




"""simple docstring"""

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__magic_name__				     =     """
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__magic_name__				     =     """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


__magic_name__				     =     """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
"""




def     _A					( __lowercase    ):





       """simple docstring"""



       def remove_articles(__lowercase    ):
              lowerCamelCase__          =						re.compile(r"""\b(a|an|the)\b"""							,     re.UNICODE    )
              return re.sub(__UpperCamelCase							,     """ """							,     __UpperCamelCase    )

       def white_space_fix(__lowercase    ):
              return " ".join(text.split()    )

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

       def lower(__lowercase    ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase    )    )    )    )




def     _A					( __lowercase							,     __lowercase    ):





       """simple docstring"""



       return int(normalize_answer(__UpperCamelCase    ) == normalize_answer(__UpperCamelCase    )    )




def     _A					( __lowercase							,     __lowercase    ):





       """simple docstring"""



       lowerCamelCase__          =						[any(compute_exact(__UpperCamelCase							,     __UpperCamelCase    ) for ref in refs    ) for pred, refs in zip(__UpperCamelCase							,     __UpperCamelCase    )]
       return (sum(__UpperCamelCase    ) / len(__UpperCamelCase    )) * 100




def     _A					( __lowercase							,     __lowercase							,     __lowercase							,     __lowercase    ):





       """simple docstring"""



       lowerCamelCase__          =						[rgram for rgrams in rgramslist for rgram in rgrams]
       lowerCamelCase__          =						Counter(__UpperCamelCase    )

       lowerCamelCase__          =						Counter(__UpperCamelCase    )
       lowerCamelCase__          =						Counter()
       for sgram, scount in sgramcounter.items():
              lowerCamelCase__          =						scount * numref

       lowerCamelCase__          =						Counter(__UpperCamelCase    )
       lowerCamelCase__          =						Counter()
       for cgram, ccount in cgramcounter.items():
              lowerCamelCase__          =						ccount * numref

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

       lowerCamelCase__          =						0
       lowerCamelCase__          =						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.
       lowerCamelCase__          =						1
       lowerCamelCase__          =						1
       if len(__UpperCamelCase    ) > 0:
              lowerCamelCase__          =						keeptmpscorea / len(__UpperCamelCase    )
       if len(__UpperCamelCase    ) > 0:
              # Fix an alleged bug [2] in the keep score computation.
              # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
              lowerCamelCase__          =						keeptmpscorea / sum(keepgramcounterall_rep.values()    )
       lowerCamelCase__          =						0
       if keepscore_precision > 0 or keepscore_recall > 0:
              lowerCamelCase__          =						2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

       # DELETION
       lowerCamelCase__          =						sgramcounter_rep - cgramcounter_rep
       lowerCamelCase__          =						delgramcounter_rep - rgramcounter
       lowerCamelCase__          =						sgramcounter_rep - rgramcounter
       lowerCamelCase__          =						0
       lowerCamelCase__          =						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.
       lowerCamelCase__          =						1
       if len(__UpperCamelCase    ) > 0:
              lowerCamelCase__          =						deltmpscorea / len(__UpperCamelCase    )

       # ADDITION
       lowerCamelCase__          =						set(__UpperCamelCase    ) - set(__UpperCamelCase    )
       lowerCamelCase__          =						set(__UpperCamelCase    ) & set(__UpperCamelCase    )
       lowerCamelCase__          =						set(__UpperCamelCase    ) - set(__UpperCamelCase    )

       lowerCamelCase__          =						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.
       lowerCamelCase__          =						1
       lowerCamelCase__          =						1
       if len(__UpperCamelCase    ) > 0:
              lowerCamelCase__          =						addtmpscore / len(__UpperCamelCase    )
       if len(__UpperCamelCase    ) > 0:
              lowerCamelCase__          =						addtmpscore / len(__UpperCamelCase    )
       lowerCamelCase__          =						0
       if addscore_precision > 0 or addscore_recall > 0:
              lowerCamelCase__          =						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

       return (keepscore, delscore_precision, addscore)




def     _A					( __lowercase							,     __lowercase							,     __lowercase    ):





