



'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_ :	List[Any]         =  get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
@require_tokenizers
class 							lowercase__	(							lowercase						,					unittest.TestCase   ):
	lowercase__  				=			XLMRobertaTokenizer
	lowercase__  				=			XLMRobertaTokenizerFast
	lowercase__  				=			True
	lowercase__  				=			True





	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							super().setUp()

							# We have a SentencePiece fixture for testing
							_UpperCamelCase					:		Tuple 				=   XLMRobertaTokenizer(lowerCamelCase__ ,keep_accents=lowerCamelCase__ )
							tokenizer.save_pretrained(self.tmpdirname )





	def 	UpperCamelCase_  ( self					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   '<pad>'
							_UpperCamelCase					:		str 				=   1

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





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   list(self.get_tokenizer().get_vocab().keys() )

							self.assertEqual(vocab_keys[0] ,'<s>' )
							self.assertEqual(vocab_keys[1] ,'<pad>' )
							self.assertEqual(vocab_keys[-1] ,'<mask>' )
							self.assertEqual(len(lowerCamelCase__ ) ,1002 )





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							self.assertEqual(self.get_tokenizer().vocab_size ,1002 )





	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   XLMRobertaTokenizer(lowerCamelCase__ ,keep_accents=lowerCamelCase__ )

							_UpperCamelCase					:		Dict 				=   tokenizer.tokenize('This is a test' )
							self.assertListEqual(lowerCamelCase__ ,['▁This', '▁is', '▁a', '▁t', 'est'] )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,)

							_UpperCamelCase					:		Union[str, Any] 				=   tokenizer.tokenize('I was born in 92000, and this is falsé.' )
							self.assertListEqual(
							    lowerCamelCase__ ,[
							        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',
							        '.',
							    ] ,)
							_UpperCamelCase					:		int 				=   tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
							self.assertListEqual(
							    lowerCamelCase__ ,[
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
							    ] ,)

							_UpperCamelCase					:		Union[str, Any] 				=   tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
							self.assertListEqual(
							    lowerCamelCase__ ,[
							        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>',
							        '.',
							    ] ,)





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							if not self.test_slow_tokenizer:
													# as we don't have a slow version, we can't compare the outputs between slow and fast versions
													return

							_UpperCamelCase					:		Tuple 				=   (self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
																			_UpperCamelCase					:		List[Any] 				=   self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )
																			_UpperCamelCase					:		Tuple 				=   self.tokenizer_class.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )

																			_UpperCamelCase					:		Optional[int] 				=   tempfile.mkdtemp()

																			_UpperCamelCase					:		str 				=   tokenizer_r.save_pretrained(lowerCamelCase__ )
																			_UpperCamelCase					:		Union[str, Any] 				=   tokenizer_p.save_pretrained(lowerCamelCase__ )

																			# Checks it save with the same files + the tokenizer.json file for the fast one
																			self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
																			_UpperCamelCase					:		str 				=   tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
																			self.assertSequenceEqual(lowerCamelCase__ ,lowerCamelCase__ )

																			# Checks everything loads correctly in the same way
																			_UpperCamelCase					:		int 				=   tokenizer_r.from_pretrained(lowerCamelCase__ )
																			_UpperCamelCase					:		str 				=   tokenizer_p.from_pretrained(lowerCamelCase__ )

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																									self.assertTrue(hasattr(lowerCamelCase__ ,lowerCamelCase__ ) )
																									# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																									# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																			shutil.rmtree(lowerCamelCase__ )

																			# Save tokenizer rust, legacy_format=True
																			_UpperCamelCase					:		str 				=   tempfile.mkdtemp()

																			_UpperCamelCase					:		Any 				=   tokenizer_r.save_pretrained(lowerCamelCase__ ,legacy_format=lowerCamelCase__ )
																			_UpperCamelCase					:		Optional[Any] 				=   tokenizer_p.save_pretrained(lowerCamelCase__ )

																			# Checks it save with the same files
																			self.assertSequenceEqual(lowerCamelCase__ ,lowerCamelCase__ )

																			# Checks everything loads correctly in the same way
																			_UpperCamelCase					:		Tuple 				=   tokenizer_r.from_pretrained(lowerCamelCase__ )
																			_UpperCamelCase					:		str 				=   tokenizer_p.from_pretrained(lowerCamelCase__ )

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																									self.assertTrue(hasattr(lowerCamelCase__ ,lowerCamelCase__ ) )

																			shutil.rmtree(lowerCamelCase__ )

																			# Save tokenizer rust, legacy_format=False
																			_UpperCamelCase					:		Tuple 				=   tempfile.mkdtemp()

																			_UpperCamelCase					:		Dict 				=   tokenizer_r.save_pretrained(lowerCamelCase__ ,legacy_format=lowerCamelCase__ )
																			_UpperCamelCase					:		int 				=   tokenizer_p.save_pretrained(lowerCamelCase__ )

