






"""simple docstring"""


import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


__SCREAMING_SNAKE_CASE      =      '\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

__SCREAMING_SNAKE_CASE      =      '\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

__SCREAMING_SNAKE_CASE      =      '\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - \'id\': id of the question-answer pair (see above),\n            - \'answers\': a list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,    _KWARGS_DESCRIPTION	)
class 	__UpperCamelCase    (    datasets.Metric	):
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					str:
											return datasets.MetricInfo(
											    description=_DESCRIPTION    ,   citation=_CITATION    ,   inputs_description=_KWARGS_DESCRIPTION    ,   features=datasets.Features(
											        {
											            'predictions': {
											                'id': datasets.Value('string'      ),
											                'prediction_text': datasets.Value('string'      ),
											                'no_answer_probability': datasets.Value('float32'      ),
											            },
											            'references': {
											                'id': datasets.Value('string'      ),
											                'answers': datasets.features.Sequence(
											                    {'text': datasets.Value('string'      ), 'answer_start': datasets.Value('int32'      )}      ),
											            },
											        }      )    ,   codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/']    ,   reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/']    ,   )
					def 					UpperCAmelCase__   (       self       :  Optional[Any]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  List[Any]    ,   UpperCAmelCase       :  List[str]=1.0      )  ->					Union[str, Any]:
											lowerCAmelCase      :Any				  = {p['id']: p['no_answer_probability'] for p in predictions}
											lowerCAmelCase      :Union[str, Any]				  = [{'paragraphs': [{'qas': references}]}]
											lowerCAmelCase      :str				  = {p['id']: p['prediction_text'] for p in predictions}

											lowerCAmelCase      :Dict				  = make_qid_to_has_ans(UpperCAmelCase      )  # maps qid to True/False
											lowerCAmelCase      :int				  = [k for k, v in qid_to_has_ans.items() if v]
											lowerCAmelCase      :Tuple				  = [k for k, v in qid_to_has_ans.items() if not v]

											lowerCAmelCase  ,   lowerCAmelCase      :int				  = get_raw_scores(UpperCAmelCase    ,   UpperCAmelCase      )
											lowerCAmelCase      :Any				  = apply_no_ans_threshold(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )
											lowerCAmelCase      :Optional[Any]				  = apply_no_ans_threshold(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )
											lowerCAmelCase      :str				  = make_eval_dict(UpperCAmelCase    ,   UpperCAmelCase      )

											if has_ans_qids:
																	lowerCAmelCase      :List[str]				  = make_eval_dict(UpperCAmelCase    ,   UpperCAmelCase    ,   qid_list=UpperCAmelCase      )
																	merge_eval(UpperCAmelCase    ,   UpperCAmelCase    ,   'HasAns'      )
											if no_ans_qids:
																	lowerCAmelCase      :List[str]				  = make_eval_dict(UpperCAmelCase    ,   UpperCAmelCase    ,   qid_list=UpperCAmelCase      )
																	merge_eval(UpperCAmelCase    ,   UpperCAmelCase    ,   'NoAns'      )
											find_all_best_thresh(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )
											return dict(UpperCAmelCase      )







"""simple docstring"""


import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE      =      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      =      {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

__SCREAMING_SNAKE_CASE      =      {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

__SCREAMING_SNAKE_CASE      =      {'stas/tiny-wmt19-en-de': 10_24}
__SCREAMING_SNAKE_CASE      =      {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 10_24,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = set()
						lowerCAmelCase      :Optional[Any]				  = word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)   )
												lowerCAmelCase      :int				  = char
						return pairs


