


from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case__     							= '\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n'

snake_case__     							= '\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n'


snake_case__     							= '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\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\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 .","About 95 species are now accepted .","95 species are now accepted ."]]\n    >>> sari = datasets.load_metric("sari")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 26.953601953601954}\n'
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :int      ,						lowercase__  :Any      ,						lowercase__  :str				)       ->  List[str]:
						__magic_name__				:							Union[str, Any]				=      [rgram for rgrams in rgramslist for rgram in rgrams]
						__magic_name__				:							Optional[Any]				=      Counter(lowercase__				)

						__magic_name__				:							Dict				=      Counter(lowercase__				)
						__magic_name__				:							Dict				=      Counter()
						for sgram, scount in sgramcounter.items():
												__magic_name__				:							Tuple				=      scount * numref

						__magic_name__				:							Any				=      Counter(lowercase__				)
						__magic_name__				:							Any				=      Counter()
						for cgram, ccount in cgramcounter.items():
												__magic_name__				:							Any				=      ccount * numref

						# KEEP
						__magic_name__				:							Any				=      sgramcounter_rep & cgramcounter_rep
						__magic_name__				:							str				=      keepgramcounter_rep & rgramcounter
						__magic_name__				:							Optional[Any]				=      sgramcounter_rep & rgramcounter

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

						# DELETION
						__magic_name__				:							Union[str, Any]				=      sgramcounter_rep - cgramcounter_rep
						__magic_name__				:							Any				=      delgramcounter_rep - rgramcounter
						__magic_name__				:							Any				=      sgramcounter_rep - rgramcounter
						__magic_name__				:							List[Any]				=      0
						__magic_name__				:							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.
						__magic_name__				:							List[str]				=      1
						if len(lowercase__				) > 0:
												__magic_name__				:							Tuple				=      deltmpscorea / len(lowercase__				)

						# ADDITION
						__magic_name__				:							int				=      set(lowercase__				) - set(lowercase__				)
						__magic_name__				:							List[Any]				=      set(lowercase__				) & set(lowercase__				)
						__magic_name__				:							Optional[int]				=      set(lowercase__				) - set(lowercase__				)

						__magic_name__				:							List[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.
						__magic_name__				:							Dict				=      1
						__magic_name__				:							Tuple				=      1
						if len(lowercase__				) > 0:
												__magic_name__				:							str				=      addtmpscore / len(lowercase__				)
						if len(lowercase__				) > 0:
												__magic_name__				:							int				=      addtmpscore / len(lowercase__				)
						__magic_name__				:							str				=      0
						if addscore_precision > 0 or addscore_recall > 0:
												__magic_name__				:							int				=      2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :str      ,						lowercase__  :Optional[Any]				)       ->  List[str]:
						__magic_name__				:							Union[str, Any]				=      len(lowercase__				)

						__magic_name__				:							List[str]				=      ssent.split(' '				)
						__magic_name__				:							List[str]				=      csent.split(' '				)
						__magic_name__				:							List[str]				=      []
						__magic_name__				:							List[str]				=      []
						__magic_name__				:							Any				=      []
						__magic_name__				:							str				=      []
						__magic_name__				:							Union[str, Any]				=      []
						__magic_name__				:							Union[str, Any]				=      []

						__magic_name__				:							Optional[int]				=      []
						__magic_name__				:							Dict				=      []
						__magic_name__				:							str				=      []
						__magic_name__				:							int				=      []
						for rsent in rsents:
												__magic_name__				:							Tuple				=      rsent.split(' '				)
												__magic_name__				:							str				=      []
												__magic_name__				:							Union[str, Any]				=      []
												__magic_name__				:							List[Any]				=      []
												ragramslist.append(lowercase__				)
												for i in range(0      ,						len(lowercase__				) - 1				):
																		if i < len(lowercase__				) - 1:
																								__magic_name__				:							Union[str, Any]				=      ragrams[i] + ' ' + ragrams[i + 1]
																								ragrams.append(lowercase__				)
																		if i < len(lowercase__				) - 2:
																								__magic_name__				:							List[str]				=      ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
																								ragrams.append(lowercase__				)
																		if i < len(lowercase__				) - 3:
																								__magic_name__				:							Any				=      ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
																								ragrams.append(lowercase__				)
												ragramslist.append(lowercase__				)
												ragramslist.append(lowercase__				)
												ragramslist.append(lowercase__				)

						for i in range(0      ,						len(lowercase__				) - 1				):
												if i < len(lowercase__				) - 1:
																		__magic_name__				:							Dict				=      sagrams[i] + ' ' + sagrams[i + 1]
																		sagrams.append(lowercase__				)
												if i < len(lowercase__				) - 2:
																		__magic_name__				:							str				=      sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
																		sagrams.append(lowercase__				)
												if i < len(lowercase__				) - 3:
																		__magic_name__				:							Tuple				=      sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
																		sagrams.append(lowercase__				)

						for i in range(0      ,						len(lowercase__				) - 1				):
												if i < len(lowercase__				) - 1:
																		__magic_name__				:							Optional[Any]				=      cagrams[i] + ' ' + cagrams[i + 1]
																		cagrams.append(lowercase__				)
												if i < len(lowercase__				) - 2:
																		__magic_name__				:							Any				=      cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
																		cagrams.append(lowercase__				)
												if i < len(lowercase__				) - 3:
																		__magic_name__				:							List[Any]				=      cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
																		cagrams.append(lowercase__				)

