


import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
						import torch

						from transformers import (
						    MraForMaskedLM,
						    MraForMultipleChoice,
						    MraForQuestionAnswering,
						    MraForSequenceClassification,
						    MraForTokenClassification,
						    MraModel,
						)
						from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class        _lowercase      :






				'''simple docstring'''



				def __init__(       self			:List[Any]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Optional[Any]=2			,			lowerCAmelCase__			:Tuple=8			,			lowerCAmelCase__			:List[Any]=True			,			lowerCAmelCase__			:Optional[Any]=True			,			lowerCAmelCase__			:int=True			,			lowerCAmelCase__			:str=True			,			lowerCAmelCase__			:Tuple=99			,			lowerCAmelCase__			:int=16			,			lowerCAmelCase__			:int=5			,			lowerCAmelCase__			:Optional[Any]=2			,			lowerCAmelCase__			:List[Any]=36			,			lowerCAmelCase__			:Optional[Any]="gelu"			,			lowerCAmelCase__			:List[Any]=0.0			,			lowerCAmelCase__			:Any=0.0			,			lowerCAmelCase__			:Union[str, Any]=512			,			lowerCAmelCase__			:List[str]=16			,			lowerCAmelCase__			:List[str]=2			,			lowerCAmelCase__			:Union[str, Any]=0.02			,			lowerCAmelCase__			:Any=3			,			lowerCAmelCase__			:str=4			,			lowerCAmelCase__			:Optional[Any]=None			,			)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     int					=		parent
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		batch_size
										__SCREAMING_SNAKE_CASE						:     Any					=		seq_length
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		is_training
										__SCREAMING_SNAKE_CASE						:     int					=		use_input_mask
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		use_token_type_ids
										__SCREAMING_SNAKE_CASE						:     Dict					=		use_labels
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		vocab_size
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		hidden_size
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		num_attention_heads
										__SCREAMING_SNAKE_CASE						:     Dict					=		intermediate_size
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		hidden_act
										__SCREAMING_SNAKE_CASE						:     int					=		hidden_dropout_prob
										__SCREAMING_SNAKE_CASE						:     List[str]					=		attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE						:     Any					=		max_position_embeddings
										__SCREAMING_SNAKE_CASE						:     int					=		type_vocab_size
										__SCREAMING_SNAKE_CASE						:     int					=		type_sequence_label_size
										__SCREAMING_SNAKE_CASE						:     Tuple					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     Tuple					=		num_labels
										__SCREAMING_SNAKE_CASE						:     List[str]					=		num_choices
										__SCREAMING_SNAKE_CASE						:     Any					=		scope


				def 		__magic_name__(       self			:Optional[int]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size	)

										__SCREAMING_SNAKE_CASE						:     Tuple					=		None
										if self.use_input_mask:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		random_attention_mask([self.batch_size, self.seq_length]	)

										__SCREAMING_SNAKE_CASE						:     Any					=		None
										if self.use_token_type_ids:
																__SCREAMING_SNAKE_CASE						:     Tuple					=		ids_tensor([self.batch_size, self.seq_length]			,			self.type_vocab_size	)

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		None
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
										if self.use_labels:
																__SCREAMING_SNAKE_CASE						:     int					=		ids_tensor([self.batch_size]			,			self.type_sequence_label_size	)
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		ids_tensor([self.batch_size, self.seq_length]			,			self.num_labels	)
																__SCREAMING_SNAKE_CASE						:     str					=		ids_tensor([self.batch_size]			,			self.num_choices	)

										__SCREAMING_SNAKE_CASE						:     int					=		self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


				def 		__magic_name__(       self			:Any	)		->			Dict:
										return MraConfig(
										    vocab_size=self.vocab_size			,			hidden_size=self.hidden_size			,			num_hidden_layers=self.num_hidden_layers			,			num_attention_heads=self.num_attention_heads			,			intermediate_size=self.intermediate_size			,			hidden_act=self.hidden_act			,			hidden_dropout_prob=self.hidden_dropout_prob			,			attention_probs_dropout_prob=self.attention_probs_dropout_prob			,			max_position_embeddings=self.max_position_embeddings			,			type_vocab_size=self.type_vocab_size			,			is_decoder=lowercase_			,			initializer_range=self.initializer_range			,			)


				def 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     int					=		self.get_config()
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		300
										return config


				def 		__magic_name__(       self			:Any	)		->			Any:
										(
										    __SCREAMING_SNAKE_CASE   
										)						:     Union[str, Any]					=		self.prepare_config_and_inputs()

										__SCREAMING_SNAKE_CASE						:     str					=		True
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
										__SCREAMING_SNAKE_CASE						:     Dict					=		ids_tensor([self.batch_size, self.seq_length]			,			vocab_size=2	)