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[str]				  = text.replace('，'    ,			','   )
						lowerCAmelCase      :str				  = re.sub(R'。\s*'    ,			'. '    ,			a__   )
						lowerCAmelCase      :int				  = text.replace('、'    ,			','   )
						lowerCAmelCase      :str				  = text.replace('”'    ,			'"'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('“'    ,			'"'   )
						lowerCAmelCase      :List[Any]				  = text.replace('∶'    ,			':'   )
						lowerCAmelCase      :List[Any]				  = text.replace('：'    ,			':'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('？'    ,			'?'   )
						lowerCAmelCase      :Tuple				  = text.replace('《'    ,			'"'   )
						lowerCAmelCase      :Dict				  = text.replace('》'    ,			'"'   )
						lowerCAmelCase      :List[str]				  = text.replace('）'    ,			')'   )
						lowerCAmelCase      :Dict				  = text.replace('！'    ,			'!'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('（'    ,			'('   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('；'    ,			';'   )
						lowerCAmelCase      :Any				  = text.replace('１'    ,			'1'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('」'    ,			'"'   )
						lowerCAmelCase      :Any				  = text.replace('「'    ,			'"'   )
						lowerCAmelCase      :str				  = text.replace('０'    ,			'0'   )
						lowerCAmelCase      :Dict				  = text.replace('３'    ,			'3'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('２'    ,			'2'   )
						lowerCAmelCase      :str				  = text.replace('５'    ,			'5'   )
						lowerCAmelCase      :Tuple				  = text.replace('６'    ,			'6'   )
						lowerCAmelCase      :Optional[int]				  = text.replace('９'    ,			'9'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('７'    ,			'7'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('８'    ,			'8'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('４'    ,			'4'   )
						lowerCAmelCase      :int				  = re.sub(R'．\s*'    ,			'. '    ,			a__   )
						lowerCAmelCase      :List[Any]				  = text.replace('～'    ,			'~'   )
						lowerCAmelCase      :int				  = text.replace('’'    ,			'\''   )
						lowerCAmelCase      :Dict				  = text.replace('…'    ,			'...'   )
						lowerCAmelCase      :Any				  = text.replace('━'    ,			'-'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('〈'    ,			'<'   )
						lowerCAmelCase      :Any				  = text.replace('〉'    ,			'>'   )
						lowerCAmelCase      :str				  = text.replace('【'    ,			'['   )
						lowerCAmelCase      :Any				  = text.replace('】'    ,			']'   )
						lowerCAmelCase      :int				  = text.replace('％'    ,			'%'   )
						return text


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = []
						for char in text:
												lowerCAmelCase      :Optional[Any]				  = unicodedata.category(a__   )
												if cat.startswith('C'   ):
																		continue
												output.append(a__   )
						return "".join(a__   )







class 	__UpperCamelCase    (    UpperCamelCase	):
					lowercase_						:							Dict			=     VOCAB_FILES_NAMES
					lowercase_						:							Any			=     PRETRAINED_VOCAB_FILES_MAP
					lowercase_						:							Dict			=     PRETRAINED_INIT_CONFIGURATION
					lowercase_						:							Optional[Any]			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_						:							Union[str, Any]			=     ["""input_ids""", """attention_mask"""]
					def __init__(       self       :  int    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  str=False    ,   UpperCAmelCase       :  Optional[int]="<unk>"    ,   UpperCAmelCase       :  List[Any]="<s>"    ,   UpperCAmelCase       :  List[str]="</s>"    ,   UpperCAmelCase       :  Optional[int]="<pad>"    ,   **UpperCAmelCase       :  int    ,   )  ->					int:
											super().__init__(
											    langs=UpperCAmelCase    ,   src_vocab_file=UpperCAmelCase    ,   tgt_vocab_file=UpperCAmelCase    ,   merges_file=UpperCAmelCase    ,   do_lower_case=UpperCAmelCase    ,   unk_token=UpperCAmelCase    ,   bos_token=UpperCAmelCase    ,   sep_token=UpperCAmelCase    ,   pad_token=UpperCAmelCase    ,   **UpperCAmelCase    ,   )

											try:
																	import sacremoses
											except ImportError:
																	raise ImportError(
																	    'You need to install sacremoses to use XLMTokenizer. '
																	    'See https://pypi.org/project/sacremoses/ for installation.'      )

											lowerCAmelCase      :str				  = sacremoses

											lowerCAmelCase      :str				  = src_vocab_file
											lowerCAmelCase      :List[str]				  = tgt_vocab_file
											lowerCAmelCase      :str				  = merges_file
											lowerCAmelCase      :Optional[Any]				  = do_lower_case

											# cache of sm.MosesPunctNormalizer instance
											lowerCAmelCase      :str				  = {}
											# cache of sm.MosesTokenizer instance
											lowerCAmelCase      :Any				  = {}
											lowerCAmelCase      :List[str]				  = {}

											if langs and len(UpperCAmelCase      ) == 2:
																	lowerCAmelCase  ,   lowerCAmelCase      :List[Any]				  = langs
											else:
																	raise ValueError(
																	    f"""arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. """
																	    'Usually that means that tokenizer can\'t find a mapping for the given model path '
																	    'in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer.'      )