       """simple docstring"""



       lowerCamelCase__          =						len(__UpperCamelCase    )

       lowerCamelCase__          =						ssent.split(""" """    )
       lowerCamelCase__          =						csent.split(""" """    )
       lowerCamelCase__          =						[]
       lowerCamelCase__          =						[]
       lowerCamelCase__          =						[]
       lowerCamelCase__          =						[]
       lowerCamelCase__          =						[]
       lowerCamelCase__          =						[]

       lowerCamelCase__          =						[]
       lowerCamelCase__          =						[]
       lowerCamelCase__          =						[]
       lowerCamelCase__          =						[]
       for rsent in rsents:
              lowerCamelCase__          =						rsent.split(""" """    )
              lowerCamelCase__          =						[]
              lowerCamelCase__          =						[]
              lowerCamelCase__          =						[]
              ragramslist.append(__UpperCamelCase    )
              for i in range(0							,     len(__UpperCamelCase    ) - 1    ):
                     if i < len(__UpperCamelCase    ) - 1:
                            lowerCamelCase__          =						ragrams[i] + """ """ + ragrams[i + 1]
                            ragrams.append(__UpperCamelCase    )
                     if i < len(__UpperCamelCase    ) - 2:
                            lowerCamelCase__          =						ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
                            ragrams.append(__UpperCamelCase    )
                     if i < len(__UpperCamelCase    ) - 3:
                            lowerCamelCase__          =						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:
                     lowerCamelCase__          =						sagrams[i] + """ """ + sagrams[i + 1]
                     sagrams.append(__UpperCamelCase    )
              if i < len(__UpperCamelCase    ) - 2:
                     lowerCamelCase__          =						sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
                     sagrams.append(__UpperCamelCase    )
              if i < len(__UpperCamelCase    ) - 3:
                     lowerCamelCase__          =						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:
                     lowerCamelCase__          =						cagrams[i] + """ """ + cagrams[i + 1]
                     cagrams.append(__UpperCamelCase    )
              if i < len(__UpperCamelCase    ) - 2:
                     lowerCamelCase__          =						cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
                     cagrams.append(__UpperCamelCase    )
              if i < len(__UpperCamelCase    ) - 3:
                     lowerCamelCase__          =						cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
                     cagrams.append(__UpperCamelCase    )

       ((lowerCamelCase__)     ,	(lowerCamelCase__)     ,	(lowerCamelCase__))          =						SARIngram(__UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase    )
       ((lowerCamelCase__)     ,	(lowerCamelCase__)     ,	(lowerCamelCase__))          =						SARIngram(__UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase    )
       ((lowerCamelCase__)     ,	(lowerCamelCase__)     ,	(lowerCamelCase__))          =						SARIngram(__UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase    )
       ((lowerCamelCase__)     ,	(lowerCamelCase__)     ,	(lowerCamelCase__))          =						SARIngram(__UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase    )
       lowerCamelCase__          =						sum([keepascore, keepascore, keepascore, keepascore]    ) / 4
       lowerCamelCase__          =						sum([delascore, delascore, delascore, delascore]    ) / 4
       lowerCamelCase__          =						sum([addascore, addascore, addascore, addascore]    ) / 4
       lowerCamelCase__          =						(avgkeepscore + avgdelscore + avgaddscore) / 3
       return finalscore




def     _A					( __lowercase							,     __lowercase = True							,     __lowercase = "13a"							,     __lowercase = True    ):





       """simple docstring"""



       if lowercase:
              lowerCamelCase__          =						sentence.lower()

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

       if not return_str:
              lowerCamelCase__          =						normalized_sent.split()

       return normalized_sent




def     _A					( __lowercase							,     __lowercase							,     __lowercase    ):





