






'''simple docstring'''







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_A	:			str         ='''\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'''

_A	:			str         ='''\\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'''


_A	:			Dict         ='''\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  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Optional[Any]:

							def remove_articles(UpperCamelCase      ):
														lowerCamelCase__					:	List[str]         =       re.compile(r"""\b(a|an|the)\b"""				,			re.UNICODE      )
														return re.sub(a_				,			""" """				,			a_      )

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

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

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

							return white_space_fix(remove_articles(remove_punc(lower(a_      )      )      )      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Optional[int]:
							return int(normalize_answer(a_      ) == normalize_answer(a_      )      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Any:
							lowerCamelCase__					:	Tuple         =       [any(compute_exact(a_				,			a_      ) for ref in refs      ) for pred, refs in zip(a_				,			a_      )]
							return (sum(a_      ) / len(a_      )) * 100





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[str]:
							lowerCamelCase__					:	Tuple         =       [rgram for rgrams in rgramslist for rgram in rgrams]
							lowerCamelCase__					:	str         =       Counter(a_      )

							lowerCamelCase__					:	Tuple         =       Counter(a_      )
							lowerCamelCase__					:	str         =       Counter()
							for sgram, scount in sgramcounter.items():
														lowerCamelCase__					:	List[str]         =       scount * numref

							lowerCamelCase__					:	Any         =       Counter(a_      )
							lowerCamelCase__					:	Tuple         =       Counter()
							for cgram, ccount in cgramcounter.items():
														lowerCamelCase__					:	Union[str, Any]         =       ccount * numref

							# KEEP
							lowerCamelCase__					:	Union[str, Any]         =       sgramcounter_rep & cgramcounter_rep
							lowerCamelCase__					:	Optional[Any]         =       keepgramcounter_rep & rgramcounter
							lowerCamelCase__					:	Dict         =       sgramcounter_rep & rgramcounter

							lowerCamelCase__					:	str         =       0
							lowerCamelCase__					:	Union[str, Any]         =       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__					:	Any         =       1
							lowerCamelCase__					:	int         =       1
							if len(a_      ) > 0:
														lowerCamelCase__					:	Optional[int]         =       keeptmpscorea / len(a_      )
							if len(a_      ) > 0:
														# Fix an alleged bug [2] in the keep score computation.
														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
														lowerCamelCase__					:	List[Any]         =       keeptmpscorea / sum(keepgramcounterall_rep.values()      )
							lowerCamelCase__					:	List[str]         =       0
							if keepscore_precision > 0 or keepscore_recall > 0:
														lowerCamelCase__					:	int         =       2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

							# DELETION
							lowerCamelCase__					:	List[Any]         =       sgramcounter_rep - cgramcounter_rep
							lowerCamelCase__					:	int         =       delgramcounter_rep - rgramcounter
							lowerCamelCase__					:	Optional[Any]         =       sgramcounter_rep - rgramcounter
							lowerCamelCase__					:	List[Any]         =       0
							lowerCamelCase__					:	List[str]         =       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__					:	Dict         =       1
							if len(a_      ) > 0:
														lowerCamelCase__					:	Any         =       deltmpscorea / len(a_      )

							# ADDITION
							lowerCamelCase__					:	Optional[int]         =       set(a_      ) - set(a_      )
							lowerCamelCase__					:	str         =       set(a_      ) & set(a_      )
							lowerCamelCase__					:	Optional[int]         =       set(a_      ) - set(a_      )

							lowerCamelCase__					:	Union[str, Any]         =       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__					:	Optional[int]         =       1
							lowerCamelCase__					:	List[str]         =       1
							if len(a_      ) > 0:
														lowerCamelCase__					:	Union[str, Any]         =       addtmpscore / len(a_      )
							if len(a_      ) > 0:
														lowerCamelCase__					:	List[Any]         =       addtmpscore / len(a_      )
							lowerCamelCase__					:	Tuple         =       0
							if addscore_precision > 0 or addscore_recall > 0:
														lowerCamelCase__					:	Optional[Any]         =       2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Any:
							lowerCamelCase__					:	int         =       len(a_      )

							lowerCamelCase__					:	int         =       ssent.split(""" """      )
							lowerCamelCase__					:	Dict         =       csent.split(""" """      )
							lowerCamelCase__					:	str         =       []
							lowerCamelCase__					:	Optional[int]         =       []
							lowerCamelCase__					:	Tuple         =       []
							lowerCamelCase__					:	Any         =       []
							lowerCamelCase__					:	Dict         =       []
							lowerCamelCase__					:	Any         =       []

