





import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_SCREAMING_SNAKE_CASE   =      '''
@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",
}
'''

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


_SCREAMING_SNAKE_CASE   =      '''
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_     :					List[str]			):



			def remove_articles(lowerCamelCase_     :					int			):
						__lowercase			  =							re.compile(r'''\b(a|an|the)\b'''						,    re.UNICODE			)
						return re.sub(A__						,    ''' '''						,    A__			)

			def white_space_fix(lowerCamelCase_     :					List[str]			):
						return " ".join(text.split()			)

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

			def lower(lowerCamelCase_     :					Union[str, Any]			):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(A__			)			)			)			)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					List[str]			):
			return int(normalize_answer(A__			) == normalize_answer(A__			)			)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					Dict			):
			__lowercase			  =							[any(compute_exact(A__						,    A__			) for ref in refs			) for pred, refs in zip(A__						,    A__			)]
			return (sum(A__			) / len(A__			)) * 1_0_0




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Union[str, Any]						,    lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					int			):
			__lowercase			  =							[rgram for rgrams in rgramslist for rgram in rgrams]
			__lowercase			  =							Counter(A__			)

			__lowercase			  =							Counter(A__			)
			__lowercase			  =							Counter()
			for sgram, scount in sgramcounter.items():
						__lowercase			  =							scount * numref

			__lowercase			  =							Counter(A__			)
			__lowercase			  =							Counter()
			for cgram, ccount in cgramcounter.items():
						__lowercase			  =							ccount * numref

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

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

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

			# ADDITION
			__lowercase			  =							set(A__			) - set(A__			)
			__lowercase			  =							set(A__			) & set(A__			)
			__lowercase			  =							set(A__			) - set(A__			)

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

			return (keepscore, delscore_precision, addscore)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Any						,    lowerCamelCase_     :					Optional[Any]			):
			__lowercase			  =							len(A__			)

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

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

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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					Dict = True						,    lowerCamelCase_     :					Tuple = "13a"						,    lowerCamelCase_     :					List[str] = True			):



			if lowercase:
						__lowercase			  =							sentence.lower()

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

			if not return_str:
						__lowercase			  =							normalized_sent.split()

			return normalized_sent




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					Union[str, Any]						,    lowerCamelCase_     :					Optional[int]			):



			if not (len(A__			) == len(A__			) == len(A__			)):
						raise ValueError('''Sources length must match predictions and references lengths.'''			)
			__lowercase			  =							0
			for src, pred, refs in zip(A__						,    A__						,    A__			):
						sari_score += SARIsent(normalize(A__			)						,    normalize(A__			)						,    [normalize(A__			) for sent in refs]			)
			__lowercase			  =							sari_score / len(A__			)
			return 1_0_0 * sari_score




def 				_lowerCAmelCase  (			lowerCamelCase_     :					int						,    lowerCamelCase_     :					Tuple						,    lowerCamelCase_     :					Union[str, Any]="exp"						,    lowerCamelCase_     :					List[Any]=None						,    lowerCamelCase_     :					Dict=False						,    lowerCamelCase_     :					List[Any]=False						,    lowerCamelCase_     :					Optional[int]=False						,    ):
			__lowercase			  =							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'''			)
			__lowercase			  =							[[refs[i] for refs in references] for i in range(A__			)]
			__lowercase			  =							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  ):







	'''simple docstring'''

	def   _UpperCAmelCase       (self	)							->							Optional[Any]:







				'''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       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							Union[str, Any]:







				'''simple docstring'''





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












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''BlenderbotTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlenderbotForCausalLM''',
	    '''BlenderbotForConditionalGeneration''',
	    '''BlenderbotModel''',
	    '''BlenderbotPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''TFBlenderbotForConditionalGeneration''',
	    '''TFBlenderbotModel''',
	    '''TFBlenderbotPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''FlaxBlenderbotForConditionalGeneration''',
	    '''FlaxBlenderbotModel''',
	    '''FlaxBlenderbotPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

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





