


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case_						:			str													=							'''
@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",
}
'''

snake_case_						:			List[str]													=							'''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


snake_case_						:			Dict													=							'''
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 						lowerCamelCase(   a__):

				def remove_articles(a__):
								_SCREAMING_SNAKE_CASE			  =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__):
								_SCREAMING_SNAKE_CASE			  =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 						lowerCamelCase(   a__	,a__):
				return int(normalize_answer(a__) == normalize_answer(a__))







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =[any(compute_exact(a__	,a__) for ref in refs) for pred, refs in zip(a__	,a__)]
				return (sum(a__) / len(a__)) * 100







def 						lowerCamelCase(   a__	,a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =[rgram for rgrams in rgramslist for rgram in rgrams]
				_SCREAMING_SNAKE_CASE			  =Counter(a__)

				_SCREAMING_SNAKE_CASE			  =Counter(a__)
				_SCREAMING_SNAKE_CASE			  =Counter()
				for sgram, scount in sgramcounter.items():
								_SCREAMING_SNAKE_CASE			  =scount * numref

				_SCREAMING_SNAKE_CASE			  =Counter(a__)
				_SCREAMING_SNAKE_CASE			  =Counter()
				for cgram, ccount in cgramcounter.items():
								_SCREAMING_SNAKE_CASE			  =ccount * numref

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

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

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

				# ADDITION
				_SCREAMING_SNAKE_CASE			  =set(a__) - set(a__)
				_SCREAMING_SNAKE_CASE			  =set(a__) & set(a__)
				_SCREAMING_SNAKE_CASE			  =set(a__) - set(a__)

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

				return (keepscore, delscore_precision, addscore)







def 						lowerCamelCase(   a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =len(a__)

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

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

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







def 						lowerCamelCase(   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:
								_SCREAMING_SNAKE_CASE			  =sentence.lower()

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

				if not return_str:
								_SCREAMING_SNAKE_CASE			  =normalized_sent.split()

				return normalized_sent







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







def 						lowerCamelCase(   a__	,a__	,a__="exp"	,a__=None	,a__=False	,a__=False	,a__=False	,):
				_SCREAMING_SNAKE_CASE			  =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''')
				_SCREAMING_SNAKE_CASE			  =[[refs[i] for refs in references] for i in range(a__)]
				_SCREAMING_SNAKE_CASE			  =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 							__UpperCamelCase				(       self					:      str  )							->     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 							__UpperCamelCase				(       self					:      Any				,    _a					:      Dict				,    _a					:      str				,    _a					:      str  )							->     List[Any]:


										"""simple docstring"""





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



from typing import TYPE_CHECKING

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


snake_case_						:			List[Any]													=							{
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			str													=							[
							    '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GraphormerForGraphClassification''',
							    '''GraphormerModel''',
							    '''GraphormerPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							snake_case_						:			Union[str, Any]													=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)