
'''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 		_UpperCAmelCase (     _lowerCamelCase  :				List[Any]     ,					_lowerCamelCase  :				Tuple=0.999     ,					_lowerCamelCase  :				Any="cosine"     ,					)			->  List[str]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(_lowerCamelCase  :				Optional[int]						):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(_lowerCamelCase  :				str						):
      return math.exp(t * -12.0						)

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

  _lowerCAmelCase				:     int       =    []
  for i in range(UpperCamelCase__						):
    _lowerCAmelCase				:     Dict       =    i / num_diffusion_timesteps
    _lowerCAmelCase				:     Tuple       =    (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_     (UpperCamelCase__							,	UpperCamelCase__			):
       __lowerCAmelCase			: Any			=					[e.name for e in KarrasDiffusionSchedulers]
       __lowerCAmelCase			: Optional[Any]			=					2




       @register_to_config
       def __init__(   self      ,      snake_case_ = 1_0_0_0      ,      snake_case_ = 0.0_0085      ,      snake_case_ = 0.012      ,      snake_case_ = "linear"      ,      snake_case_ = None      ,      snake_case_ = "epsilon"      ,      snake_case_ = False      ,      snake_case_ = False      ,      snake_case_ = 1.0      ,      snake_case_ = "linspace"      ,      snake_case_ = 0      ,      ):
         if trained_betas is not None:
           _lowerCAmelCase				:     List[Any]       =    torch.tensor(__lowerCamelCase      ,      dtype=torch.floataa     )
         elif beta_schedule == "linear":
           _lowerCAmelCase				:     List[Any]       =    torch.linspace(__lowerCamelCase      ,      __lowerCamelCase      ,      __lowerCamelCase      ,      dtype=torch.floataa     )
         elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           _lowerCAmelCase				:     Tuple       =    (
               torch.linspace(beta_start**0.5      ,      beta_end**0.5      ,      __lowerCamelCase      ,      dtype=torch.floataa     ) ** 2
           )
         elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           _lowerCAmelCase				:     Any       =    betas_for_alpha_bar(__lowerCamelCase      ,      alpha_transform_type="""cosine"""     )
         elif beta_schedule == "exp":
           _lowerCAmelCase				:     Union[str, Any]       =    betas_for_alpha_bar(__lowerCamelCase      ,      alpha_transform_type="""exp"""     )
         else:
           raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'     )

         _lowerCAmelCase				:     Any       =    1.0 - self.betas
         _lowerCAmelCase				:     List[str]       =    torch.cumprod(self.alphas      ,      dim=0     )

         #  set all values
         self.set_timesteps(__lowerCamelCase      ,      __lowerCamelCase      ,      __lowerCamelCase     )
         _lowerCAmelCase				:     Optional[Any]       =    use_karras_sigmas




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_=None     ):
         if schedule_timesteps is None:
           _lowerCAmelCase				:     Dict       =    self.timesteps

         _lowerCAmelCase				:     str       =    (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:
           _lowerCAmelCase				:     Tuple       =    1 if len(__lowerCamelCase     ) > 1 else 0
         else:
           _lowerCAmelCase				:     List[str]       =    timestep.cpu().item() if torch.is_tensor(__lowerCamelCase     ) else timestep
           _lowerCAmelCase				:     Any       =    self._index_counter[timestep_int]

         return indices[pos].item()




       @property
       def 							__UpperCamelCase				(   self     ):
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

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




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      ):
         _lowerCAmelCase				:     Optional[int]       =    self.index_for_timestep(__lowerCamelCase     )

         _lowerCAmelCase				:     List[str]       =    self.sigmas[step_index]
         _lowerCAmelCase				:     str       =    sample / ((sigma**2 + 1) ** 0.5)
         return sample




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_ = None      ,      snake_case_ = None      ,      ):
         _lowerCAmelCase				:     Union[str, Any]       =    num_inference_steps

         _lowerCAmelCase				:     Optional[Any]       =    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":
           _lowerCAmelCase				:     str       =    np.linspace(0      ,      num_train_timesteps - 1      ,      __lowerCamelCase      ,      dtype=__lowerCamelCase     )[::-1].copy()
         elif self.config.timestep_spacing == "leading":
           _lowerCAmelCase				:     Tuple       =    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
           _lowerCAmelCase				:     str       =    (np.arange(0      ,      __lowerCamelCase     ) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase     )
           timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
           _lowerCAmelCase				:     Optional[Any]       =    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
           _lowerCAmelCase				:     Tuple       =    (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\'.'     )

