



'''simple docstring'''

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCAmelCase						      =       '''
@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",
}
'''

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


__lowerCAmelCase						      =       '''
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					(				lowerCAmelCase_  )      ->				Any:

		def remove_articles(lowerCAmelCase_  ):
				_a					:				Union[str, Any]				=							re.compile(r'\b(a|an|the)\b'						,      re.UNICODE  )
				return re.sub(lowerCAmelCase_						,      ' '						,      lowerCAmelCase_  )

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

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

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

		return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_  )  )  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				List[Any]:
		return int(normalize_answer(lowerCAmelCase_  ) == normalize_answer(lowerCAmelCase_  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Dict:
		_a					:				List[Any]				=							[any(compute_exact(lowerCAmelCase_						,      lowerCAmelCase_  ) for ref in refs  ) for pred, refs in zip(lowerCAmelCase_						,      lowerCAmelCase_  )]
		return (sum(lowerCAmelCase_  ) / len(lowerCAmelCase_  )) * 100



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				List[Any]:
		_a					:				Optional[Any]				=							[rgram for rgrams in rgramslist for rgram in rgrams]
		_a					:				Union[str, Any]				=							Counter(lowerCAmelCase_  )

		_a					:				int				=							Counter(lowerCAmelCase_  )
		_a					:				Dict				=							Counter()
		for sgram, scount in sgramcounter.items():
				_a					:				str				=							scount * numref

		_a					:				List[Any]				=							Counter(lowerCAmelCase_  )
		_a					:				Dict				=							Counter()
		for cgram, ccount in cgramcounter.items():
				_a					:				Dict				=							ccount * numref

		# KEEP
		_a					:				List[Any]				=							sgramcounter_rep & cgramcounter_rep
		_a					:				Union[str, Any]				=							keepgramcounter_rep & rgramcounter
		_a					:				List[str]				=							sgramcounter_rep & rgramcounter

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

		# DELETION
		_a					:				Any				=							sgramcounter_rep - cgramcounter_rep
		_a					:				Optional[int]				=							delgramcounter_rep - rgramcounter
		_a					:				int				=							sgramcounter_rep - rgramcounter
		_a					:				Union[str, Any]				=							0
		_a					:				Tuple				=							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.
		_a					:				Optional[Any]				=							1
		if len(lowerCAmelCase_  ) > 0:
				_a					:				Tuple				=							deltmpscorea / len(lowerCAmelCase_  )

		# ADDITION
		_a					:				List[Any]				=							set(lowerCAmelCase_  ) - set(lowerCAmelCase_  )
		_a					:				List[str]				=							set(lowerCAmelCase_  ) & set(lowerCAmelCase_  )
		_a					:				Optional[int]				=							set(lowerCAmelCase_  ) - set(lowerCAmelCase_  )

		_a					:				Optional[int]				=							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.
		_a					:				Optional[int]				=							1
		_a					:				List[str]				=							1
		if len(lowerCAmelCase_  ) > 0:
				_a					:				Dict				=							addtmpscore / len(lowerCAmelCase_  )
		if len(lowerCAmelCase_  ) > 0:
				_a					:				int				=							addtmpscore / len(lowerCAmelCase_  )
		_a					:				Optional[int]				=							0
		if addscore_precision > 0 or addscore_recall > 0:
				_a					:				int				=							2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

		return (keepscore, delscore_precision, addscore)



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Any:
		_a					:				int				=							len(lowerCAmelCase_  )

		_a					:				List[str]				=							ssent.split(' '  )
		_a					:				Any				=							csent.split(' '  )
		_a					:				Optional[int]				=							[]
		_a					:				Dict				=							[]
		_a					:				List[str]				=							[]
		_a					:				Dict				=							[]
		_a					:				Optional[int]				=							[]
		_a					:				int				=							[]

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

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

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

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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_ = True						,      lowerCAmelCase_ = "13a"						,      lowerCAmelCase_ = True  )      ->				int:
		# 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:
				_a					:				Dict				=							sentence.lower()

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

		if not return_str:
				_a					:				str				=							normalized_sent.split()

		return normalized_sent



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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_="exp"						,      lowerCAmelCase_=None						,      lowerCAmelCase_=False						,      lowerCAmelCase_=False						,      lowerCAmelCase_=False						,      )      ->				List[Any]:
		_a					:				List[str]				=							len(references[0]  )
		if any(len(lowerCAmelCase_  ) != references_per_prediction for refs in references  ):
				raise ValueError('Sacrebleu requires the same number of references for each prediction'  )
		_a					:				List[Any]				=							[[refs[i] for refs in references] for i in range(lowerCAmelCase_  )]
		_a					:				Optional[Any]				=							sacrebleu.corpus_bleu(
		    lowerCAmelCase_						,      lowerCAmelCase_						,      smooth_method=lowerCAmelCase_						,      smooth_value=lowerCAmelCase_						,      force=lowerCAmelCase_						,      lowercase=lowerCAmelCase_						,      use_effective_order=lowerCAmelCase_						,      )
		return output.score

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







							def 							__lowercase      (							self :	str		):
									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 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int		):
									_a					:				Optional[Any]				=							{}
									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









'''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						      =       {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Optional[int]           = 'distilbert'
							lowerCAmelCase	:   Optional[int]           = {
							    'hidden_size': 'dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							}







							def __init__(							self :	str     ,_UpperCAmelCase :	Optional[Any]=30522     ,_UpperCAmelCase :	int=512     ,_UpperCAmelCase :	int=False     ,_UpperCAmelCase :	str=6     ,_UpperCAmelCase :	int=12     ,_UpperCAmelCase :	Union[str, Any]=768     ,_UpperCAmelCase :	Optional[Any]=4 * 768     ,_UpperCAmelCase :	int=0.1     ,_UpperCAmelCase :	Union[str, Any]=0.1     ,_UpperCAmelCase :	Tuple="gelu"     ,_UpperCAmelCase :	Any=0.02     ,_UpperCAmelCase :	List[Any]=0.1     ,_UpperCAmelCase :	Dict=0.2     ,_UpperCAmelCase :	Union[str, Any]=0     ,**_UpperCAmelCase :	List[Any]     ,):
									_a					:				Optional[Any]				=							vocab_size
									_a					:				Dict				=							max_position_embeddings
									_a					:				Optional[int]				=							sinusoidal_pos_embds
									_a					:				List[Any]				=							n_layers
									_a					:				Any				=							n_heads
									_a					:				int				=							dim
									_a					:				Optional[Any]				=							hidden_dim
									_a					:				str				=							dropout
									_a					:				Optional[Any]				=							attention_dropout
									_a					:				Any				=							activation
									_a					:				Tuple				=							initializer_range
									_a					:				Union[str, Any]				=							qa_dropout
									_a					:				List[Any]				=							seq_classif_dropout
									super().__init__(**_UpperCAmelCase     ,pad_token_id=_UpperCAmelCase		)

class      __magic_name__						(   _UpperCamelCase       ):







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




