


'''simple docstring'''



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	:						int       =	'\\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	:						int       =	'\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							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):

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

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

					def remove_punc(_SCREAMING_SNAKE_CASE      :   int   ):
										__a			:       Optional[int]					    =  set(string.punctuation   )
										return "".join(ch for ch in text if ch not in exclude   )

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

					return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE   )   )   )   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Tuple   ):
					return int(normalize_answer(_SCREAMING_SNAKE_CASE   ) == normalize_answer(_SCREAMING_SNAKE_CASE   )   )






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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  [rgram for rgrams in rgramslist for rgram in rgrams]
					__a			:       Optional[int]					    =  Counter(_SCREAMING_SNAKE_CASE   )

					__a			:       Union[str, Any]					    =  Counter(_SCREAMING_SNAKE_CASE   )
					__a			:       Union[str, Any]					    =  Counter()
					for sgram, scount in sgramcounter.items():
										__a			:       int					    =  scount * numref

					__a			:       Optional[int]					    =  Counter(_SCREAMING_SNAKE_CASE   )
					__a			:       List[str]					    =  Counter()
					for cgram, ccount in cgramcounter.items():
										__a			:       Union[str, Any]					    =  ccount * numref

					# KEEP
					__a			:       Any					    =  sgramcounter_rep & cgramcounter_rep
					__a			:       Tuple					    =  keepgramcounter_rep & rgramcounter
					__a			:       Any					    =  sgramcounter_rep & rgramcounter

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

					# DELETION
					__a			:       List[Any]					    =  sgramcounter_rep - cgramcounter_rep
					__a			:       Optional[int]					    =  delgramcounter_rep - rgramcounter
					__a			:       Union[str, Any]					    =  sgramcounter_rep - rgramcounter
					__a			:       int					    =  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			:       List[str]					    =  1
					if len(_SCREAMING_SNAKE_CASE   ) > 0:
										__a			:       Union[str, Any]					    =  deltmpscorea / len(_SCREAMING_SNAKE_CASE   )

					# ADDITION
					__a			:       str					    =  set(_SCREAMING_SNAKE_CASE   ) - set(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  set(_SCREAMING_SNAKE_CASE   ) & set(_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  set(_SCREAMING_SNAKE_CASE   ) - set(_SCREAMING_SNAKE_CASE   )

					__a			:       Optional[Any]					    =  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			:       Tuple					    =  1
					__a			:       Optional[Any]					    =  1
					if len(_SCREAMING_SNAKE_CASE   ) > 0:
										__a			:       str					    =  addtmpscore / len(_SCREAMING_SNAKE_CASE   )
					if len(_SCREAMING_SNAKE_CASE   ) > 0:
										__a			:       List[Any]					    =  addtmpscore / len(_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  0
					if addscore_precision > 0 or addscore_recall > 0:
										__a			:       Tuple					    =  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

					return (keepscore, delscore_precision, addscore)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       Dict					    =  len(_SCREAMING_SNAKE_CASE   )

					__a			:       int					    =  ssent.split(' '   )
					__a			:       List[str]					    =  csent.split(' '   )
					__a			:       Tuple					    =  []
					__a			:       Dict					    =  []
					__a			:       Dict					    =  []
					__a			:       Union[str, Any]					    =  []
					__a			:       Any					    =  []
					__a			:       Optional[int]					    =  []

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

					for i in range(0						,      len(_SCREAMING_SNAKE_CASE   ) - 1   ):
										if i < len(_SCREAMING_SNAKE_CASE   ) - 1:
															__a			:       Optional[int]					    =  sagrams[i] + ' ' + sagrams[i + 1]
															sagrams.append(_SCREAMING_SNAKE_CASE   )
										if i < len(_SCREAMING_SNAKE_CASE   ) - 2:
															__a			:       Any					    =  sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
															sagrams.append(_SCREAMING_SNAKE_CASE   )
										if i < len(_SCREAMING_SNAKE_CASE   ) - 3:
															__a			:       int					    =  sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
															sagrams.append(_SCREAMING_SNAKE_CASE   )

					for i in range(0						,      len(_SCREAMING_SNAKE_CASE   ) - 1   ):
										if i < len(_SCREAMING_SNAKE_CASE   ) - 1:
															__a			:       str					    =  cagrams[i] + ' ' + cagrams[i + 1]
															cagrams.append(_SCREAMING_SNAKE_CASE   )
										if i < len(_SCREAMING_SNAKE_CASE   ) - 2:
															__a			:       List[str]					    =  cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
															cagrams.append(_SCREAMING_SNAKE_CASE   )
										if i < len(_SCREAMING_SNAKE_CASE   ) - 3:
															__a			:       Optional[Any]					    =  cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
															cagrams.append(_SCREAMING_SNAKE_CASE   )

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   bool = True						,      _SCREAMING_SNAKE_CASE      :   str = "13a"						,      _SCREAMING_SNAKE_CASE      :   bool = 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:
										__a			:       Any					    =  sentence.lower()

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

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

					return normalized_sent






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






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





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




	def       __UpperCAmelCase							(   self							):

						'''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						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  {}
						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







'''simple docstring'''



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class 							__UpperCamelCase				(		unittest.TestCase					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  inspect.getfile(accelerate.test_utils							)
						__a			:       List[str]					    =  os.path.sep.join(
						    mod_file.split(os.path.sep							)[:-1] + ['scripts', 'external_deps', 'test_metrics.py']							)

						from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

						__a			:       Union[str, Any]					    =  test_metrics




	@require_cpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						debug_launcher(self.test_metrics.main						,				num_processes=1							)




	@require_cpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						debug_launcher(self.test_metrics.main							)




	@require_single_gpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.test_metrics.main()





	@require_multi_gpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						print(f"""Found {torch.cuda.device_count()} devices."""							)
						__a			:       List[Any]					    =  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
						with patch_environment(omp_num_threads=1							):
											execute_subprocess_async(__a						,				env=os.environ.copy()							)



