





'''simple docstring'''







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case_     :       Optional[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"

snake_case_     :       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"


snake_case_     :       str						=  "\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__      :			Any   ) ->	Tuple:

						def remove_articles(SCREAMING_SNAKE_CASE__      :			Optional[int]   ):
												UpperCAmelCase_			:      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__      :			str   ):
												return " ".join(text.split()   )

						def remove_punc(SCREAMING_SNAKE_CASE__      :			Union[str, Any]   ):
												UpperCAmelCase_			:      int     						=  set(string.punctuation   )
												return "".join(ch for ch in text if ch not in exclude   )

						def lower(SCREAMING_SNAKE_CASE__      :			List[str]   ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__   )   )   )   )





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Union[str, Any]   ) ->	Optional[int]:
						return int(normalize_answer(SCREAMING_SNAKE_CASE__   ) == normalize_answer(SCREAMING_SNAKE_CASE__   )   )





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			List[Any]   ) ->	int:
						UpperCAmelCase_			:      Tuple     						=  [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__      :			Dict,			SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			List[str]   ) ->	Optional[Any]:
						UpperCAmelCase_			:      Union[str, Any]     						=  [rgram for rgrams in rgramslist for rgram in rgrams]
						UpperCAmelCase_			:      Any     						=  Counter(SCREAMING_SNAKE_CASE__   )

						UpperCAmelCase_			:      Optional[Any]     						=  Counter(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Optional[Any]     						=  Counter()
						for sgram, scount in sgramcounter.items():
												UpperCAmelCase_			:      Optional[Any]     						=  scount * numref

						UpperCAmelCase_			:      List[str]     						=  Counter(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Union[str, Any]     						=  Counter()
						for cgram, ccount in cgramcounter.items():
												UpperCAmelCase_			:      Any     						=  ccount * numref

						# KEEP
						UpperCAmelCase_			:      List[str]     						=  sgramcounter_rep & cgramcounter_rep
						UpperCAmelCase_			:      Tuple     						=  keepgramcounter_rep & rgramcounter
						UpperCAmelCase_			:      Any     						=  sgramcounter_rep & rgramcounter

						UpperCAmelCase_			:      Any     						=  0
						UpperCAmelCase_			:      Tuple     						=  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[int]     						=  1
						if len(SCREAMING_SNAKE_CASE__   ) > 0:
												UpperCAmelCase_			:      Optional[Any]     						=  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)
												UpperCAmelCase_			:      List[Any]     						=  keeptmpscorea / sum(keepgramcounterall_rep.values()   )
						UpperCAmelCase_			:      List[Any]     						=  0
						if keepscore_precision > 0 or keepscore_recall > 0:
												UpperCAmelCase_			:      List[Any]     						=  2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

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

						# ADDITION
						UpperCAmelCase_			:      str     						=  set(SCREAMING_SNAKE_CASE__   ) - set(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Any     						=  set(SCREAMING_SNAKE_CASE__   ) & set(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Optional[int]     						=  set(SCREAMING_SNAKE_CASE__   ) - set(SCREAMING_SNAKE_CASE__   )

						UpperCAmelCase_			:      int     						=  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_			:      Dict     						=  1
						UpperCAmelCase_			:      Any     						=  1
						if len(SCREAMING_SNAKE_CASE__   ) > 0:
												UpperCAmelCase_			:      str     						=  addtmpscore / len(SCREAMING_SNAKE_CASE__   )
						if len(SCREAMING_SNAKE_CASE__   ) > 0:
												UpperCAmelCase_			:      Optional[Any]     						=  addtmpscore / len(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      int     						=  0
						if addscore_precision > 0 or addscore_recall > 0:
												UpperCAmelCase_			:      Any     						=  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			Any   ) ->	Dict:
						UpperCAmelCase_			:      Optional[int]     						=  len(SCREAMING_SNAKE_CASE__   )

						UpperCAmelCase_			:      Tuple     						=  ssent.split(''' '''   )
						UpperCAmelCase_			:      Tuple     						=  csent.split(''' '''   )
						UpperCAmelCase_			:      List[Any]     						=  []
						UpperCAmelCase_			:      Tuple     						=  []
						UpperCAmelCase_			:      List[Any]     						=  []
						UpperCAmelCase_			:      Dict     						=  []
						UpperCAmelCase_			:      List[str]     						=  []
						UpperCAmelCase_			:      Optional[Any]     						=  []

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

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





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			bool = True,			SCREAMING_SNAKE_CASE__      :			str = "13a",			SCREAMING_SNAKE_CASE__      :			bool = True   ) ->	Optional[Any]:
						# 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:
												UpperCAmelCase_			:      Dict     						=  sentence.lower()

