetricInfo(
														    description=_DESCRIPTION				,     citation=_CITATION				,     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/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
														        '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
														        '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
														        '''https://github.com/mjpost/sacreBLEU''',
														    ]				,     reference_urls=[
														        '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
														        '''https://github.com/mjpost/sacreBLEU''',
														        '''https://en.wikipedia.org/wiki/BLEU''',
														        '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
														    ]				,     )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    Any   )     ->				Dict:


														"""simple docstring"""


														_a      	=							{}
														result.update({'''sari''': compute_sari(sources=lowerCAmelCase_				,     predictions=lowerCAmelCase_				,     references=lowerCAmelCase_   )}   )
														result.update({'''sacrebleu''': compute_sacrebleu(predictions=lowerCAmelCase_				,     references=lowerCAmelCase_   )}   )
														result.update({'''exact''': compute_em(predictions=lowerCAmelCase_				,     references=lowerCAmelCase_   )}   )
														return result