											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as src_vocab_handle:
																	lowerCAmelCase      :Tuple				  = json.load(UpperCAmelCase      )
											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as tgt_vocab_handle:
																	lowerCAmelCase      :str				  = json.load(UpperCAmelCase      )
																	lowerCAmelCase      :int				  = {v: k for k, v in tgt_vocab.items()}
											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as merges_handle:
																	lowerCAmelCase      :Optional[int]				  = merges_handle.read().split('\n'      )[:-1]
											lowerCAmelCase      :List[Any]				  = [tuple(merge.split()[:2]      ) for merge in merges]
											lowerCAmelCase      :int				  = dict(zip(UpperCAmelCase    ,   range(len(UpperCAmelCase      )      )      )      )
											lowerCAmelCase      :str				  = {}
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					Dict[str, int]:
											return self.get_src_vocab()
					@property
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					int:
											return self.src_vocab_size
					def 					UpperCAmelCase__   (       self       :  int    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Tuple      )  ->					List[Any]:
											if lang not in self.cache_moses_punct_normalizer:
																	lowerCAmelCase      :str				  = self.sm.MosesPunctNormalizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :List[str]				  = punct_normalizer
											return self.cache_moses_punct_normalizer[lang].normalize(UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Tuple    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Optional[int]      )  ->					str:
											if lang not in self.cache_moses_tokenizer:
																	lowerCAmelCase      :Optional[int]				  = self.sm.MosesTokenizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :Any				  = moses_tokenizer
											return self.cache_moses_tokenizer[lang].tokenize(
											    UpperCAmelCase    ,   aggressive_dash_splits=UpperCAmelCase    ,   return_str=UpperCAmelCase    ,   escape=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[Any]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  str      )  ->					List[Any]:
											if lang not in self.cache_moses_detokenizer:
																	lowerCAmelCase      :Any				  = self.sm.MosesDetokenizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :str				  = moses_detokenizer
											return self.cache_moses_detokenizer[lang].detokenize(UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  List[Any]      )  ->					int:
											lowerCAmelCase      :Dict				  = replace_unicode_punct(UpperCAmelCase      )
											lowerCAmelCase      :Union[str, Any]				  = self.moses_punct_norm(UpperCAmelCase    ,   UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = remove_non_printing_char(UpperCAmelCase      )
											return text
					@property
					def 					UpperCAmelCase__   (       self       :  str      )  ->					Union[str, Any]:
											return len(self.encoder      )
					@property
					def 					UpperCAmelCase__   (       self       :  int      )  ->					Optional[int]:
											return len(self.decoder      )
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					Tuple:
											return dict(self.encoder    ,   **self.added_tokens_encoder      )
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					str:
											return dict(self.decoder    ,   **self.added_tokens_decoder      )
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  List[str]      )  ->					Tuple:
											lowerCAmelCase      :List[str]				  = tuple(token[:-1]      ) + (token[-1] + '</w>',)
											if token in self.cache:
																	return self.cache[token]
											lowerCAmelCase      :Dict				  = get_pairs(UpperCAmelCase      )

											if not pairs:
																	return token + "</w>"

											while True:
																	lowerCAmelCase      :Optional[Any]				  = min(UpperCAmelCase    ,   key=lambda UpperCAmelCase      : self.bpe_ranks.get(UpperCAmelCase    ,   float('inf'      )      )      )
																	if bigram not in self.bpe_ranks:
																							break
																	lowerCAmelCase  ,   lowerCAmelCase      :Tuple				  = bigram
																	lowerCAmelCase      :int				  = []
																	lowerCAmelCase      :Union[str, Any]				  = 0
																	while i < len(UpperCAmelCase      ):
																							try:
																													lowerCAmelCase      :List[Any]				  = word.index(UpperCAmelCase    ,   UpperCAmelCase      )
																							except ValueError:
																													new_word.extend(word[i:]      )
																													break
																							else:
																													new_word.extend(word[i:j]      )
																													lowerCAmelCase      :Any				  = j

																							if word[i] == first and i < len(UpperCAmelCase      ) - 1 and word[i + 1] == second:
																													new_word.append(first + second      )
																													i += 2
																							else:
																													new_word.append(word[i]      )
																													i += 1
																	lowerCAmelCase      :Optional[Any]				  = tuple(UpperCAmelCase      )
																	lowerCAmelCase      :Dict				  = new_word
																	if len(UpperCAmelCase      ) == 1:
																							break
																	else:
																							lowerCAmelCase      :Any				  = get_pairs(UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = ' '.join(UpperCAmelCase      )
											if word == "\n  </w>":
																	lowerCAmelCase      :List[Any]				  = '\n</w>'
											lowerCAmelCase      :Optional[Any]				  = word
											return word
					def 					UpperCAmelCase__   (       self       :  List[Any]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  List[str]="en"    ,   UpperCAmelCase       :  int=False      )  ->					List[Any]:
											lowerCAmelCase      :Optional[Any]				  = self.src_lang

