


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_lowercase       :      int	=	"\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"

_lowercase       :      str	=	"\\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"


_lowercase       :      Optional[Any]	=	"\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   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]						) ->   Tuple:





	"""simple docstring"""







	def remove_articles(UpperCamelCase__:     List[Any]						):
		A								=				re.compile(r"""\b(a|an|the)\b"""							,					re.UNICODE						)
		return re.sub(UpperCamelCase__							,					""" """							,					UpperCamelCase__						)

	def white_space_fix(UpperCamelCase__:     Optional[int]						):
		return " ".join(text.split()						)

	def remove_punc(UpperCamelCase__:     Tuple						):
		A								=				set(string.punctuation						)
		return "".join(ch for ch in text if ch not in exclude						)

	def lower(UpperCamelCase__:     Optional[Any]						):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__						)						)						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     Optional[int]						) ->   str:





	"""simple docstring"""







	return int(normalize_answer(UpperCamelCase__						) == normalize_answer(UpperCamelCase__						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     str						) ->   Dict:





	"""simple docstring"""







	A								=				[any(compute_exact(UpperCamelCase__							,					UpperCamelCase__						) for ref in refs						) for pred, refs in zip(UpperCamelCase__							,					UpperCamelCase__						)]
	return (sum(UpperCamelCase__						) / len(UpperCamelCase__						)) * 1_00





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     Any							,					UpperCamelCase__:     Union[str, Any]						) ->   Optional[Any]:





	"""simple docstring"""







	A								=				[rgram for rgrams in rgramslist for rgram in rgrams]
	A								=				Counter(UpperCamelCase__						)

	A								=				Counter(UpperCamelCase__						)
	A								=				Counter()
	for sgram, scount in sgramcounter.items():
		A								=				scount * numref

	A								=				Counter(UpperCamelCase__						)
	A								=				Counter()
	for cgram, ccount in cgramcounter.items():
		A								=				ccount * numref

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

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

	# DELETION
	A								=				sgramcounter_rep - cgramcounter_rep
	A								=				delgramcounter_rep - rgramcounter
	A								=				sgramcounter_rep - rgramcounter
	A								=				0
	A								=				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								=				1
	if len(UpperCamelCase__						) > 0:
		A								=				deltmpscorea / len(UpperCamelCase__						)

	# ADDITION
	A								=				set(UpperCamelCase__						) - set(UpperCamelCase__						)
	A								=				set(UpperCamelCase__						) & set(UpperCamelCase__						)
	A								=				set(UpperCamelCase__						) - set(UpperCamelCase__						)

	A								=				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								=				1
	A								=				1
	if len(UpperCamelCase__						) > 0:
		A								=				addtmpscore / len(UpperCamelCase__						)
	if len(UpperCamelCase__						) > 0:
		A								=				addtmpscore / len(UpperCamelCase__						)
	A								=				0
	if addscore_precision > 0 or addscore_recall > 0:
		A								=				2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

	return (keepscore, delscore_precision, addscore)





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     str						) ->   Union[str, Any]:





	"""simple docstring"""







	A								=				len(UpperCamelCase__						)

	A								=				ssent.split(""" """						)
	A								=				csent.split(""" """						)
	A								=				[]
	A								=				[]
	A								=				[]
	A								=				[]
	A								=				[]
	A								=				[]

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

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





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     bool = True							,					UpperCamelCase__:     str = "13a"							,					UpperCamelCase__:     bool = True						) ->   str:





	"""simple docstring"""







	if lowercase:
		A								=				sentence.lower()

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

	if not return_str:
		A								=				normalized_sent.split()

	return normalized_sent





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     str						) ->   Any:





	"""simple docstring"""







	if not (len(UpperCamelCase__						) == len(UpperCamelCase__						) == len(UpperCamelCase__						)):
		raise ValueError("""Sources length must match predictions and references lengths."""						)
	A								=				0
	for src, pred, refs in zip(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						):
		sari_score += SARIsent(normalize(UpperCamelCase__						)							,					normalize(UpperCamelCase__						)							,					[normalize(UpperCamelCase__						) for sent in refs]						)
	A								=				sari_score / len(UpperCamelCase__						)
	return 1_00 * sari_score





def   _lowerCAmelCase				(   UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Any="exp"							,					UpperCamelCase__:     str=None							,					UpperCamelCase__:     List[Any]=False							,					UpperCamelCase__:     Optional[Any]=False							,					UpperCamelCase__:     int=False							,					) ->   Optional[Any]:





	"""simple docstring"""







	A								=				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"""						)
	A								=				[[refs[i] for refs in references] for i in range(UpperCamelCase__						)]
	A								=				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      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					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				,   a__				,   a__				,   a__   )	->				int:
					A								=				{}
					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







import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   Tuple:





	"""simple docstring"""







	def wrapper(*UpperCamelCase__:     Union[str, Any]							,					**UpperCamelCase__:     List[str]						):
		A								=				timeit.default_timer()
		A								=				func(*UpperCamelCase__							,					**UpperCamelCase__						)
		A								=				timeit.default_timer() - starttime
		return delta

	A								=				func.__name__

	return wrapper





def   _lowerCAmelCase				(   UpperCamelCase__:     dict							,					UpperCamelCase__:     List[str]=1_00							,					UpperCamelCase__:     int=None						) ->   Any:





	"""simple docstring"""







	A								=				[]
	A								=				seq_shapes or {}
	for i in range(UpperCamelCase__						):
		A								=				{}
		for col_id, (k, v) in enumerate(features.items()						):
			if isinstance(UpperCamelCase__							,					_ArrayXD						):
				A								=				np.random.rand(*v.shape						).astype(v.dtype						)
			elif isinstance(UpperCamelCase__							,					datasets.Value						):
				if v.dtype == "string":
					A								=				"""The small grey turtle was surprisingly fast when challenged."""
				else:
					A								=				np.random.randint(10							,					size=1						).astype(v.dtype						).item()
			elif isinstance(UpperCamelCase__							,					datasets.Sequence						):
				while isinstance(UpperCamelCase__							,					datasets.Sequence						):
					A								=				v.feature
				A								=				seq_shapes[k]
				A								=				np.random.rand(*UpperCamelCase__						).astype(v.dtype						)
			A								=				data

		dummy_data.append((i, example)						)

	return dummy_data





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     List[str]=1_00							,					UpperCamelCase__:     str=None						) ->   Optional[int]:





	"""simple docstring"""







	A								=				generate_examples(UpperCamelCase__							,					num_examples=UpperCamelCase__							,					seq_shapes=UpperCamelCase__						)

	with ArrowWriter(features=UpperCamelCase__							,					path=UpperCamelCase__						) as writer:
		for key, record in dummy_data:
			A								=				features.encode_example(UpperCamelCase__						)
			writer.write(UpperCamelCase__						)

		A					,			A								=				writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'						)

	A								=				datasets.Dataset.from_file(filename=UpperCamelCase__							,					info=datasets.DatasetInfo(features=UpperCamelCase__						)						)

	return dataset



