




'''simple docstring'''


import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import BertGenerationDecoder, BertGenerationEncoder

class       A   :







							def __init__(  self	:  int							,      lowerCAmelCase_	:  Dict							,      lowerCAmelCase_	:  int=13							,      lowerCAmelCase_	:  List[str]=7							,      lowerCAmelCase_	:  Any=True							,      lowerCAmelCase_	:  Union[str, Any]=True							,      lowerCAmelCase_	:  Any=99							,      lowerCAmelCase_	:  Any=32							,      lowerCAmelCase_	:  int=5							,      lowerCAmelCase_	:  Tuple=4							,      lowerCAmelCase_	:  List[str]=37							,      lowerCAmelCase_	:  Union[str, Any]="gelu"							,      lowerCAmelCase_	:  Dict=0.1							,      lowerCAmelCase_	:  Tuple=0.1							,      lowerCAmelCase_	:  int=50							,      lowerCAmelCase_	:  Union[str, Any]=0.0_2							,      lowerCAmelCase_	:  Optional[Any]=True							,      lowerCAmelCase_	:  str=None							,      )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    parent
														_a     					=    batch_size
														_a     					=    seq_length
														_a     					=    is_training
														_a     					=    use_input_mask
														_a     					=    vocab_size
														_a     					=    hidden_size
														_a     					=    num_hidden_layers
														_a     					=    num_attention_heads
														_a     					=    intermediate_size
														_a     					=    hidden_act
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    max_position_embeddings
														_a     					=    initializer_range
														_a     					=    use_labels
														_a     					=    scope







							def        __lowerCAmelCase      (  self	:  Any )      ->							Tuple:


														"""simple docstring"""


														_a     					=    ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size )

														_a     					=    None
														if self.use_input_mask:
																					_a     					=    random_attention_mask([self.batch_size, self.seq_length] )

														if self.use_labels:
																					_a     					=    ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size )

														_a     					=    self.get_config()

														return config, input_ids, input_mask, token_labels







							def        __lowerCAmelCase      (  self	:  Any )      ->							int:


														"""simple docstring"""


														return BertGenerationConfig(
														    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							,      is_decoder=lowerCAmelCase_							,      initializer_range=self.initializer_range							,      )







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


														"""simple docstring"""


														(
														    (
														    _a
														)					,							(
														    _a
														)					,							(
														    _a
														)					,							(
														    _a
														)					,							
														)     					=    self.prepare_config_and_inputs()

														_a     					=    True
														_a     					=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
														_a     					=    ids_tensor([self.batch_size, self.seq_length]							,      vocab_size=2 )

														return (
														    config,
														    input_ids,
														    input_mask,
														    token_labels,
														    encoder_hidden_states,
														    encoder_attention_mask,
														)







							def        __lowerCAmelCase      (  self	:  Union[str, Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  List[str]							,      lowerCAmelCase_	:  Tuple							,      lowerCAmelCase_	:  int							,      **lowerCAmelCase_	:  Union[str, Any]							,      )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    BertGenerationEncoder(config=lowerCAmelCase_ )
														model.to(lowerCAmelCase_ )
														model.eval()
														_a     					=    model(lowerCAmelCase_							,      attention_mask=lowerCAmelCase_ )
														_a     					=    model(lowerCAmelCase_ )
														self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size) )







							def        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Dict							,      lowerCAmelCase_	:  Dict							,      lowerCAmelCase_	:  List[str]							,      lowerCAmelCase_	:  List[str]							,      **lowerCAmelCase_	:  Any							,      )      ->							Dict:


														"""simple docstring"""


														_a     					=    True
														_a     					=    BertGenerationEncoder(config=lowerCAmelCase_ )
														model.to(lowerCAmelCase_ )
														model.eval()
														_a     					=    model(
														    lowerCAmelCase_							,      attention_mask=lowerCAmelCase_							,      encoder_hidden_states=lowerCAmelCase_							,      encoder_attention_mask=lowerCAmelCase_							,      )
														_a     					=    model(
														    lowerCAmelCase_							,      attention_mask=lowerCAmelCase_							,      encoder_hidden_states=lowerCAmelCase_							,      )
														self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size) )







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  Optional[int]							,      lowerCAmelCase_	:  Optional[int]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  List[str]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  int							,      **lowerCAmelCase_	:  Dict							,      )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    True
														_a     					=    True
														_a     					=    BertGenerationDecoder(config=lowerCAmelCase_ ).to(lowerCAmelCase_ ).eval()

														# first forward pass
														_a     					=    model(
														    lowerCAmelCase_							,      attention_mask=lowerCAmelCase_							,      encoder_hidden_states=lowerCAmelCase_							,      encoder_attention_mask=lowerCAmelCase_							,      use_cache=lowerCAmelCase_							,      )
														_a     					=    outputs.past_key_values

														# create hypothetical multiple next token and extent to next_input_ids
														_a     					=    ids_tensor((self.batch_size, 3)							,      config.vocab_size )
														_a     					=    ids_tensor((self.batch_size, 3)							,      vocab_size=2 )