											if self.do_lower_case:
																	lowerCAmelCase      :Tuple				  = text.lower()

											if bypass_tokenizer:
																	lowerCAmelCase      :List[str]				  = text.split()
											else:
																	lowerCAmelCase      :Tuple				  = self.moses_pipeline(UpperCAmelCase    ,   lang=UpperCAmelCase      )
																	lowerCAmelCase      :Optional[int]				  = self.moses_tokenize(UpperCAmelCase    ,   lang=UpperCAmelCase      )

											lowerCAmelCase      :int				  = []
											for token in text:
																	if token:
																							split_tokens.extend(list(self.bpe(UpperCAmelCase      ).split(' '      )      )      )

											return split_tokens
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  Any      )  ->					List[Any]:
											return self.encoder.get(UpperCAmelCase    ,   self.encoder.get(self.unk_token      )      )
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]    ,   UpperCAmelCase       :  Any      )  ->					Dict:
											return self.decoder.get(UpperCAmelCase    ,   self.unk_token      )
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  Optional[Any]      )  ->					int:
											lowerCAmelCase      :Optional[int]				  = [t.replace(' '    ,   ''      ).replace('</w>'    ,   ' '      ) for t in tokens]
											lowerCAmelCase      :Optional[int]				  = ''.join(UpperCAmelCase      ).split()
											# detokenize
											lowerCAmelCase      :Dict				  = self.moses_detokenize(UpperCAmelCase    ,   self.tgt_lang      )
											return text
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None      )  ->					List[int]:
											lowerCAmelCase      :Union[str, Any]				  = [self.sep_token_id]

											# no bos used in fairseq
											if token_ids_a is None:
																	return token_ids_a + sep
											return token_ids_a + sep + token_ids_a + sep
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None    ,   UpperCAmelCase       :  bool = False      )  ->					List[int]:

											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=UpperCAmelCase    ,   token_ids_a=UpperCAmelCase    ,   already_has_special_tokens=UpperCAmelCase      )
											# no bos used in fairseq
											if token_ids_a is not None:
																	return ([0] * len(UpperCAmelCase      )) + [1] + ([0] * len(UpperCAmelCase      )) + [1]
											return ([0] * len(UpperCAmelCase      )) + [1]
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None      )  ->					List[int]:
											lowerCAmelCase      :Optional[Any]				  = [self.sep_token_id]

											# no bos used in fairseq
											if token_ids_a is None:
																	return len(token_ids_a + sep      ) * [0]
											return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Optional[str] = None      )  ->					Tuple[str]:
											if not os.path.isdir(UpperCAmelCase      ):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
																	return

											lowerCAmelCase      :Any				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['src_vocab_file']      )
											lowerCAmelCase      :Tuple				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['tgt_vocab_file']      )
											lowerCAmelCase      :Optional[int]				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as f:
																	f.write(json.dumps(self.encoder    ,   indent=2    ,   sort_keys=UpperCAmelCase    ,   ensure_ascii=UpperCAmelCase      ) + '\n'      )

											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as f:
																	lowerCAmelCase      :int				  = {v: k for k, v in self.decoder.items()}
																	f.write(json.dumps(UpperCAmelCase    ,   indent=2    ,   sort_keys=UpperCAmelCase    ,   ensure_ascii=UpperCAmelCase      ) + '\n'      )

											lowerCAmelCase      :Optional[Any]				  = 0
											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as writer:
																	for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,   key=lambda UpperCAmelCase      : kv[1]      ):
																							if index != token_index:
																													logger.warning(
																													    f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
																													    ' Please check that the tokenizer is not corrupted!'      )
																													lowerCAmelCase      :List[Any]				  = token_index
																							writer.write(' '.join(UpperCAmelCase      ) + '\n'      )
																							index += 1

											return src_vocab_file, tgt_vocab_file, merges_file
					def __getstate__(       self       :  Union[str, Any]      )  ->					int:
											lowerCAmelCase      :Any				  = self.__dict__.copy()
											lowerCAmelCase      :Tuple				  = None
											return state
					def __setstate__(       self       :  Any    ,   UpperCAmelCase       :  Union[str, Any]      )  ->					List[Any]:
											lowerCAmelCase      :Tuple				  = d

											try:
																	import sacremoses
											except ImportError:
																	raise ImportError(
																	    'You need to install sacremoses to use XLMTokenizer. '
																	    'See https://pypi.org/project/sacremoses/ for installation.'      )

											lowerCAmelCase      :str				  = sacremoses