							lowerCamelCase__					:	List[Any]         =       []
							lowerCamelCase__					:	int         =       []
							lowerCamelCase__					:	str         =       []
							lowerCamelCase__					:	int         =       []
							for rsent in rsents:
														lowerCamelCase__					:	List[Any]         =       rsent.split(""" """      )
														lowerCamelCase__					:	str         =       []
														lowerCamelCase__					:	Dict         =       []
														lowerCamelCase__					:	List[Any]         =       []
														ragramslist.append(a_      )
														for i in range(0				,			len(a_      ) - 1      ):
																					if i < len(a_      ) - 1:
																												lowerCamelCase__					:	Optional[Any]         =       ragrams[i] + ''' ''' + ragrams[i + 1]
																												ragrams.append(a_      )
																					if i < len(a_      ) - 2:
																												lowerCamelCase__					:	Any         =       ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
																												ragrams.append(a_      )
																					if i < len(a_      ) - 3:
																												lowerCamelCase__					:	Optional[int]         =       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__					:	Union[str, Any]         =       sagrams[i] + ''' ''' + sagrams[i + 1]
																					sagrams.append(a_      )
														if i < len(a_      ) - 2:
																					lowerCamelCase__					:	Optional[int]         =       sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
																					sagrams.append(a_      )
														if i < len(a_      ) - 3:
																					lowerCamelCase__					:	List[Any]         =       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__					:	Any         =       cagrams[i] + ''' ''' + cagrams[i + 1]
																					cagrams.append(a_      )
														if i < len(a_      ) - 2:
																					lowerCamelCase__					:	Optional[int]         =       cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
																					cagrams.append(a_      )
														if i < len(a_      ) - 3:
																					lowerCamelCase__					:	Optional[int]         =       cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
																					cagrams.append(a_      )

							(lowerCamelCase__)					:	Optional[Any]         =       SARIngram(a_				,			a_				,			a_				,			a_      )
							(lowerCamelCase__)					:	Union[str, Any]         =       SARIngram(a_				,			a_				,			a_				,			a_      )
							(lowerCamelCase__)					:	Union[str, Any]         =       SARIngram(a_				,			a_				,			a_				,			a_      )
							(lowerCamelCase__)					:	Any         =       SARIngram(a_				,			a_				,			a_				,			a_      )
							lowerCamelCase__					:	Optional[Any]         =       sum([keepascore, keepascore, keepascore, keepascore]      ) / 4
							lowerCamelCase__					:	Union[str, Any]         =       sum([delascore, delascore, delascore, delascore]      ) / 4
							lowerCamelCase__					:	Optional[int]         =       sum([addascore, addascore, addascore, addascore]      ) / 4
							lowerCamelCase__					:	Optional[Any]         =       (avgkeepscore + avgdelscore + avgaddscore) / 3
							return finalscore





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase = True				,			UpperCamelCase = "13a"				,			UpperCamelCase = True      )      ->       Any:
							# 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__					:	Tuple         =       sentence.lower()

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

							if not return_str:
														lowerCamelCase__					:	int         =       normalized_sent.split()

							return normalized_sent





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Tuple:
							if not (len(a_      ) == len(a_      ) == len(a_      )):
														raise ValueError("""Sources length must match predictions and references lengths."""      )
							lowerCamelCase__					:	Dict         =       0
							for src, pred, refs in zip(a_				,			a_				,			a_      ):
														sari_score += SARIsent(normalize(a_      )				,			normalize(a_      )				,			[normalize(a_      ) for sent in refs]      )
							lowerCamelCase__					:	Any         =       sari_score / len(a_      )
							return 100 * sari_score





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase="exp"				,			UpperCamelCase=None				,			UpperCamelCase=False				,			UpperCamelCase=False				,			UpperCamelCase=False				,			)      ->       Dict:
							lowerCamelCase__					:	str         =       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__					:	Union[str, Any]         =       [[refs[i] for refs in references] for i in range(a_      )]
							lowerCamelCase__					:	List[Any]         =       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 _lowercase	(				datasets.Metric		):




							def 		lowerCamelCase_				(							self:				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 		lowerCamelCase_				(							self:				List[Any]			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Tuple			,							UpperCamelCase__:				Union[str, Any]     ):
														lowerCamelCase__					:	Dict         =       {}
														result.update({"""sari""": compute_sari(sources=SCREAMING_SNAKE_CASE__			,							predictions=SCREAMING_SNAKE_CASE__			,							references=SCREAMING_SNAKE_CASE__     )}     )
														result.update({"""sacrebleu""": compute_sacrebleu(predictions=SCREAMING_SNAKE_CASE__			,							references=SCREAMING_SNAKE_CASE__     )}     )
														result.update({"""exact""": compute_em(predictions=SCREAMING_SNAKE_CASE__			,							references=SCREAMING_SNAKE_CASE__     )}     )
														return result








"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  '''xmod'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=3_05_22		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__="absolute"		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=("en_XX",)		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





             """simple docstring"""






             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  vocab_size
             __SCREAMING_SNAKE_CASE   :List[Any]						=  hidden_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Tuple						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Dict						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :str						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  max_position_embeddings
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  type_vocab_size
             __SCREAMING_SNAKE_CASE   :str						=  initializer_range
             __SCREAMING_SNAKE_CASE   :List[Any]						=  layer_norm_eps
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  position_embedding_type
             __SCREAMING_SNAKE_CASE   :Any						=  use_cache
             __SCREAMING_SNAKE_CASE   :List[str]						=  classifier_dropout
             __SCREAMING_SNAKE_CASE   :Any						=  pre_norm
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_reduction_factor
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_layer_norm
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_reuse_layer_norm
             __SCREAMING_SNAKE_CASE   :Tuple						=  ln_before_adapter
             __SCREAMING_SNAKE_CASE   :Any						=  list(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  default_language






class 					_SCREAMING_SNAKE_CASE(   A  ):




       @property
       def     _UpperCamelCase     (						self							)			->      Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task == "multiple-choice":
                   __SCREAMING_SNAKE_CASE   :Tuple						=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  {0: '''batch''', 1: '''sequence'''}
             return OrderedDict(
                 [
                     ('''input_ids''', dynamic_axis),
                     ('''attention_mask''', dynamic_axis),
                 ]							)