



"""simple docstring"""







import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A    =			'▁'

A    =			get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
@require_tokenizers
class      UpperCAmelCase__		(      UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Dict 							=     BigBirdTokenizer
		lowerCAmelCase_       :	Dict 							=     BigBirdTokenizerFast
		lowerCAmelCase_       :	Union[str, Any] 							=     True
		lowerCAmelCase_       :	List[str] 							=     True



		def 					A_		( self  :    Optional[Any]				)			->						Dict:






									'''simple docstring'''

									super().setUp()

									A         =					self.tokenizer_class(snake_case      ,							keep_accents=snake_case				)
									tokenizer.save_pretrained(self.tmpdirname				)



		def 					A_		( self  :    int				)			->						List[Any]:






									'''simple docstring'''

									A         =					'<s>'
									A         =					1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case				)      ,							snake_case				)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case				)      ,							snake_case				)



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






									'''simple docstring'''

									A         =					list(self.get_tokenizer().get_vocab().keys()				)

									self.assertEqual(vocab_keys[0]      ,							'<unk>'				)
									self.assertEqual(vocab_keys[1]      ,							'<s>'				)
									self.assertEqual(vocab_keys[-1]      ,							'[MASK]'				)
									self.assertEqual(len(snake_case				)      ,							1_004				)



		def 					A_		( self  :    int				)			->						Optional[Any]:






									'''simple docstring'''

									self.assertEqual(self.get_tokenizer().vocab_size      ,							1_000				)



		def 					A_		( self  :    List[Any]				)			->						int:






									'''simple docstring'''

									if not self.test_rust_tokenizer:
																return

									A         =					self.get_tokenizer()
									A         =					self.get_rust_tokenizer()

									A         =					'I was born in 92000, and this is falsé.'

									A         =					tokenizer.tokenize(snake_case				)
									A         =					rust_tokenizer.tokenize(snake_case				)
									self.assertListEqual(snake_case      ,							snake_case				)

									A         =					tokenizer.encode(snake_case      ,							add_special_tokens=snake_case				)
									A         =					rust_tokenizer.encode(snake_case      ,							add_special_tokens=snake_case				)
									self.assertListEqual(snake_case      ,							snake_case				)

									A         =					self.get_rust_tokenizer()
									A         =					tokenizer.encode(snake_case				)
									A         =					rust_tokenizer.encode(snake_case				)
									self.assertListEqual(snake_case      ,							snake_case				)



		def 					A_		( self  :    List[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					BigBirdTokenizer(snake_case      ,							keep_accents=snake_case				)

									A         =					tokenizer.tokenize('This is a test'				)
									self.assertListEqual(snake_case      ,							['▁This', '▁is', '▁a', '▁t', 'est']				)

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(snake_case				)      ,							[285, 46, 10, 170, 382]      ,							)

									A         =					tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
									self.assertListEqual(
									    snake_case      ,							[
									        SPIECE_UNDERLINE + 'I',
									        SPIECE_UNDERLINE + 'was',
									        SPIECE_UNDERLINE + 'b',
									        'or',
									        'n',
									        SPIECE_UNDERLINE + 'in',
									        SPIECE_UNDERLINE + '',
									        '9',
									        '2',
									        '0',
									        '0',
									        '0',
									        ',',
									        SPIECE_UNDERLINE + 'and',
									        SPIECE_UNDERLINE + 'this',
									        SPIECE_UNDERLINE + 'is',
									        SPIECE_UNDERLINE + 'f',
									        'al',
									        's',
									        'é',
									        '.',
									    ]      ,							)
									A         =					tokenizer.convert_tokens_to_ids(snake_case				)
									self.assertListEqual(
									    snake_case      ,							[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,							)

									A         =					tokenizer.convert_ids_to_tokens(snake_case				)
									self.assertListEqual(
									    snake_case      ,							[
									        SPIECE_UNDERLINE + 'I',
									        SPIECE_UNDERLINE + 'was',
									        SPIECE_UNDERLINE + 'b',
									        'or',
									        'n',
									        SPIECE_UNDERLINE + 'in',
									        SPIECE_UNDERLINE + '',
									        '<unk>',
									        '2',
									        '0',
									        '0',
									        '0',
									        ',',
									        SPIECE_UNDERLINE + 'and',
									        SPIECE_UNDERLINE + 'this',
									        SPIECE_UNDERLINE + 'is',
									        SPIECE_UNDERLINE + 'f',
									        'al',
									        's',
									        '<unk>',
									        '.',
									    ]      ,							)