														# append to next input_ids and
														_a     					=    torch.cat([input_ids, next_tokens]							,      dim=-1 )
														_a     					=    torch.cat([input_mask, next_mask]							,      dim=-1 )

														_a     					=    model(
														    lowerCAmelCase_							,      attention_mask=lowerCAmelCase_							,      encoder_hidden_states=lowerCAmelCase_							,      encoder_attention_mask=lowerCAmelCase_							,      output_hidden_states=lowerCAmelCase_							,      )['''hidden_states'''][0]
														_a     					=    model(
														    lowerCAmelCase_							,      attention_mask=lowerCAmelCase_							,      encoder_hidden_states=lowerCAmelCase_							,      encoder_attention_mask=lowerCAmelCase_							,      past_key_values=lowerCAmelCase_							,      output_hidden_states=lowerCAmelCase_							,      )['''hidden_states'''][0]

														# select random slice
														_a     					=    ids_tensor((1,)							,      output_from_past.shape[-1] ).item()
														_a     					=    output_from_no_past[:, -3:, random_slice_idx].detach()
														_a     					=    output_from_past[:, :, random_slice_idx].detach()

														self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

														# test that outputs are equal for slice
														self.parent.assertTrue(torch.allclose(lowerCAmelCase_							,      lowerCAmelCase_							,      atol=1e-3 ) )







							def        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  Any							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  Optional[int]							,      lowerCAmelCase_	:  int							,      *lowerCAmelCase_	:  Optional[int]							,      )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    BertGenerationDecoder(lowerCAmelCase_ )
														model.to(lowerCAmelCase_ )
														model.eval()
														_a     					=    model(lowerCAmelCase_							,      attention_mask=lowerCAmelCase_							,      labels=lowerCAmelCase_ )
														self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.seq_length, self.vocab_size) )







							def        __lowerCAmelCase      (  self	:  int )      ->							int:


														"""simple docstring"""


														_a					,							_a					,							_a					,							_a     					=    self.prepare_config_and_inputs()
														_a     					=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
														return config, inputs_dict



@require_torch
class       A   (		_a					,_a					,_a					,unittest.TestCase   ):
							lowercase_          =    (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
							lowercase_          =    (BertGenerationDecoder,) if is_torch_available() else ()
							lowercase_          =    (
							    {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
							    if is_torch_available()
							    else {}
							)







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


														"""simple docstring"""


														_a     					=    BertGenerationEncoderTester(self )
														_a     					=    ConfigTester(self							,      config_class=lowerCAmelCase_							,      hidden_size=37 )







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


														"""simple docstring"""


														self.config_tester.run_common_tests()







							def        __lowerCAmelCase      (  self	:  str )      ->							Tuple:


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCAmelCase_ )







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


														"""simple docstring"""


														_a					,							_a					,							_a					,							_a     					=    self.model_tester.prepare_config_and_inputs()
														_a     					=    '''bert'''
														self.model_tester.create_and_check_model(lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs_for_decoder()
														self.model_tester.create_and_check_model_as_decoder(*lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							int:


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs_for_decoder()
														self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCAmelCase_ )







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


														"""simple docstring"""


														(
														    (
														    _a
														)					,							(
														    _a
														)					,							(
														    _a
														)					,							(
														    _a
														)					,							(
														    _a
														)					,							(
														    _a
														)					,							
														)     					=    self.model_tester.prepare_config_and_inputs_for_decoder()

														_a     					=    None

														self.model_tester.create_and_check_model_as_decoder(
														    lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_							,      )







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


														"""simple docstring"""


														_a     					=    self.model_tester.prepare_config_and_inputs_for_decoder()
														self.model_tester.create_and_check_for_causal_lm(*lowerCAmelCase_ )







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


														"""simple docstring"""


														_a     					=    BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
														self.assertIsNotNone(lowerCAmelCase_ )



@require_torch
class       A   (		unittest.TestCase   ):







							@slow
							def        __lowerCAmelCase      (  self	:  Dict )      ->							Any:


														"""simple docstring"""


														_a     					=    BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
														_a     					=    torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]] )
														with torch.no_grad():
																					_a     					=    model(lowerCAmelCase_ )[0]
														_a     					=    torch.Size([1, 8, 10_24] )
														self.assertEqual(output.shape							,      lowerCAmelCase_ )
														_a     					=    torch.tensor(
														    [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]] )
														self.assertTrue(torch.allclose(output[:, :3, :3]							,      lowerCAmelCase_							,      atol=1e-4 ) )



@require_torch
class       A   (		unittest.TestCase   ):







							@slow
							def        __lowerCAmelCase      (  self	:  List[str] )      ->							str:


														"""simple docstring"""


														_a     					=    BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
														_a     					=    torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]] )
														with torch.no_grad():
																					_a     					=    model(lowerCAmelCase_ )[0]
														_a     					=    torch.Size([1, 8, 5_03_58] )
														self.assertEqual(output.shape							,      lowerCAmelCase_ )
														_a     					=    torch.tensor(
														    [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]] )
														self.assertTrue(torch.allclose(output[:, :3, :3]							,      lowerCAmelCase_							,      atol=1e-4 ) )







