



import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__A   			="\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   			="\\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   			="\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        a						(      _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





		def remove_articles(_UpperCAmelCase							:					Any						):
				__UpperCAmelCase    :     Dict													=	re.compile(R'''\b(a|an|the)\b''' ,   re.UNICODE						)
				return re.sub(_UpperCAmelCase ,   ''' ''' ,   _UpperCAmelCase						)

		def white_space_fix(_UpperCAmelCase							:					Tuple						):
				return " ".join(text.split()						)

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

		def lower(_UpperCAmelCase							:					Tuple						):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase						)						)						)						)

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		return int(normalize_answer(_UpperCAmelCase						) == normalize_answer(_UpperCAmelCase						)						)

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Tuple													=	[any(compute_exact(_UpperCAmelCase ,   _UpperCAmelCase						) for ref in refs						) for pred, refs in zip(_UpperCAmelCase ,   _UpperCAmelCase						)]
		return (sum(_UpperCAmelCase						) / len(_UpperCAmelCase						)) * 1_00

def        a						(      _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Tuple						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	[rgram for rgrams in rgramslist for rgram in rgrams]
		__UpperCAmelCase    :     List[str]													=	Counter(_UpperCAmelCase						)

		__UpperCAmelCase    :     Any													=	Counter(_UpperCAmelCase						)
		__UpperCAmelCase    :     Union[str, Any]													=	Counter()
		for sgram, scount in sgramcounter.items():
				__UpperCAmelCase    :     Dict													=	scount * numref

		__UpperCAmelCase    :     str													=	Counter(_UpperCAmelCase						)
		__UpperCAmelCase    :     Dict													=	Counter()
		for cgram, ccount in cgramcounter.items():
				__UpperCAmelCase    :     Union[str, Any]													=	ccount * numref

		# KEEP
		__UpperCAmelCase    :     Tuple													=	sgramcounter_rep & cgramcounter_rep
		__UpperCAmelCase    :     Dict													=	keepgramcounter_rep & rgramcounter
		__UpperCAmelCase    :     Any													=	sgramcounter_rep & rgramcounter

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

		# DELETION
		__UpperCAmelCase    :     Optional[Any]													=	sgramcounter_rep - cgramcounter_rep
		__UpperCAmelCase    :     List[Any]													=	delgramcounter_rep - rgramcounter
		__UpperCAmelCase    :     List[Any]													=	sgramcounter_rep - rgramcounter
		__UpperCAmelCase    :     Dict													=	0
		__UpperCAmelCase    :     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.
		__UpperCAmelCase    :     List[Any]													=	1
		if len(_UpperCAmelCase						) > 0:
				__UpperCAmelCase    :     Union[str, Any]													=	deltmpscorea / len(_UpperCAmelCase						)

		# ADDITION
		__UpperCAmelCase    :     Dict													=	set(_UpperCAmelCase						) - set(_UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[int]													=	set(_UpperCAmelCase						) & set(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[str]													=	set(_UpperCAmelCase						) - set(_UpperCAmelCase						)

		__UpperCAmelCase    :     Tuple													=	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.
		__UpperCAmelCase    :     Optional[Any]													=	1
		__UpperCAmelCase    :     List[str]													=	1
		if len(_UpperCAmelCase						) > 0:
				__UpperCAmelCase    :     Dict													=	addtmpscore / len(_UpperCAmelCase						)
		if len(_UpperCAmelCase						) > 0:
				__UpperCAmelCase    :     Union[str, Any]													=	addtmpscore / len(_UpperCAmelCase						)
		__UpperCAmelCase    :     Union[str, Any]													=	0
		if addscore_precision > 0 or addscore_recall > 0:
				__UpperCAmelCase    :     Optional[int]													=	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

		return (keepscore, delscore_precision, addscore)

def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	len(_UpperCAmelCase						)

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

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

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

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

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

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					bool = True ,   _UpperCAmelCase							:					str = "13a" ,   _UpperCAmelCase							:					bool = True						):







		'''simple docstring'''





		if lowercase:
				__UpperCAmelCase    :     List[str]													=	sentence.lower()

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

		if not return_str:
				__UpperCAmelCase    :     List[str]													=	normalized_sent.split()

		return normalized_sent

def        a						(      _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					Optional[Any]						):







		'''simple docstring'''





		if not (len(_UpperCAmelCase						) == len(_UpperCAmelCase						) == len(_UpperCAmelCase						)):
				raise ValueError('''Sources length must match predictions and references lengths.'''						)
		__UpperCAmelCase    :     Optional[Any]													=	0
		for src, pred, refs in zip(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						):
				sari_score += SARIsent(normalize(_UpperCAmelCase						) ,   normalize(_UpperCAmelCase						) ,   [normalize(_UpperCAmelCase						) for sent in refs]						)
		__UpperCAmelCase    :     Union[str, Any]													=	sari_score / len(_UpperCAmelCase						)
		return 1_00 * sari_score

def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Tuple="exp" ,   _UpperCAmelCase							:					List[str]=None ,   _UpperCAmelCase							:					List[str]=False ,   _UpperCAmelCase							:					Any=False ,   _UpperCAmelCase							:					Optional[int]=False ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Tuple													=	len(references[0]						)
		if any(len(_UpperCAmelCase						) != references_per_prediction for refs in references						):
				raise ValueError('''Sacrebleu requires the same number of references for each prediction'''						)
		__UpperCAmelCase    :     Dict													=	[[refs[i] for refs in references] for i in range(_UpperCAmelCase						)]
		__UpperCAmelCase    :     Optional[int]													=	sacrebleu.corpus_bleu(
		    _UpperCAmelCase ,   _UpperCAmelCase ,   smooth_method=_UpperCAmelCase ,   smooth_value=_UpperCAmelCase ,   force=_UpperCAmelCase ,   lowercase=_UpperCAmelCase ,   use_effective_order=_UpperCAmelCase ,   )
		return output.score

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




			'''simple docstring'''






			def  snake_case__     (   self							: int ):
					'''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  snake_case__     (   self							: Tuple       ,       a_							: Any       ,       a_							: Dict       ,       a_							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	{}
					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 ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
				from .pipeline_kandinsky import KandinskyPipeline
				from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
				from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
				from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
				from .text_encoder import MultilingualCLIP