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)


				def 		__magic_name__(       self			:List[Any]			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:Optional[Any]	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		MraModel(config=lowercase_	)
										model.to(lowercase_	)
										model.eval()
										__SCREAMING_SNAKE_CASE						:     Tuple					=		model(lowercase_			,			attention_mask=lowercase_			,			token_type_ids=lowercase_	)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		model(lowercase_			,			token_type_ids=lowercase_	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		model(lowercase_	)
										self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)	)


				def 		__magic_name__(       self			:Optional[int]			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:Optional[int]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Tuple			,			lowerCAmelCase__			:Any			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Any			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Dict			,			)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Any					=		True
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		MraModel(lowercase_	)
										model.to(lowercase_	)
										model.eval()
										__SCREAMING_SNAKE_CASE						:     List[str]					=		model(
										    lowercase_			,			attention_mask=lowercase_			,			token_type_ids=lowercase_			,			encoder_hidden_states=lowercase_			,			encoder_attention_mask=lowercase_			,			)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		model(
										    lowercase_			,			attention_mask=lowercase_			,			token_type_ids=lowercase_			,			encoder_hidden_states=lowercase_			,			)
										__SCREAMING_SNAKE_CASE						:     Dict					=		model(lowercase_			,			attention_mask=lowercase_			,			token_type_ids=lowercase_	)
										self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)	)


				def 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:int			,			lowerCAmelCase__			:Any			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:int			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:str	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     str					=		MraForMaskedLM(config=lowercase_	)
										model.to(lowercase_	)
										model.eval()
										__SCREAMING_SNAKE_CASE						:     List[str]					=		model(lowercase_			,			attention_mask=lowercase_			,			token_type_ids=lowercase_			,			labels=lowercase_	)
										self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size)	)


				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Any	)		->			int:
										__SCREAMING_SNAKE_CASE						:     Dict					=		MraForQuestionAnswering(config=lowercase_	)
										model.to(lowercase_	)
										model.eval()
										__SCREAMING_SNAKE_CASE						:     int					=		model(
										    lowercase_			,			attention_mask=lowercase_			,			token_type_ids=lowercase_			,			start_positions=lowercase_			,			end_positions=lowercase_			,			)
										self.parent.assertEqual(result.start_logits.shape			,			(self.batch_size, self.seq_length)	)
										self.parent.assertEqual(result.end_logits.shape			,			(self.batch_size, self.seq_length)	)


				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Optional[int]			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Tuple			,			lowerCAmelCase__			:List[str]	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.num_labels
										__SCREAMING_SNAKE_CASE						:     Dict					=		MraForSequenceClassification(lowercase_	)
										model.to(lowercase_	)
										model.eval()
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		model(lowercase_			,			attention_mask=lowercase_			,			token_type_ids=lowercase_			,			labels=lowercase_	)
										self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)	)


				def 		__magic_name__(       self			:Optional[int]			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:int			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:Tuple			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:Any	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		self.num_labels
										__SCREAMING_SNAKE_CASE						:     int					=		MraForTokenClassification(config=lowercase_	)
										model.to(lowercase_	)
										model.eval()
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		model(lowercase_			,			attention_mask=lowercase_			,			token_type_ids=lowercase_			,			labels=lowercase_	)
										self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.num_labels)	)


				def 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Tuple			,			lowerCAmelCase__			:Any	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.num_choices
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		MraForMultipleChoice(config=lowercase_	)
										model.to(lowercase_	)
										model.eval()
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		input_ids.unsqueeze(1	).expand(-1			,			self.num_choices			,			-1	).contiguous()
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		token_type_ids.unsqueeze(1	).expand(-1			,			self.num_choices			,			-1	).contiguous()
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		input_mask.unsqueeze(1	).expand(-1			,			self.num_choices			,			-1	).contiguous()
										__SCREAMING_SNAKE_CASE						:     int					=		model(
										    lowercase_			,			attention_mask=lowercase_			,			token_type_ids=lowercase_			,			labels=lowercase_			,			)
										self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_choices)	)



				def 		__magic_name__(       self			:int	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.prepare_config_and_inputs()
										(
										    __SCREAMING_SNAKE_CASE   
										)						:     Optional[Any]					=		config_and_inputs
										__SCREAMING_SNAKE_CASE						:     List[str]					=		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict




@require_torch
class        _lowercase      (			lowercase__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Tuple						=		(
				    (
				        MraModel,
				        MraForMaskedLM,
				        MraForMultipleChoice,
				        MraForQuestionAnswering,
				        MraForSequenceClassification,
				        MraForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		False
				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		False
				SCREAMING_SNAKE_CASE__				:			List[str]						=		False
				SCREAMING_SNAKE_CASE__				:			Tuple						=		False

				SCREAMING_SNAKE_CASE__				:			Dict						=		()


				def 		__magic_name__(       self			:Optional[Any]	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		MraModelTester(self	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		ConfigTester(self			,			config_class=lowercase_			,			hidden_size=37	)


				def 		__magic_name__(       self			:List[str]	)		->			Union[str, Any]:
										self.config_tester.run_common_tests()


				def 		__magic_name__(       self			:int	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase_	)


				def 		__magic_name__(       self			:List[str]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Any					=		self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																__SCREAMING_SNAKE_CASE						:     Tuple					=		type
																self.model_tester.create_and_check_model(*lowercase_	)


				def 		__magic_name__(       self			:Tuple	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowercase_	)


				def 		__magic_name__(       self			:Any	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*lowercase_	)


				def 		__magic_name__(       self			:List[Any]	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowercase_	)


				def 		__magic_name__(       self			:Optional[int]	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowercase_	)


				def 		__magic_name__(       self			:List[Any]	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowercase_	)