         _lowerCAmelCase				:     Any       =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
         _lowerCAmelCase				:     Dict       =    np.log(__lowerCamelCase     )
         _lowerCAmelCase				:     Any       =    np.interp(__lowerCamelCase      ,      np.arange(0      ,      len(__lowerCamelCase     )     )      ,      __lowerCamelCase     )

         if self.config.use_karras_sigmas:
           _lowerCAmelCase				:     List[str]       =    self._convert_to_karras(in_sigmas=__lowerCamelCase      ,      num_inference_steps=self.num_inference_steps     )
           _lowerCAmelCase				:     str       =    np.array([self._sigma_to_t(__lowerCamelCase      ,      __lowerCamelCase     ) for sigma in sigmas]     )

         _lowerCAmelCase				:     Dict       =    np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
         _lowerCAmelCase				:     Tuple       =    torch.from_numpy(__lowerCamelCase     ).to(device=__lowerCamelCase     )
         _lowerCAmelCase				:     Any       =    torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2     ), sigmas[-1:]]     )

         _lowerCAmelCase				:     Optional[Any]       =    torch.from_numpy(__lowerCamelCase     )
         _lowerCAmelCase				:     Any       =    torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2     )]     )

         if str(__lowerCamelCase     ).startswith("""mps"""     ):
           # mps does not support float64
           _lowerCAmelCase				:     Any       =    timesteps.to(__lowerCamelCase      ,      dtype=torch.floataa     )
         else:
           _lowerCAmelCase				:     Optional[int]       =    timesteps.to(device=__lowerCamelCase     )

         # empty dt and derivative
         _lowerCAmelCase				:     List[str]       =    None
         _lowerCAmelCase				:     List[Any]       =    None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         _lowerCAmelCase				:     List[Any]       =    defaultdict(__lowerCamelCase     )




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_     ):
         # get log sigma
         _lowerCAmelCase				:     str       =    np.log(__lowerCamelCase     )

         # get distribution
         _lowerCAmelCase				:     Optional[Any]       =    log_sigma - log_sigmas[:, np.newaxis]

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

         _lowerCAmelCase				:     Tuple       =    log_sigmas[low_idx]
         _lowerCAmelCase				:     List[str]       =    log_sigmas[high_idx]

         # interpolate sigmas
         _lowerCAmelCase				:     List[Any]       =    (low - log_sigma) / (low - high)
         _lowerCAmelCase				:     Union[str, Any]       =    np.clip(__lowerCamelCase      ,      0      ,      1     )

         # transform interpolation to time range
         _lowerCAmelCase				:     List[Any]       =    (1 - w) * low_idx + w * high_idx
         _lowerCAmelCase				:     Any       =    t.reshape(sigma.shape     )
         return t




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_     ):
         _lowerCAmelCase				:     Any       =    in_sigmas[-1].item()
         _lowerCAmelCase				:     List[str]       =    in_sigmas[0].item()

         _lowerCAmelCase				:     str       =    7.0  # 7.0 is the value used in the paper
         _lowerCAmelCase				:     str       =    np.linspace(0      ,      1      ,      __lowerCamelCase     )
         _lowerCAmelCase				:     Dict       =    sigma_min ** (1 / rho)
         _lowerCAmelCase				:     str       =    sigma_max ** (1 / rho)
         _lowerCAmelCase				:     int       =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas




       @property
       def 							__UpperCamelCase				(   self     ):
         return self.dt is None




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_ = True      ,      ):
         _lowerCAmelCase				:     Any       =    self.index_for_timestep(__lowerCamelCase     )

         # advance index counter by 1
         _lowerCAmelCase				:     Dict       =    timestep.cpu().item() if torch.is_tensor(__lowerCamelCase     ) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
           _lowerCAmelCase				:     str       =    self.sigmas[step_index]
           _lowerCAmelCase				:     Tuple       =    self.sigmas[step_index + 1]
         else:
           # 2nd order / Heun's method
           _lowerCAmelCase				:     Tuple       =    self.sigmas[step_index - 1]
           _lowerCAmelCase				:     Optional[Any]       =    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
         _lowerCAmelCase				:     Union[str, Any]       =    0
         _lowerCAmelCase				:     List[str]       =    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":
           _lowerCAmelCase				:     List[str]       =    sigma_hat if self.state_in_first_order else sigma_next
           _lowerCAmelCase				:     int       =    sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
           _lowerCAmelCase				:     List[Any]       =    sigma_hat if self.state_in_first_order else sigma_next
           _lowerCAmelCase				:     Any       =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**2 + 1)
           )
         elif self.config.prediction_type == "sample":
           _lowerCAmelCase				:     Dict       =    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:
           _lowerCAmelCase				:     Dict       =    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
           _lowerCAmelCase				:     Optional[int]       =    (sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           _lowerCAmelCase				:     Any       =    sigma_next - sigma_hat

