




'''simple docstring'''


import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 		snake_case_   (UpperCamelCase	:  Any			,						UpperCamelCase	:  Optional[int]=0.999			,						UpperCamelCase	:  Optional[int]="cosine"			,						):





							'''simple docstring'''


							if alpha_transform_type == "cosine":

														def alpha_bar_fn(UpperCamelCase	:  Any				):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(UpperCamelCase	:  int				):
																					return math.exp(t * -12.0				)

							else:
														raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'				)

							_a     					=    []
							for i in range(UpperCamelCase				):
														_a     					=    i / num_diffusion_timesteps
														_a     					=    (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(UpperCamelCase				) / alpha_bar_fn(UpperCamelCase				)			,						UpperCamelCase				)				)
							return torch.tensor(UpperCamelCase			,						dtype=torch.floataa				)

class       A   (		_a					,_a   ):
							lowercase_          =    [e.name for e in KarrasDiffusionSchedulers]
							lowercase_          =    2







							@register_to_config
							def __init__(  self	:  int							,      lowerCAmelCase_	:  int = 10_00							,      lowerCAmelCase_	:  float = 0.0_0_0_8_5							,      lowerCAmelCase_	:  float = 0.0_1_2							,      lowerCAmelCase_	:  str = "linear"							,      lowerCAmelCase_	:  Optional[Union[np.ndarray, List[float]]] = None							,      lowerCAmelCase_	:  str = "epsilon"							,      lowerCAmelCase_	:  Optional[bool] = False							,      lowerCAmelCase_	:  Optional[bool] = False							,      lowerCAmelCase_	:  float = 1.0							,      lowerCAmelCase_	:  str = "linspace"							,      lowerCAmelCase_	:  int = 0							,      )      ->							List[Any]:


														"""simple docstring"""


														if trained_betas is not None:
																					_a     					=    torch.tensor(lowerCAmelCase_							,      dtype=torch.floataa )
														elif beta_schedule == "linear":
																					_a     					=    torch.linspace(lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_							,      dtype=torch.floataa )
														elif beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					_a     					=    (
																					    torch.linspace(beta_start**0.5							,      beta_end**0.5							,      lowerCAmelCase_							,      dtype=torch.floataa ) ** 2
																					)
														elif beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					_a     					=    betas_for_alpha_bar(lowerCAmelCase_							,      alpha_transform_type='''cosine''' )
														elif beta_schedule == "exp":
																					_a     					=    betas_for_alpha_bar(lowerCAmelCase_							,      alpha_transform_type='''exp''' )
														else:
																					raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )

														_a     					=    1.0 - self.betas
														_a     					=    torch.cumprod(self.alphas							,      dim=0 )

														#  set all values
														self.set_timesteps(lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_ )
														_a     					=    use_karras_sigmas







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


														"""simple docstring"""


														if schedule_timesteps is None:
																					_a     					=    self.timesteps

														_a     					=    (schedule_timesteps == timestep).nonzero()

														# The sigma index that is taken for the **very** first `step`
														# is always the second index (or the last index if there is only 1)
														# This way we can ensure we don't accidentally skip a sigma in
														# case we start in the middle of the denoising schedule (e.g. for image-to-image)
														if len(self._index_counter ) == 0:
																					_a     					=    1 if len(lowerCAmelCase_ ) > 1 else 0
														else:
																					_a     					=    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_ ) else timestep
																					_a     					=    self._index_counter[timestep_int]

														return indices[pos].item()







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


														"""simple docstring"""


														if self.config.timestep_spacing in ["linspace", "trailing"]:
																					return self.sigmas.max()

														return (self.sigmas.max() ** 2 + 1) ** 0.5







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  torch.FloatTensor							,      lowerCAmelCase_	:  Union[float, torch.FloatTensor]							,      )      ->							torch.FloatTensor:


														"""simple docstring"""


														_a     					=    self.index_for_timestep(lowerCAmelCase_ )

														_a     					=    self.sigmas[step_index]
														_a     					=    sample / ((sigma**2 + 1) ** 0.5)
														return sample