																			# Checks it saved the tokenizer.json file
																			self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )

																			# Checks everything loads correctly in the same way
																			_UpperCamelCase					:		List[Any] 				=   tokenizer_r.from_pretrained(lowerCamelCase__ )
																			_UpperCamelCase					:		Any 				=   tokenizer_p.from_pretrained(lowerCamelCase__ )

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																									self.assertTrue(hasattr(lowerCamelCase__ ,lowerCamelCase__ ) )

																			shutil.rmtree(lowerCamelCase__ )





	@cached_property
	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base' )





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							with tempfile.NamedTemporaryFile() as f:
													shutil.copyfile(lowerCamelCase__ ,f.name )
													_UpperCamelCase					:		Optional[Any] 				=   XLMRobertaTokenizer(f.name ,keep_accents=lowerCamelCase__ )
													_UpperCamelCase					:		str 				=   pickle.dumps(lowerCamelCase__ )
							pickle.loads(lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							if not self.test_rust_tokenizer:
													return

							_UpperCamelCase					:		int 				=   self.get_tokenizer()
							_UpperCamelCase					:		List[str] 				=   self.get_rust_tokenizer()

							_UpperCamelCase					:		Any 				=   'I was born in 92000, and this is falsé.'

							_UpperCamelCase					:		str 				=   tokenizer.tokenize(lowerCamelCase__ )
							_UpperCamelCase					:		Any 				=   rust_tokenizer.tokenize(lowerCamelCase__ )
							self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )

							_UpperCamelCase					:		Union[str, Any] 				=   tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
							_UpperCamelCase					:		Tuple 				=   rust_tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
							self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )

							_UpperCamelCase					:		Tuple 				=   self.get_rust_tokenizer()
							_UpperCamelCase					:		Union[str, Any] 				=   tokenizer.encode(lowerCamelCase__ )
							_UpperCamelCase					:		Optional[int] 				=   rust_tokenizer.encode(lowerCamelCase__ )
							self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )





	@slow
	def 	UpperCamelCase_  ( self					:				Any ):







							'''simple docstring'''
							_UpperCamelCase					:		Union[str, Any] 				=   'Hello World!'
							_UpperCamelCase					:		str 				=   [0, 35378, 6661, 38, 2]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

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





	@slow
	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   (
							    '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'
							)
							_UpperCamelCase					:		str 				=   [
							    0,
							    3293,
							    83,
							    10,
							    4552,
							    4989,
							    7986,
							    678,
							    10,
							    5915,
							    111,
							    179459,
							    124850,
							    4,
							    6044,
							    237,
							    12,
							    6,
							    5,
							    6,
							    4,
							    6780,
							    705,
							    15,
							    1388,
							    44,
							    378,
							    10114,
							    711,
							    152,
							    20,
							    6,
							    5,
							    22376,
							    642,
							    1221,
							    15190,
							    34153,
							    450,
							    5608,
							    959,
							    1119,
							    57702,
							    136,
							    186,
							    47,
							    1098,
							    29367,
							    47,
							    # 4426, # What fairseq tokenizes from "<unk>": "_<"
							    # 3678, # What fairseq tokenizes from "<unk>": "unk"
							    # 2740, # What fairseq tokenizes from "<unk>": ">"
							    3,  # What we tokenize from "<unk>": "<unk>"
							    6,  # Residue from the tokenization: an extra sentencepiece underline
							    4,
							    6044,
							    237,
							    6284,
							    50901,
							    528,
							    31,
							    90,
							    34,
							    927,
							    2,
							]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

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





	@slow
	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							# fmt: off
							_UpperCamelCase					:		Union[str, Any] 				=   {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 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, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 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]], '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, 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, 0, 0, 0, 0, 0], [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, 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=lowerCamelCase__ ,model_name='xlm-roberta-base' ,revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3' ,)





import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase_       	=	{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

lowerCAmelCase_       	=	{
    '''google/bigbird-roberta-base''': 40_96,
    '''google/bigbird-roberta-large''': 40_96,
    '''google/bigbird-base-trivia-itc''': 40_96,
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE     :   List[Any]									=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE     :   List[Any]									=				["input_ids", "attention_mask"]
			SCREAMING_SNAKE_CASE     :   List[int]									=				[]







			def __init__(     self :				List[str]							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Dict="<unk>"							,   _UpperCamelCase :				List[str]="<s>"							,   _UpperCamelCase :				Tuple="</s>"							,   _UpperCamelCase :				Any="<pad>"							,   _UpperCamelCase :				Any="[SEP]"							,   _UpperCamelCase :				Optional[Any]="[MASK]"							,   _UpperCamelCase :				Any="[CLS]"							,   _UpperCamelCase :				Optional[Dict[str, Any]] = None							,   **_UpperCamelCase :				Dict							,   )      ->None:
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else bos_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else eos_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else unk_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else pad_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else cls_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else sep_token

