
'''simple docstring'''



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						(							lowercase__		):

						def remove_articles(lowercase__		):
												lowercase__							       =				re.compile(r"""\b(a|an|the)\b"""						,						re.UNICODE		)
												return re.sub(SCREAMING_SNAKE_CASE_						,						""" """						,						SCREAMING_SNAKE_CASE_		)

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

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

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

						return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_		)		)		)		)





def        _A						(							lowercase__						,						lowercase__		):
						return int(normalize_answer(SCREAMING_SNAKE_CASE_		) == normalize_answer(SCREAMING_SNAKE_CASE_		)		)





def        _A						(							lowercase__						,						lowercase__		):
						lowercase__							       =				[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        _A						(							lowercase__						,						lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__							       =				[rgram for rgrams in rgramslist for rgram in rgrams]
						lowercase__							       =				Counter(SCREAMING_SNAKE_CASE_		)

						lowercase__							       =				Counter(SCREAMING_SNAKE_CASE_		)
						lowercase__							       =				Counter()
						for sgram, scount in sgramcounter.items():
												lowercase__							       =				scount * numref

						lowercase__							       =				Counter(SCREAMING_SNAKE_CASE_		)
						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(SCREAMING_SNAKE_CASE_		) > 0:
												lowercase__							       =				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)
												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(SCREAMING_SNAKE_CASE_		) > 0:
												lowercase__							       =				deltmpscorea / len(SCREAMING_SNAKE_CASE_		)

						# ADDITION
						lowercase__							       =				set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)
						lowercase__							       =				set(SCREAMING_SNAKE_CASE_		) & set(SCREAMING_SNAKE_CASE_		)
						lowercase__							       =				set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)

						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(SCREAMING_SNAKE_CASE_		) > 0:
												lowercase__							       =				addtmpscore / len(SCREAMING_SNAKE_CASE_		)
						if len(SCREAMING_SNAKE_CASE_		) > 0:
												lowercase__							       =				addtmpscore / len(SCREAMING_SNAKE_CASE_		)
						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        _A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__							       =				len(SCREAMING_SNAKE_CASE_		)

						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(SCREAMING_SNAKE_CASE_		)
												for i in range(0						,						len(SCREAMING_SNAKE_CASE_		) - 1		):
																		if i < len(SCREAMING_SNAKE_CASE_		) - 1:
																								lowercase__							       =				ragrams[i] + " " + ragrams[i + 1]
																								ragrams.append(SCREAMING_SNAKE_CASE_		)
																		if i < len(SCREAMING_SNAKE_CASE_		) - 2:
																								lowercase__							       =				ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
																								ragrams.append(SCREAMING_SNAKE_CASE_		)
																		if i < len(SCREAMING_SNAKE_CASE_		) - 3:
																								lowercase__							       =				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:
																		lowercase__							       =				sagrams[i] + " " + sagrams[i + 1]
																		sagrams.append(SCREAMING_SNAKE_CASE_		)
												if i < len(SCREAMING_SNAKE_CASE_		) - 2:
																		lowercase__							       =				sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
																		sagrams.append(SCREAMING_SNAKE_CASE_		)
												if i < len(SCREAMING_SNAKE_CASE_		) - 3:
																		lowercase__							       =				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:
																		lowercase__							       =				cagrams[i] + " " + cagrams[i + 1]
																		cagrams.append(SCREAMING_SNAKE_CASE_		)
												if i < len(SCREAMING_SNAKE_CASE_		) - 2:
																		lowercase__							       =				cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
																		cagrams.append(SCREAMING_SNAKE_CASE_		)
												if i < len(SCREAMING_SNAKE_CASE_		) - 3:
																		lowercase__							       =				cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
																		cagrams.append(SCREAMING_SNAKE_CASE_		)

						(lowercase__)							       =				SARIngram(SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_		)
						(lowercase__)							       =				SARIngram(SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_		)
						(lowercase__)							       =				SARIngram(SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_		)
						(lowercase__)							       =				SARIngram(SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_		)
						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        _A						(							lowercase__						,						lowercase__ = True						,						lowercase__ = "13a"						,						lowercase__ = True		):

						if lowercase:
												lowercase__							       =				sentence.lower()

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

						if not return_str:
												lowercase__							       =				normalized_sent.split()

						return normalized_sent





def        _A						(							lowercase__						,						lowercase__						,						lowercase__		):

						if not (len(SCREAMING_SNAKE_CASE_		) == len(SCREAMING_SNAKE_CASE_		) == len(SCREAMING_SNAKE_CASE_		)):
												raise ValueError("""Sources length must match predictions and references lengths."""		)
						lowercase__							       =				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]		)
						lowercase__							       =				sari_score / len(SCREAMING_SNAKE_CASE_		)
						return 100 * sari_score





def        _A						(							lowercase__						,						lowercase__						,						lowercase__="exp"						,						lowercase__=None						,						lowercase__=False						,						lowercase__=False						,						lowercase__=False						,						):
						lowercase__							       =				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"""		)
						lowercase__							       =				[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE_		)]
						lowercase__							       =				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 	A							(				datasets.Metric ):



			def A__       (    self				)	->						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 A__       (    self				,   lowerCamelCase__				,   lowerCamelCase__				,   lowerCamelCase__				)	->						Tuple:




									'''simple docstring'''



									lowercase__							       =				{}
									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 json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class 	UpperCAmelCase_					(					UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      List[Any]     		=		BioGptTokenizer
   __A :      Optional[int]     		=		False






   def _snake_case    (		self ):






     """simple docstring"""





     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     lowerCamelCase :					Union[str, Any]    =				[
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "w</w>",
         "r</w>",
         "t</w>",
         "lo",
         "low",
         "er</w>",
         "low</w>",
         "lowest</w>",
         "newer</w>",
         "wider</w>",
         "<unk>",
     ]
     lowerCamelCase :					str    =				dict(zip(__A						,				range(len(__A ) ) ) )
     lowerCamelCase :					Dict    =				["l o 123", "lo w 1456", "e r</w> 1789", ""]

     lowerCamelCase :					Dict    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["vocab_file"] )
     lowerCamelCase :					int    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["merges_file"] )
     with open(self.vocab_file						,				"w" ) as fp:
       fp.write(json.dumps(__A ) )
     with open(self.merges_file						,				"w" ) as fp:
       fp.write("\n".join(__A ) )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				"lower newer"
     lowerCamelCase :					Union[str, Any]    =				"lower newer"
     return input_text, output_text






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer(self.vocab_file						,				self.merges_file )

     lowerCamelCase :					Optional[int]    =				"lower"
     lowerCamelCase :					Any    =				["low", "er</w>"]
     lowerCamelCase :					List[str]    =				tokenizer.tokenize(__A )
     self.assertListEqual(__A						,				__A )

     lowerCamelCase :					Union[str, Any]    =				tokens + ["<unk>"]
     lowerCamelCase :					List[str]    =				[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A )						,				__A )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer.from_pretrained("microsoft/biogpt" )

     lowerCamelCase :					Optional[int]    =				tokenizer.encode("sequence builders"						,				add_special_tokens=__A )
     lowerCamelCase :					Tuple    =				tokenizer.encode("multi-sequence build"						,				add_special_tokens=__A )

     lowerCamelCase :					Tuple    =				tokenizer.build_inputs_with_special_tokens(__A )
     lowerCamelCase :					List[str]    =				tokenizer.build_inputs_with_special_tokens(__A						,				__A )

     self.assertTrue(encoded_sentence == [2] + text )
     self.assertTrue(encoded_pair == [2] + text + [2] + text_a )