           # store for 2nd order step
           _lowerCAmelCase				:     int       =    derivative
           _lowerCAmelCase				:     Any       =    dt
           _lowerCAmelCase				:     int       =    sample
         else:
           # 2. 2nd order / Heun's method
           _lowerCAmelCase				:     List[Any]       =    (sample - pred_original_sample) / sigma_next
           _lowerCAmelCase				:     Optional[Any]       =    (self.prev_derivative + derivative) / 2

           # 3. take prev timestep & sample
           _lowerCAmelCase				:     Optional[int]       =    self.dt
           _lowerCAmelCase				:     Tuple       =    self.sample

           # free dt and derivative
           # Note, this puts the scheduler in "first order mode"
           _lowerCAmelCase				:     Union[str, Any]       =    None
           _lowerCAmelCase				:     int       =    None
           _lowerCAmelCase				:     Optional[int]       =    None

         _lowerCAmelCase				:     str       =    sample + derivative * dt

         if not return_dict:
           return (prev_sample,)

         return SchedulerOutput(prev_sample=__lowerCamelCase     )




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      ):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         _lowerCAmelCase				:     str       =    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
           _lowerCAmelCase				:     int       =    self.timesteps.to(original_samples.device      ,      dtype=torch.floataa     )
           _lowerCAmelCase				:     Any       =    timesteps.to(original_samples.device      ,      dtype=torch.floataa     )
         else:
           _lowerCAmelCase				:     Any       =    self.timesteps.to(original_samples.device     )
           _lowerCAmelCase				:     int       =    timesteps.to(original_samples.device     )

         _lowerCAmelCase				:     int       =    [self.index_for_timestep(__lowerCamelCase      ,      __lowerCamelCase     ) for t in timesteps]

         _lowerCAmelCase				:     List[str]       =    sigmas[step_indices].flatten()
         while len(sigma.shape     ) < len(original_samples.shape     ):
           _lowerCAmelCase				:     Optional[Any]       =    sigma.unsqueeze(-1     )

         _lowerCAmelCase				:     Dict       =    original_samples + noise * sigma
         return noisy_samples







       def __len__(   self     ):
         return self.config.num_train_timesteps









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


a__:      Union[str, Any]									=		logging.get_logger(__name__)

a__:      Union[str, Any]									=		{'vocab_file': 'spiece.model'}

a__:      Tuple									=		{
    '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'
        ),
    }
}

a__:      Any									=		{
    'google/bigbird-roberta-base': 4_096,
    'google/bigbird-roberta-large': 4_096,
    'google/bigbird-base-trivia-itc': 4_096,
}




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE							       =	['''input_ids''', '''attention_mask''']
 __SCREAMING_SNAKE_CASE							       =	[]



 def __init__(							self,__lowerCamelCase,__lowerCamelCase="<unk>",__lowerCamelCase="<s>",__lowerCamelCase="</s>",__lowerCamelCase="<pad>",__lowerCamelCase="[SEP]",__lowerCamelCase="[MASK]",__lowerCamelCase="[CLS]",__lowerCamelCase = None,**__lowerCamelCase,):
        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 				UpperCamelCase			(							self ):
        return self.sp_model.get_piece_size()



 def 				UpperCamelCase			(							self ):
        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 ):
        A__											=						self.__dict__.copy()
        A__											=						None
        return state



 def __setstate__(							self,__lowerCamelCase ):
        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 				UpperCamelCase			(							self,__lowerCamelCase ):
        return self.sp_model.encode(__lowerCamelCase,out_type=__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        return self.sp_model.piece_to_id(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						self.sp_model.IdToPiece(__lowerCamelCase )
        return token



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = False,__lowerCamelCase = None,__lowerCamelCase = True,**__lowerCamelCase,):
        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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = False ):
        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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        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]