						((__magic_name__)		, (__magic_name__)		, (__magic_name__))				:							str				=      SARIngram(lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__				)
						((__magic_name__)		, (__magic_name__)		, (__magic_name__))				:							List[Any]				=      SARIngram(lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__				)
						((__magic_name__)		, (__magic_name__)		, (__magic_name__))				:							str				=      SARIngram(lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__				)
						((__magic_name__)		, (__magic_name__)		, (__magic_name__))				:							int				=      SARIngram(lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__				)
						__magic_name__				:							Union[str, Any]				=      sum([keepascore, keepascore, keepascore, keepascore]				) / 4
						__magic_name__				:							str				=      sum([delascore, delascore, delascore, delascore]				) / 4
						__magic_name__				:							int				=      sum([addascore, addascore, addascore, addascore]				) / 4
						__magic_name__				:							Union[str, Any]				=      (avgkeepscore + avgdelscore + avgaddscore) / 3
						return finalscore
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :int      ,						lowercase__  :bool = True      ,						lowercase__  :str = "13a"      ,						lowercase__  :bool = True				)       ->  Tuple:
						# 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:
												__magic_name__				:							List[str]				=      sentence.lower()

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

						if not return_str:
												__magic_name__				:							Tuple				=      normalized_sent.split()

						return normalized_sent



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,       _KWARGS_DESCRIPTION  )
class     snake_case__  (      datasets.Metric  ):
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,      citation=_CITATION     ,      inputs_description=_KWARGS_DESCRIPTION     ,      features=datasets.Features(
										        {
										            'sources': datasets.Value('string'     ,      id='sequence'      ),
										            'predictions': datasets.Value('string'     ,      id='sequence'      ),
										            'references': datasets.Sequence(datasets.Value('string'     ,      id='sequence'      )     ,      id='references'      ),
										        }      )     ,      codebase_urls=[
										        'https://github.com/cocoxu/simplification/blob/master/SARI.py',
										        'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
										    ]     ,      reference_urls=['https://www.aclweb.org/anthology/Q16-1029.pdf']     ,      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if not (len(_UpperCAmelCase      ) == len(_UpperCAmelCase      ) == len(_UpperCAmelCase      )):
																raise ValueError('Sources length must match predictions and references lengths.'      )
										__magic_name__				:							Optional[int]				=      0
										for src, pred, refs in zip(_UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):
																sari_score += SARIsent(normalize(_UpperCAmelCase      )     ,      normalize(_UpperCAmelCase      )     ,      [normalize(_UpperCAmelCase      ) for sent in refs]      )
										__magic_name__				:							Tuple				=      sari_score / len(_UpperCAmelCase      )
										return {"sari": 100 * sari_score}






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case__     							= {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FlaubertForMultipleChoice',
			    'FlaubertForQuestionAnswering',
			    'FlaubertForQuestionAnsweringSimple',
			    'FlaubertForSequenceClassification',
			    'FlaubertForTokenClassification',
			    'FlaubertModel',
			    'FlaubertWithLMHeadModel',
			    'FlaubertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFFlaubertForMultipleChoice',
			    'TFFlaubertForQuestionAnsweringSimple',
			    'TFFlaubertForSequenceClassification',
			    'TFFlaubertForTokenClassification',
			    'TFFlaubertModel',
			    'TFFlaubertPreTrainedModel',
			    'TFFlaubertWithLMHeadModel',
			]


if TYPE_CHECKING:
			from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
			from .tokenization_flaubert import FlaubertTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flaubert import (
						    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaubertForMultipleChoice,
						    FlaubertForQuestionAnswering,
						    FlaubertForQuestionAnsweringSimple,
						    FlaubertForSequenceClassification,
						    FlaubertForTokenClassification,
						    FlaubertModel,
						    FlaubertPreTrainedModel,
						    FlaubertWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_flaubert import (
						    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFlaubertForMultipleChoice,
						    TFFlaubertForQuestionAnsweringSimple,
						    TFFlaubertForSequenceClassification,
						    TFFlaubertForTokenClassification,
						    TFFlaubertModel,
						    TFFlaubertPreTrainedModel,
						    TFFlaubertWithLMHeadModel,
						)

else:
			import sys

			snake_case__     							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


