




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_A     =			'''
@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",
}
'''

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


_A     =			'''
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 __UpperCamelCase			(       _A  ):

						def remove_articles(_A  ):
												lowerCAmelCase_      			=  re.compile(r'''\b(a|an|the)\b''' ,  re.UNICODE  )
												return re.sub(_A ,  ''' ''' ,  _A  )

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

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

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

						return white_space_fix(remove_articles(remove_punc(lower(_A  )  )  )  )





def __UpperCamelCase			(       _A ,  _A  ):
						return int(normalize_answer(_A  ) == normalize_answer(_A  )  )





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





def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  [rgram for rgrams in rgramslist for rgram in rgrams]
						lowerCAmelCase_      			=  Counter(_A  )

						lowerCAmelCase_      			=  Counter(_A  )
						lowerCAmelCase_      			=  Counter()
						for sgram, scount in sgramcounter.items():
												lowerCAmelCase_      			=  scount * numref

						lowerCAmelCase_      			=  Counter(_A  )
						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(_A  ) > 0:
												lowerCAmelCase_      			=  keeptmpscorea / len(_A  )
						if len(_A  ) > 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(_A  ) > 0:
												lowerCAmelCase_      			=  deltmpscorea / len(_A  )

						# ADDITION
						lowerCAmelCase_      			=  set(_A  ) - set(_A  )
						lowerCAmelCase_      			=  set(_A  ) & set(_A  )
						lowerCAmelCase_      			=  set(_A  ) - set(_A  )

						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(_A  ) > 0:
												lowerCAmelCase_      			=  addtmpscore / len(_A  )
						if len(_A  ) > 0:
												lowerCAmelCase_      			=  addtmpscore / len(_A  )
						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 __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  len(_A  )

						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(_A  )
												for i in range(0 ,  len(_A  ) - 1  ):
																		if i < len(_A  ) - 1:
																								lowerCAmelCase_      			=  ragrams[i] + ''' ''' + ragrams[i + 1]
																								ragrams.append(_A  )
																		if i < len(_A  ) - 2:
																								lowerCAmelCase_      			=  ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
																								ragrams.append(_A  )
																		if i < len(_A  ) - 3:
																								lowerCAmelCase_      			=  ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
																								ragrams.append(_A  )
												ragramslist.append(_A  )
												ragramslist.append(_A  )
												ragramslist.append(_A  )

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

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

						((lowerCAmelCase_)			,					(lowerCAmelCase_)			,					(lowerCAmelCase_))      			=  SARIngram(_A ,  _A ,  _A ,  _A  )
						((lowerCAmelCase_)			,					(lowerCAmelCase_)			,					(lowerCAmelCase_))      			=  SARIngram(_A ,  _A ,  _A ,  _A  )
						((lowerCAmelCase_)			,					(lowerCAmelCase_)			,					(lowerCAmelCase_))      			=  SARIngram(_A ,  _A ,  _A ,  _A  )
						((lowerCAmelCase_)			,					(lowerCAmelCase_)			,					(lowerCAmelCase_))      			=  SARIngram(_A ,  _A ,  _A ,  _A  )
						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 __UpperCamelCase			(       _A ,  _A = True ,  _A = "13a" ,  _A = True  ):
						# Normalization is requried for the ASSET dataset (one of the primary
						# datasets in sentence simplification) to allow using space
						# to split the sentence. Even though Wiki-Auto and TURK datasets,
						# do not require normalization, we do it for consistency.
						# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
						# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

						if lowercase:
												lowerCAmelCase_      			=  sentence.lower()

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

						if not return_str:
												lowerCAmelCase_      			=  normalized_sent.split()

						return normalized_sent





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





def __UpperCamelCase			(       _A ,  _A ,  _A="exp" ,  _A=None ,  _A=False ,  _A=False ,  _A=False ,  ):
						lowerCAmelCase_      			=  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'''  )
						lowerCAmelCase_      			=  [[refs[i] for refs in references] for i in range(_A  )]
						lowerCAmelCase_      			=  sacrebleu.corpus_bleu(
						    _A ,  _A ,  smooth_method=_A ,  smooth_value=_A ,  force=_A ,  lowercase=_A ,  use_effective_order=_A ,  )
						return output.score






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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""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        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



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










import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_A     =			'''scheduler_config.json'''


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   1
		__snake_case									=   2
		__snake_case									=   3
		__snake_case									=   4
		__snake_case									=   5
		__snake_case									=   6
		__snake_case									=   7
		__snake_case									=   8
		__snake_case									=   9
		__snake_case									=   10
		__snake_case									=   11
		__snake_case									=   12
		__snake_case									=   13
		__snake_case									=   14







@dataclass
class  A		(  __UpperCAmelCase     ):
		__snake_case									=   42







class  A		:
		__snake_case									=   SCHEDULER_CONFIG_NAME
		__snake_case									=   []
		__snake_case									=   True



		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__=False,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_      			=  cls.load_config(
								    pretrained_model_name_or_path=UpperCamelCase__,	subfolder=UpperCamelCase__,	return_unused_kwargs=UpperCamelCase__,	return_commit_hash=UpperCamelCase__,	**UpperCamelCase__,	)
								return cls.from_config(UpperCamelCase__,	return_unused_kwargs=UpperCamelCase__,	**UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = False,	**UpperCamelCase__   ):







								"""simple docstring"""



								self.save_config(save_directory=UpperCamelCase__,	push_to_hub=UpperCamelCase__,	**UpperCamelCase__   )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self._get_compatibles()







		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  list(set([cls.__name__] + cls._compatibles   )   )
								lowerCAmelCase_      			=  importlib.import_module(__name__.split('''.'''   )[0]   )
								lowerCAmelCase_      			=  [
								    getattr(UpperCamelCase__,	UpperCamelCase__   ) for c in compatible_classes_str if hasattr(UpperCamelCase__,	UpperCamelCase__   )
								]
								return compatible_classes