									# Mask token behave like a normal word, i.e. include the space before it
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else mask_token

									snake_case_   =	{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=_UpperCamelCase							,   eos_token=_UpperCamelCase							,   unk_token=_UpperCamelCase							,   pad_token=_UpperCamelCase							,   sep_token=_UpperCamelCase							,   mask_token=_UpperCamelCase							,   cls_token=_UpperCamelCase							,   sp_model_kwargs=self.sp_model_kwargs							,   **_UpperCamelCase							,   )

									snake_case_   =	vocab_file

									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(_UpperCamelCase							)







			@property
			def 				snake_case__(     self :				str							)      ->List[Any]:
									return self.sp_model.get_piece_size()







			def 				snake_case__(     self :				int							)      ->Union[str, Any]:
									snake_case_   =	{self.convert_ids_to_tokens(_UpperCamelCase							): i for i in range(self.vocab_size							)}
									vocab.update(self.added_tokens_encoder							)
									return vocab







			def __getstate__(     self :				Tuple							)      ->Any:
									snake_case_   =	self.__dict__.copy()
									snake_case_   =	None
									return state







			def __setstate__(     self :				str							,   _UpperCamelCase :				List[Any]							)      ->List[str]:
									snake_case_   =	d

									# for backward compatibility
									if not hasattr(self							,   '''sp_model_kwargs'''							):
															snake_case_   =	{}

									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(self.vocab_file							)







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				str							)      ->List[str]:
									return self.sp_model.encode(_UpperCamelCase							,   out_type=_UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				List[str]							)      ->Tuple:
									return self.sp_model.piece_to_id(_UpperCamelCase							)







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				str							)      ->List[Any]:
									snake_case_   =	self.sp_model.IdToPiece(_UpperCamelCase							)
									return token







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				Optional[int]							)      ->List[str]:
									snake_case_   =	[]
									snake_case_   =	''''''
									snake_case_   =	False
									for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																					if not prev_is_special:
																											out_string += " "
																					out_string += self.sp_model.decode(_UpperCamelCase							) + token
																					snake_case_   =	True
																					snake_case_   =	[]
															else:
																					current_sub_tokens.append(_UpperCamelCase							)
																					snake_case_   =	False
									out_string += self.sp_model.decode(_UpperCamelCase							)
									return out_string.strip()







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				bool = True							,   **_UpperCamelCase :				List[str]							,   )      ->str:
									snake_case_   =	kwargs.pop('''use_source_tokenizer'''							,   _UpperCamelCase							)

									snake_case_   =	self.convert_ids_to_tokens(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							)

									# To avoid mixing byte-level and unicode for byte-level BPT
									# we need to build string separately for added tokens and byte-level tokens
									# cf. https://github.com/huggingface/transformers/issues/1133
									snake_case_   =	[]
									snake_case_   =	[]
									for token in filtered_tokens:
															if skip_special_tokens and token in self.all_special_ids:
																					continue
															if token in self.added_tokens_encoder:
																					if current_sub_text:
																											sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase							)							)
																											snake_case_   =	[]
																					sub_texts.append(_UpperCamelCase							)
															else:
																					current_sub_text.append(_UpperCamelCase							)
									if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase							)							)

									# Mimic the behavior of the Rust tokenizer:
									# No space before [MASK] and [SEP]
									if spaces_between_special_tokens:
															snake_case_   =	re.sub(R''' (\[(MASK|SEP)\])'''							,   R'''\1'''							,   ''' '''.join(_UpperCamelCase							)							)
									else:
															snake_case_   =	''''''.join(_UpperCamelCase							)

									snake_case_   =	(
									    clean_up_tokenization_spaces
									    if clean_up_tokenization_spaces is not None
									    else self.clean_up_tokenization_spaces
									)
									if clean_up_tokenization_spaces:
															snake_case_   =	self.clean_up_tokenization(_UpperCamelCase							)
															return clean_text
									else:
															return text







			def 				snake_case__(     self :				List[Any]							,   _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
									snake_case_   =	os.path.join(
									    _UpperCamelCase							,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

									if os.path.abspath(self.vocab_file							) != os.path.abspath(_UpperCamelCase							) and os.path.isfile(self.vocab_file							):
															copyfile(self.vocab_file							,   _UpperCamelCase							)
									elif not os.path.isfile(self.vocab_file							):
															with open(_UpperCamelCase							,   '''wb'''							) as fi:
																					snake_case_   =	self.sp_model.serialized_model_proto()
																					fi.write(_UpperCamelCase							)

									return (out_vocab_file,)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									snake_case_   =	[self.cls_token_id]
									snake_case_   =	[self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep







			def 				snake_case__(     self :				List[str]							,   _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							)

									if token_ids_a is None:
															return [1] + ([0] * len(_UpperCamelCase							)) + [1]
									return [1] + ([0] * len(_UpperCamelCase							)) + [1] + ([0] * len(_UpperCamelCase							)) + [1]







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									snake_case_   =	[self.sep_token_id]
									snake_case_   =	[self.cls_token_id]
									if token_ids_a is None:
															return len(cls + token_ids_a + sep							) * [0]
									return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]