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  Union[str, torch.device] = None							,      lowerCAmelCase_	:  Optional[int] = None							,      )      ->							str:


														"""simple docstring"""


														_a     					=    num_inference_steps

														_a     					=    num_train_timesteps or self.config.num_train_timesteps

														# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
														if self.config.timestep_spacing == "linspace":
																					_a     					=    np.linspace(0							,      num_train_timesteps - 1							,      lowerCAmelCase_							,      dtype=lowerCAmelCase_ )[::-1].copy()
														elif self.config.timestep_spacing == "leading":
																					_a     					=    num_train_timesteps // self.num_inference_steps
																					# creates integer timesteps by multiplying by ratio
																					# casting to int to avoid issues when num_inference_step is power of 3
																					_a     					=    (np.arange(0							,      lowerCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase_ )
																					timesteps += self.config.steps_offset
														elif self.config.timestep_spacing == "trailing":
																					_a     					=    num_train_timesteps / self.num_inference_steps
																					# creates integer timesteps by multiplying by ratio
																					# casting to int to avoid issues when num_inference_step is power of 3
																					_a     					=    (np.arange(lowerCAmelCase_							,      0							,      -step_ratio )).round().copy().astype(lowerCAmelCase_ )
																					timesteps -= 1
														else:
																					raise ValueError(
																					    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.' )

														_a     					=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
														_a     					=    np.log(lowerCAmelCase_ )
														_a     					=    np.interp(lowerCAmelCase_							,      np.arange(0							,      len(lowerCAmelCase_ ) )							,      lowerCAmelCase_ )

														if self.config.use_karras_sigmas:
																					_a     					=    self._convert_to_karras(in_sigmas=lowerCAmelCase_							,      num_inference_steps=self.num_inference_steps )
																					_a     					=    np.array([self._sigma_to_t(lowerCAmelCase_							,      lowerCAmelCase_ ) for sigma in sigmas] )

														_a     					=    np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
														_a     					=    torch.from_numpy(lowerCAmelCase_ ).to(device=lowerCAmelCase_ )
														_a     					=    torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )

														_a     					=    torch.from_numpy(lowerCAmelCase_ )
														_a     					=    torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )

														if str(lowerCAmelCase_ ).startswith('''mps''' ):
																					# mps does not support float64
																					_a     					=    timesteps.to(lowerCAmelCase_							,      dtype=torch.floataa )
														else:
																					_a     					=    timesteps.to(device=lowerCAmelCase_ )

														# empty dt and derivative
														_a     					=    None
														_a     					=    None

														# for exp beta schedules, such as the one for `pipeline_shap_e.py`
														# we need an index counter
														_a     					=    defaultdict(lowerCAmelCase_ )







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


														"""simple docstring"""


														_a     					=    np.log(lowerCAmelCase_ )

														# get distribution
														_a     					=    log_sigma - log_sigmas[:, np.newaxis]

														# get sigmas range
														_a     					=    np.cumsum((dists >= 0)							,      axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
														_a     					=    low_idx + 1

														_a     					=    log_sigmas[low_idx]
														_a     					=    log_sigmas[high_idx]

														# interpolate sigmas
														_a     					=    (low - log_sigma) / (low - high)
														_a     					=    np.clip(lowerCAmelCase_							,      0							,      1 )

														# transform interpolation to time range
														_a     					=    (1 - w) * low_idx + w * high_idx
														_a     					=    t.reshape(sigma.shape )
														return t







							def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  torch.FloatTensor							,      lowerCAmelCase_	:  int )      ->							torch.FloatTensor:


														"""simple docstring"""


														_a     					=    in_sigmas[-1].item()
														_a     					=    in_sigmas[0].item()

														_a     					=    7.0  # 7.0 is the value used in the paper
														_a     					=    np.linspace(0							,      1							,      lowerCAmelCase_ )
														_a     					=    sigma_min ** (1 / rho)
														_a     					=    sigma_max ** (1 / rho)
														_a     					=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
														return sigmas