				@slow
				def 		__magic_name__(       self			:Dict	)		->			Any:
										for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		MraModel.from_pretrained(lowercase_	)
																self.assertIsNotNone(lowercase_	)



				@unittest.skip(reason='''MRA does not output attentions'''	)
				def 		__magic_name__(       self			:List[Any]	)		->			Optional[int]:
										return




@require_torch
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				@slow
				def 		__magic_name__(       self			:List[str]	)		->			List[str]:
										__SCREAMING_SNAKE_CASE						:     Any					=		MraModel.from_pretrained('''uw-madison/mra-base-512-4'''	)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		torch.arange(256	).unsqueeze(0	)

										with torch.no_grad():
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		model(lowercase_	)[0]

										__SCREAMING_SNAKE_CASE						:     str					=		torch.Size((1, 256, 768)	)
										self.assertEqual(output.shape			,			lowercase_	)

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		torch.tensor(
										    [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]	)

										self.assertTrue(torch.allclose(output[:, :3, :3]			,			lowercase_			,			atol=1E-4	)	)


				@slow
				def 		__magic_name__(       self			:Dict	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''	)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		torch.arange(256	).unsqueeze(0	)

										with torch.no_grad():
																__SCREAMING_SNAKE_CASE						:     Dict					=		model(lowercase_	)[0]

										__SCREAMING_SNAKE_CASE						:     Dict					=		50_265

										__SCREAMING_SNAKE_CASE						:     str					=		torch.Size((1, 256, vocab_size)	)
										self.assertEqual(output.shape			,			lowercase_	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		torch.tensor(
										    [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]	)

										self.assertTrue(torch.allclose(output[:, :3, :3]			,			lowercase_			,			atol=1E-4	)	)



				@slow
				def 		__magic_name__(       self			:Optional[Any]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		torch.arange(4_096	).unsqueeze(0	)

										with torch.no_grad():
																__SCREAMING_SNAKE_CASE						:     str					=		model(lowercase_	)[0]

										__SCREAMING_SNAKE_CASE						:     List[str]					=		50_265

										__SCREAMING_SNAKE_CASE						:     Tuple					=		torch.Size((1, 4_096, vocab_size)	)
										self.assertEqual(output.shape			,			lowercase_	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		torch.tensor(
										    [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]	)

										self.assertTrue(torch.allclose(output[:, :3, :3]			,			lowercase_			,			atol=1E-4	)	)


"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_fnet import FNetTokenizer
else:
    _a		       =							None

_a		       =							logging.get_logger(__name__)
_a		       =							{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_a		       =							{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

_a		       =							{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

_a		       =							'▁'


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[str]       =		VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			:						Tuple       =		PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			:						Any       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		["""input_ids""", """token_type_ids"""]
  SCREAMING_SNAKE_CASE__			:						Tuple       =		FNetTokenizer







  def __init__(		self				,   lowercase_=None				,   lowercase_=None				,   lowercase_=False				,   lowercase_=True				,   lowercase_=True				,   lowercase_="<unk>"				,   lowercase_="[SEP]"				,   lowercase_="<pad>"				,   lowercase_="[CLS]"				,   lowercase_="[MASK]"				,   **lowercase_				,   ):

     """simple docstring"""
     # Mask token behave like a normal word, i.e. include the space before it and
     # is included in the raw text, there should be a match in a non-normalized sentence.
     UpperCAmelCase_					:					int			     =    (
         AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_				,   normalized=lowercase_  )
         if isinstance(lowercase_				,   lowercase_  )
         else mask_token
     )

     super().__init__(
         lowercase_				,   tokenizer_file=lowercase_				,   do_lower_case=lowercase_				,   remove_space=lowercase_				,   keep_accents=lowercase_				,   unk_token=lowercase_				,   sep_token=lowercase_				,   pad_token=lowercase_				,   cls_token=lowercase_				,   mask_token=lowercase_				,   **lowercase_				,   )

     UpperCAmelCase_					:					Any			     =    do_lower_case
     UpperCAmelCase_					:					Tuple			     =    remove_space
     UpperCAmelCase_					:					str			     =    keep_accents
     UpperCAmelCase_					:					Any			     =    vocab_file
     UpperCAmelCase_					:					List[Any]			     =    False if not self.vocab_file else True







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Any			     =    [self.cls_token_id]
     if token_ids_a is None:
        return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Any			     =    [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]







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     if not os.path.isdir(lowercase_  ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
        return
     UpperCAmelCase_					:					List[str]			     =    os.path.join(
         lowercase_				,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

     if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowercase_  ):
        copyfile(self.vocab_file				,   lowercase_  )

     return (out_vocab_file,)