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

						if not return_str:
												UpperCAmelCase_			:      Any     						=  normalized_sent.split()

						return normalized_sent





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	Dict:
						if not (len(SCREAMING_SNAKE_CASE__   ) == len(SCREAMING_SNAKE_CASE__   ) == len(SCREAMING_SNAKE_CASE__   )):
												raise ValueError('''Sources length must match predictions and references lengths.'''   )
						UpperCAmelCase_			:      Union[str, Any]     						=  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]   )
						UpperCAmelCase_			:      Optional[int]     						=  sari_score / len(SCREAMING_SNAKE_CASE__   )
						return 100 * sari_score





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[Any],			SCREAMING_SNAKE_CASE__      :			Union[str, Any],			SCREAMING_SNAKE_CASE__      :			Optional[int]="exp",			SCREAMING_SNAKE_CASE__      :			List[str]=None,			SCREAMING_SNAKE_CASE__      :			Optional[int]=False,			SCREAMING_SNAKE_CASE__      :			List[str]=False,			SCREAMING_SNAKE_CASE__      :			Optional[int]=False,			) ->	str:
						UpperCAmelCase_			:      Any     						=  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'''   )
						UpperCAmelCase_			:      int     						=  [[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE__   )]
						UpperCAmelCase_			:      int     						=  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     UpperCAmelCase__	(							self							:     Optional[int]   )					->       str:







													"""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							:     Tuple       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     int   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  {}
													result.update({'''sari''': compute_sari(sources=__magic_name__       ,     predictions=__magic_name__       ,     references=__magic_name__   )}   )
													result.update({'''sacrebleu''': compute_sacrebleu(predictions=__magic_name__       ,     references=__magic_name__   )}   )
													result.update({'''exact''': compute_em(predictions=__magic_name__       ,     references=__magic_name__   )}   )
													return result












'''simple docstring'''







import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   List[str]									=       BlenderbotSmallTokenizer
							__a					:   List[Any]									=       False

							def     UpperCAmelCase__	(							self							:     str   )					->       str:







													"""simple docstring"""
													super().setUp()

													UpperCAmelCase_			:      Tuple     						=  ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
													UpperCAmelCase_			:      Optional[Any]     						=  dict(zip(__magic_name__       ,     range(len(__magic_name__   )   )   )   )

													UpperCAmelCase_			:      int     						=  ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
													UpperCAmelCase_			:      Optional[Any]     						=  {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

													UpperCAmelCase_			:      Tuple     						=  os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['''vocab_file''']   )
													UpperCAmelCase_			:      Dict     						=  os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['''merges_file''']   )
													with open(self.vocab_file       ,     '''w'''       ,     encoding='''utf-8'''   ) as fp:
																			fp.write(json.dumps(__magic_name__   ) + '''\n'''   )
													with open(self.merges_file       ,     '''w'''       ,     encoding='''utf-8'''   ) as fp:
																			fp.write('''\n'''.join(__magic_name__   )   )

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     **__magic_name__							:     Dict   )					->       Tuple:







													"""simple docstring"""
													kwargs.update(self.special_tokens_map   )
													return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,     **__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     List[str]   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  '''adapt act apte'''
													UpperCAmelCase_			:      Tuple     						=  '''adapt act apte'''
													return input_text, output_text

							def     UpperCAmelCase__	(							self							:     str   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  BlenderbotSmallTokenizer(self.vocab_file       ,     self.merges_file       ,     **self.special_tokens_map   )
													UpperCAmelCase_			:      List[Any]     						=  '''adapt act apte'''
													UpperCAmelCase_			:      Dict     						=  ['''adapt''', '''act''', '''ap@@''', '''te''']
													UpperCAmelCase_			:      Dict     						=  tokenizer.tokenize(__magic_name__   )
													self.assertListEqual(__magic_name__       ,     __magic_name__   )

													UpperCAmelCase_			:      Tuple     						=  [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

													UpperCAmelCase_			:      Dict     						=  [0, 1, 2, 3, 4, 5]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__   )       ,     __magic_name__   )

							def     UpperCAmelCase__	(							self							:     int   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
													assert tok('''sam'''   ).input_ids == [13_84]
													UpperCAmelCase_			:      Optional[int]     						=  '''I am a small frog.'''
													UpperCAmelCase_			:      List[str]     						=  tok([src_text]       ,     padding=__magic_name__       ,     truncation=__magic_name__   )['''input_ids''']
													UpperCAmelCase_			:      Dict     						=  tok.batch_decode(__magic_name__       ,     skip_special_tokens=__magic_name__       ,     clean_up_tokenization_spaces=__magic_name__   )[0]
													assert src_text != decoded  # I wish it did!
													assert decoded == "i am a small frog ."



							def     UpperCAmelCase__	(							self							:     Any   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
													UpperCAmelCase_			:      List[Any]     						=  '''I am a small frog .'''
													UpperCAmelCase_			:      Any     						=  '''.'''
													UpperCAmelCase_			:      List[Any]     						=  tok(__magic_name__   )['''input_ids''']
													UpperCAmelCase_			:      Optional[int]     						=  tok(__magic_name__   )['''input_ids''']

													assert encoded[-1] == encoded_dot[0]