'''simple docstring'''


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


_snake_case				:				str       							=		logging.get_logger(__name__)

_snake_case				:				Tuple       							=		{'vocab_file': 'spiece.model'}

_snake_case				:				Optional[int]       							=		{
    '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'
        ),
    }
}

_snake_case				:				Tuple       							=		{
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}

class       A   (		_a   ):
							lowercase_          =    VOCAB_FILES_NAMES
							lowercase_          =    PRETRAINED_VOCAB_FILES_MAP
							lowercase_          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_          =    ['input_ids', 'attention_mask']
							lowercase_          =    []







							def __init__(  self	:  Optional[Any]							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Optional[int]="<unk>"							,      lowerCAmelCase_	:  Tuple="<s>"							,      lowerCAmelCase_	:  List[str]="</s>"							,      lowerCAmelCase_	:  int="<pad>"							,      lowerCAmelCase_	:  Union[str, Any]="[SEP]"							,      lowerCAmelCase_	:  Dict="[MASK]"							,      lowerCAmelCase_	:  Optional[int]="[CLS]"							,      lowerCAmelCase_	:  Optional[Dict[str, Any]] = None							,      **lowerCAmelCase_	:  Union[str, Any]							,      )      ->							None:


														"""simple docstring"""


														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else bos_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else eos_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else unk_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else pad_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else cls_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else sep_token

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

														_a     					=    {} if sp_model_kwargs is None else sp_model_kwargs

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

														_a     					=    vocab_file

														_a     					=    spm.SentencePieceProcessor(**self.sp_model_kwargs )
														self.sp_model.Load(lowerCAmelCase_ )







							@property
							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							int:


														"""simple docstring"""


														return self.sp_model.get_piece_size()







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


														"""simple docstring"""


														_a     					=    {self.convert_ids_to_tokens(lowerCAmelCase_ ): i for i in range(self.vocab_size )}
														vocab.update(self.added_tokens_encoder )
														return vocab







							def __getstate__(  self	:  str )      ->							Tuple:


														"""simple docstring"""


														_a     					=    self.__dict__.copy()
														_a     					=    None
														return state







							def __setstate__(  self	:  List[str]							,      lowerCAmelCase_	:  Any )      ->							Dict:


														"""simple docstring"""


														_a     					=    d

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

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







							def        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  str )      ->							List[str]:


														"""simple docstring"""


														return self.sp_model.encode(lowerCAmelCase_							,      out_type=lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  List[str] )      ->							int:


														"""simple docstring"""


														return self.sp_model.piece_to_id(lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  Tuple )      ->							str:


														"""simple docstring"""


														_a     					=    self.sp_model.IdToPiece(lowerCAmelCase_ )
														return token







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  Union[str, Any] )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    []
														_a     					=    ''''''
														_a     					=    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(lowerCAmelCase_ ) + token
																												_a     					=    True
																												_a     					=    []
																					else:
																												current_sub_tokens.append(lowerCAmelCase_ )
																												_a     					=    False
														out_string += self.sp_model.decode(lowerCAmelCase_ )
														return out_string.strip()







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  bool = False							,      lowerCAmelCase_	:  bool = None							,      lowerCAmelCase_	:  bool = True							,      **lowerCAmelCase_	:  Tuple							,      )      ->							str:


														"""simple docstring"""


														_a     					=    kwargs.pop('''use_source_tokenizer'''							,      lowerCAmelCase_ )

														_a     					=    self.convert_ids_to_tokens(lowerCAmelCase_							,      skip_special_tokens=lowerCAmelCase_ )

														# 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
														_a     					=    []
														_a     					=    []
														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(lowerCAmelCase_ ) )
																																			_a     					=    []
																												sub_texts.append(lowerCAmelCase_ )
																					else:
																												current_sub_text.append(lowerCAmelCase_ )
														if current_sub_text:
																					sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase_ ) )

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

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







							def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Optional[str] = None )      ->							Tuple[str]:


														"""simple docstring"""


														if not os.path.isdir(lowerCAmelCase_ ):
																					logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
																					return
														_a     					=    os.path.join(
														    lowerCAmelCase_							,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

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

														return (out_vocab_file,)







							def        __lowerCAmelCase      (  self	:  int							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  Optional[List[int]] = None )      ->							List[int]:


														"""simple docstring"""


														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														_a     					=    [self.cls_token_id]
														_a     					=    [self.sep_token_id]
														return cls + token_ids_a + sep + token_ids_a + sep







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  Optional[List[int]] = None							,      lowerCAmelCase_	:  bool = False )      ->							List[int]:


														"""simple docstring"""


														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=lowerCAmelCase_							,      token_ids_a=lowerCAmelCase_							,      already_has_special_tokens=lowerCAmelCase_ )

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







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  Optional[List[int]] = None )      ->							List[int]:


														"""simple docstring"""


														_a     					=    [self.sep_token_id]
														_a     					=    [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]