		@cached_property
		def 					A_		( self  :    Union[str, Any]				)			->						List[str]:






									'''simple docstring'''

									return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'				)



		@slow
		def 					A_		( self  :    List[str]				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					'Hello World!'
									A         =					[65, 18_536, 2_260, 101, 66]

									self.assertListEqual(snake_case      ,							self.big_tokenizer.encode(snake_case				)				)



		@slow
		def 					A_		( self  :    List[Any]				)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					(
									    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
									    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
									)
									# fmt: off
									A         =					[65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
									# fmt: on
									self.assertListEqual(snake_case      ,							self.big_tokenizer.encode(snake_case				)				)



		@require_torch
		@slow
		def 					A_		( self  :    int				)			->						Any:






									'''simple docstring'''

									import torch

									from transformers import BigBirdConfig, BigBirdModel

									# Build sequence
									A         =					list(self.big_tokenizer.get_vocab().keys()				)[:10]
									A         =					' '.join(snake_case				)
									A         =					self.big_tokenizer.encode_plus(snake_case      ,							return_tensors='pt'      ,							return_token_type_ids=snake_case				)
									A         =					self.big_tokenizer.batch_encode_plus(
									    [sequence + ' ' + sequence]      ,							return_tensors='pt'      ,							return_token_type_ids=snake_case				)

									A         =					BigBirdConfig(attention_type='original_full'				)
									A         =					BigBirdModel(snake_case				)

									assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

									with torch.no_grad():
																model(**snake_case				)
																model(**snake_case				)



		@slow
		def 					A_		( self  :    Optional[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'				)
									A         =					tokenizer.decode(tokenizer('Paris is the [MASK].'				).input_ids				)

									self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]'				)



		@slow
		def 					A_		( self  :    Dict				)			->						List[Any]:






									'''simple docstring'''

									A         =					{'input_ids': [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=snake_case      ,							model_name='google/bigbird-roberta-base'      ,							revision='215c99f1600e06f83acce68422f2035b2b5c3510'      ,							)








"""simple docstring"""







import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


A    =			logging.get_logger(__name__)

A    =			{'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}

A    =			{
    'vocab_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt',
    },
    'emoji_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json',
    },
}

A    =			{
    'abeja/gpt-neox-japanese-2.7b': 2_0_4_8,
}

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							with open(lowerCamelCase__				,      'r'				,      encoding='utf-8'			) as f:
														A         =					json.loads(f.read()			)

							A         =					collections.OrderedDict()
							A         =					collections.OrderedDict()
							A         =					collections.OrderedDict()
							with open(lowerCamelCase__				,      'r'				,      encoding='utf-8'			) as f:
														A         =					f.readlines()
							A         =					[[t.rstrip('\n'			)] if (t == ',' or ',' not in t) else t.rstrip('\n'			).split(','			) for t in token]
							for idx, b in enumerate(lowerCamelCase__			):
														A         =					b
														A         =					idx
														for wd in b:
																					A         =					idx

							return vocab, raw_vocab, ids_to_tokens, emoji






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Optional[int] 							=     VOCAB_FILES_NAMES
		lowerCAmelCase_       :	Any 							=     PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase_       :	Optional[Any] 							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase_       :	str 							=     ["""input_ids""", """attention_mask"""]



		def __init__( self  :    Optional[Any]      ,							snake_case  :    Tuple      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Optional[Any]="<|endoftext|>"      ,							snake_case  :    List[str]="<|endoftext|>"      ,							snake_case  :    Any="<|startoftext|>"      ,							snake_case  :    Any="<|endoftext|>"      ,							snake_case  :    Tuple=False      ,							**snake_case  :    List[str]      ,							)			->						Optional[Any]:






									'''simple docstring'''

									super().__init__(
									    unk_token=snake_case      ,							pad_token=snake_case      ,							bos_token=snake_case      ,							eos_token=snake_case      ,							do_clean_text=snake_case      ,							**snake_case      ,							)
									if not os.path.isfile(snake_case				):
																raise ValueError(
																    f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
																    ' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'				)
									if not os.path.isfile(snake_case				):
																raise ValueError(
																    f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
																    ' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'				)
									A         =					do_clean_text
									A      ,  A      ,  A      ,  A         =					load_vocab_and_emoji(snake_case      ,							snake_case				)
									A         =					SubWordJapaneseTokenizer(
									    vocab=self.vocab      ,							ids_to_tokens=self.ids_to_tokens      ,							emoji=self.emoji				)



		@property
		def 					A_		( self  :    Any				)			->						List[str]:






									'''simple docstring'''

									return len(self.raw_vocab				)



		def 					A_		( self  :    str				)			->						str:






									'''simple docstring'''

									return dict(self.raw_vocab      ,							**self.added_tokens_encoder				)



		def 					A_		( self  :    List[str]      ,							snake_case  :    Tuple				)			->						Optional[Any]:






									'''simple docstring'''

									return self.subword_tokenizer.tokenize(snake_case      ,							clean=self.do_clean_text				)



		def 					A_		( self  :    int      ,							snake_case  :    Optional[int]				)			->						Dict:






									'''simple docstring'''

									return self.vocab.get(snake_case      ,							self.vocab.get(self.unk_token				)				)



		def 					A_		( self  :    int      ,							snake_case  :    List[Any]				)			->						Tuple:






									'''simple docstring'''

									return self.subword_tokenizer.convert_id_to_token(snake_case				)



		def 					A_		( self  :    str      ,							snake_case  :    int				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					''.join(snake_case				).strip()
									return out_string



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    "Conversation"				)			->						List[int]:






									'''simple docstring'''

									A         =					[]
									for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(snake_case      ,							add_special_tokens=snake_case				) + [self.eos_token_id]				)

									if len(snake_case				) > self.model_max_length:
																A         =					input_ids[-self.model_max_length :]
									return input_ids



		def 					A_		( self  :    int      ,							snake_case  :    str      ,							snake_case  :    Optional[str] = None				)			->						Tuple[str]:






									'''simple docstring'''

									A         =					0
									if os.path.isdir(snake_case				):
																A         =					os.path.join(
																    snake_case      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
																A         =					os.path.join(
																    snake_case      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']				)
									else:
																A         =					(
																    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
																)
																A         =					(
																    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
																)
									with open(snake_case      ,							'w'      ,							encoding='utf-8'				) as writer:
																for token_index, token in self.ids_to_tokens.items():
																							if index != token_index:
																														logger.warning(
																														    f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																														    ' Please check that the vocabulary is not corrupted!'				)
																														A         =					token_index
																							writer.write(','.join(snake_case				) + '\n'				)
																							index += 1
									with open(snake_case      ,							'w'      ,							encoding='utf-8'				) as writer:
																json.dump(self.emoji      ,							snake_case				)
									return vocab_file, emoji_file






class      UpperCAmelCase__		(      UpperCamelCase     ):



		def __init__( self  :    str      ,							snake_case  :    Dict      ,							snake_case  :    Optional[Any]      ,							snake_case  :    List[Any]				)			->						int:






									'''simple docstring'''

									A         =					vocab  # same as swe
									A         =					ids_to_tokens  # same as bpe
									A         =					emoji
									A         =					np.max([len(snake_case				) for w in self.vocab.keys()]				)
									A         =					re.compile(r'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'				)
									A         =					re.compile(r'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'				)
									A         =					re.compile(r'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'				)
									A         =					re.compile(
									    r'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'				)
									A         =					re.compile(
									    r'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'				)
									A         =					re.compile(
									    r'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'				)
									A         =					'─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
									A         =					'▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
									A         =					str.maketrans({k: '<BLOCK>' for k in keisen + blocks}				)



		def __len__( self  :    List[str]				)			->						List[str]:






									'''simple docstring'''

									return len(self.ids_to_tokens				)



		def 					A_		( self  :    Tuple      ,							snake_case  :    Any				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.content_repattera.sub('<URL>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<EMAIL>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<TEL>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<DATE>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<DATE>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<PRICE>'      ,							snake_case				)
									A         =					content.translate(self.content_transa				)
									while "<BLOCK><BLOCK>" in content:
																A         =					content.replace('<BLOCK><BLOCK>'      ,							'<BLOCK>'				)
									return content



		def 					A_		( self  :    Any      ,							snake_case  :    int      ,							snake_case  :    Any=False				)			->						Any:






									'''simple docstring'''

									A         =					text.replace(' '      ,							'<SP>'				)
									A         =					text.replace('　'      ,							'<SP>'				)
									A         =					text.replace('\r\n'      ,							'<BR>'				)
									A         =					text.replace('\n'      ,							'<BR>'				)
									A         =					text.replace('\r'      ,							'<BR>'				)
									A         =					text.replace('\t'      ,							'<TAB>'				)
									A         =					text.replace('—'      ,							'ー'				)
									A         =					text.replace('−'      ,							'ー'				)
									for k, v in self.emoji["emoji"].items():
																if k in text:
																							A         =					text.replace(snake_case      ,							snake_case				)
									if clean:
																A         =					self.clean_text(snake_case				)

									def check_simbol(snake_case  :    Union[str, Any]				):
																A         =					x.encode()
																if len(snake_case				) == 1 and len(snake_case				) == 2:
																							A         =					(int(e[0]				) << 8) + int(e[1]				)
																							if (
																							    (c >= 0xc2a1 and c <= 0xc2bf)
																							    or (c >= 0xc780 and c <= 0xc783)
																							    or (c >= 0xcab9 and c <= 0xcbbf)
																							    or (c >= 0xcc80 and c <= 0xcda2)
																							):
																														return True
																return False

									def checkuae(snake_case  :    List[Any]				):
																A         =					x.encode()
																if len(snake_case				) == 1 and len(snake_case				) == 3:
																							A         =					(int(e[0]				) << 16) + (int(e[1]				) << 8) + int(e[2]				)
																							if c >= 0xe2_8080 and c <= 0xe2_b07f:
																														return True
																return False

									A         =					0
									A         =					[]
									while pos < len(snake_case				):
																A         =					min(len(snake_case				)      ,							pos + self.maxlen + 1				) if text[pos] == '<' else pos + 3
																A         =					[]  # (token_id, token, pos)
																for e in range(snake_case      ,							snake_case      ,							-1				):
																							A         =					text[pos:e]
																							if wd in self.vocab:
																														if wd[0] == "<" and len(snake_case				) > 2:
																																					A         =					[(self.vocab[wd], wd, e)]
																																					break
																														else:
																																					candidates.append((self.vocab[wd], wd, e)				)
																if len(snake_case				) > 0:
																							# the smallest token_id is adopted
																							A      ,  A      ,  A         =					sorted(snake_case      ,							key=lambda snake_case				: x[0]				)[0]
																							result.append(snake_case				)
																							A         =					e
																else:
																							A         =					pos + 1
																							A         =					text[pos:end]
																							if check_simbol(snake_case				):
																														result.append('<KIGOU>'				)
																							elif checkuae(snake_case				):
																														result.append('<U2000U2BFF>'				)
																							else:
																														for i in wd.encode('utf-8'				):
																																					result.append('<|byte%d|>' % i				)
																							A         =					end
									return result



		def 					A_		( self  :    List[str]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Optional[Any]="\n"				)			->						List[Any]:






									'''simple docstring'''

									A         =					[]
									A         =					[]
									A         =					self.ids_to_tokens[index][0]
									if word[:6] == "<|byte" and word[-2:] == "|>":
																byte_tokens.append(int(word[6:-2]				)				)
									else:
																if len(snake_case				) > 0:
																							words.append(bytearray(snake_case				).decode('utf-8'      ,							errors='replace'				)				)
																							A         =					[]
																if word[:7] == "<|emoji" and word[-2:] == "|>":
																							words.append(self.emoji['emoji_inv'][word]				)
																elif word == "<SP>":
																							words.append(' '				)
																elif word == "<BR>":
																							words.append(snake_case				)
																elif word == "<TAB>":
																							words.append('\t'				)
																elif word == "<BLOCK>":
																							words.append('▀'				)
																elif word == "<KIGOU>":
																							words.append('ǀ'				)
																elif word == "<U2000U2BFF>":
																							words.append('‖'				)
																else:
																							words.append(snake_case				)
									if len(snake_case				) > 0:
																words.append(bytearray(snake_case				).decode('utf-8'      ,							errors='replace'				)				)
									A         =					''.join(snake_case				)
									return text