       """simple docstring"""



       if not (len(__UpperCamelCase    ) == len(__UpperCamelCase    ) == len(__UpperCamelCase    )):
              raise ValueError("""Sources length must match predictions and references lengths."""    )
       lowerCamelCase__          =						0
       for src, pred, refs in zip(__UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase    ):
              sari_score += SARIsent(normalize(__UpperCamelCase    )							,     normalize(__UpperCamelCase    )							,     [normalize(__UpperCamelCase    ) for sent in refs]    )
       lowerCamelCase__          =						sari_score / len(__UpperCamelCase    )
       return 100 * sari_score




def     _A					( __lowercase							,     __lowercase							,     __lowercase="exp"							,     __lowercase=None							,     __lowercase=False							,     __lowercase=False							,     __lowercase=False							,     ):





       """simple docstring"""



       lowerCamelCase__          =						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"""    )
       lowerCamelCase__          =						[[refs[i] for refs in references] for i in range(__UpperCamelCase    )]
       lowerCamelCase__          =						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        SCREAMING_SNAKE_CASE__   (						datasets.Metric      ):
   def    __UpperCAmelCase       (       self			:				List[Any]							):
          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    __UpperCAmelCase       (       self			:				Any   ,	SCREAMING_SNAKE_CASE_			:				Optional[int]   ,	SCREAMING_SNAKE_CASE_			:				List[Any]   ,	SCREAMING_SNAKE_CASE_			:				Optional[Any]							):
          lowerCamelCase__          =						{}
          result.update({"""sari""": compute_sari(sources=_snake_case   ,	predictions=_snake_case   ,	references=_snake_case							)}							)
          result.update({"""sacrebleu""": compute_sacrebleu(predictions=_snake_case   ,	references=_snake_case							)}							)
          result.update({"""exact""": compute_em(predictions=_snake_case   ,	references=_snake_case							)}							)
          return result











import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE__   			=   '''docs/source/en/_toctree.yml'''


def A    (							__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     defaultdict(__UpperCamelCase       )
			for doc in model_doc:
						counts[doc["local"]] += 1
			A__	  =     [key for key, value in counts.items() if value > 1]

			A__	  =     []
			for duplicate_key in duplicates:
						A__	  =     list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}       )
						if len(__UpperCamelCase       ) > 1:
									raise ValueError(
									    f'''{duplicate_key} is present several times in the documentation table of content at '''
									    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
									    'others.'       )
						# Only add this once
						new_doc.append({'local': duplicate_key, 'title': titles[0]}       )

			# Add none duplicate-keys
			new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]       )

			# Sort
			return sorted(__UpperCamelCase			,						key=lambda __UpperCamelCase       : s["title"].lower()       )


def A    (							__UpperCamelCase=False       )    ->   str:
			with open(__UpperCamelCase			,						encoding='utf-8'       ) as f:
						A__	  =     yaml.safe_load(f.read()       )

			# Get to the API doc
			A__	  =     0
			while content[api_idx]["title"] != "API":
						api_idx += 1
			A__	  =     content[api_idx]['sections']

			# Then to the model doc
			A__	  =     0
			while api_doc[model_idx]["title"] != "Models":
						model_idx += 1

			A__	  =     api_doc[model_idx]['sections']

			A__	  =     [(idx, section) for idx, section in enumerate(__UpperCamelCase       ) if 'sections' in section]
			A__	  =     False
			for idx, modality_doc in modalities_docs:
						A__	  =     modality_doc['sections']
						A__	  =     clean_model_doc_toc(__UpperCamelCase       )

						if old_modality_doc != new_modality_doc:
									A__	  =     True
									if overwrite:
												A__	  =     new_modality_doc

			if diff:
						if overwrite:
									A__	  =     model_doc
									A__	  =     api_doc
									with open(__UpperCamelCase			,						'w'			,						encoding='utf-8'       ) as f:
												f.write(yaml.dump(__UpperCamelCase			,						allow_unicode=__UpperCamelCase       )       )
						else:
									raise ValueError(
									    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							check_model_doc(args.fix_and_overwrite)