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


														"""simple docstring"""


														return self.dt is None







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  Union[torch.FloatTensor, np.ndarray]							,      lowerCAmelCase_	:  Union[float, torch.FloatTensor]							,      lowerCAmelCase_	:  Union[torch.FloatTensor, np.ndarray]							,      lowerCAmelCase_	:  bool = True							,      )      ->							Union[SchedulerOutput, Tuple]:


														"""simple docstring"""


														_a     					=    self.index_for_timestep(lowerCAmelCase_ )

														# advance index counter by 1
														_a     					=    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_ ) else timestep
														self._index_counter[timestep_int] += 1

														if self.state_in_first_order:
																					_a     					=    self.sigmas[step_index]
																					_a     					=    self.sigmas[step_index + 1]
														else:
																					# 2nd order / Heun's method
																					_a     					=    self.sigmas[step_index - 1]
																					_a     					=    self.sigmas[step_index]

														# currently only gamma=0 is supported. This usually works best anyways.
														# We can support gamma in the future but then need to scale the timestep before
														# passing it to the model which requires a change in API
														_a     					=    0
														_a     					=    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

														# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
														if self.config.prediction_type == "epsilon":
																					_a     					=    sigma_hat if self.state_in_first_order else sigma_next
																					_a     					=    sample - sigma_input * model_output
														elif self.config.prediction_type == "v_prediction":
																					_a     					=    sigma_hat if self.state_in_first_order else sigma_next
																					_a     					=    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																					    sample / (sigma_input**2 + 1)
																					)
														elif self.config.prediction_type == "sample":
																					_a     					=    model_output
														else:
																					raise ValueError(
																					    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`' )

														if self.config.clip_sample:
																					_a     					=    pred_original_sample.clamp(
																					    -self.config.clip_sample_range							,      self.config.clip_sample_range )

														if self.state_in_first_order:
																					# 2. Convert to an ODE derivative for 1st order
																					_a     					=    (sample - pred_original_sample) / sigma_hat
																					# 3. delta timestep
																					_a     					=    sigma_next - sigma_hat

																					# store for 2nd order step
																					_a     					=    derivative
																					_a     					=    dt
																					_a     					=    sample
														else:
																					# 2. 2nd order / Heun's method
																					_a     					=    (sample - pred_original_sample) / sigma_next
																					_a     					=    (self.prev_derivative + derivative) / 2

																					# 3. take prev timestep & sample
																					_a     					=    self.dt
																					_a     					=    self.sample

																					# free dt and derivative
																					# Note, this puts the scheduler in "first order mode"
																					_a     					=    None
																					_a     					=    None
																					_a     					=    None

														_a     					=    sample + derivative * dt

														if not return_dict:
																					return (prev_sample,)

														return SchedulerOutput(prev_sample=lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  torch.FloatTensor							,      lowerCAmelCase_	:  torch.FloatTensor							,      lowerCAmelCase_	:  torch.FloatTensor							,      )      ->							torch.FloatTensor:


														"""simple docstring"""


														_a     					=    self.sigmas.to(device=original_samples.device							,      dtype=original_samples.dtype )
														if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase_ ):
																					# mps does not support float64
																					_a     					=    self.timesteps.to(original_samples.device							,      dtype=torch.floataa )
																					_a     					=    timesteps.to(original_samples.device							,      dtype=torch.floataa )
														else:
																					_a     					=    self.timesteps.to(original_samples.device )
																					_a     					=    timesteps.to(original_samples.device )

														_a     					=    [self.index_for_timestep(lowerCAmelCase_							,      lowerCAmelCase_ ) for t in timesteps]

														_a     					=    sigmas[step_indices].flatten()
														while len(sigma.shape ) < len(original_samples.shape ):
																					_a     					=    sigma.unsqueeze(-1 )

														_a     					=    original_samples + noise * sigma
														return noisy_samples







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


														"""simple docstring"""


														return self.config.num_train_timesteps







'''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]

