code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import math
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  
  
  __A :		Optional[int]      							=    logging.get_logger(__name__)  # pylint: disable=invalid-name
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  torch.FloatTensor
     lowercase       :  Optional[torch.FloatTensor]      =				None
  
  
  
  
  
  
  
  def 					A_			(		snake_case_     :       str						,snake_case_     :       str=0.999						,snake_case_     :       Dict="cosine"						,):
  
  
  
  
  
  
  
      '''simple docstring'''
      if alpha_transform_type == "cosine":
  
          def alpha_bar_fn(snake_case_     :       Optional[Any]    ):
              return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2
  
      elif alpha_transform_type == "exp":
  
          def alpha_bar_fn(snake_case_     :       Dict    ):
              return math.exp(t * -12.0    )
  
      else:
          raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'    )
  
      UpperCamelCase				:   Any          =							[]
      for i in range(snake_case_    ):
          UpperCamelCase				:   List[Any]          =							i / num_diffusion_timesteps
          UpperCamelCase				:   str          =							(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(snake_case_    ) / alpha_bar_fn(snake_case_    )						,snake_case_    )    )
      return torch.tensor(snake_case_						,dtype=torch.floataa    )
  
  
  class 							lowerCamelCase		( _UpperCAmelCase	,  _UpperCAmelCase				):
     lowercase       :  int      =				[e.name for e in KarrasDiffusionSchedulers]
     lowercase       :  List[Any]      =				1
  
  
  
  
  
  
  
     @register_to_config
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_ = 1000    ,      SCREAMING_SNAKE_CASE_ = 0.0001    ,      SCREAMING_SNAKE_CASE_ = 0.02    ,      SCREAMING_SNAKE_CASE_ = "linear"    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = "epsilon"    ,      SCREAMING_SNAKE_CASE_ = "linspace"    ,      SCREAMING_SNAKE_CASE_ = 0    ,      ):
         if trained_betas is not None:
             UpperCamelCase				:   int          =							torch.tensor(SCREAMING_SNAKE_CASE_    ,      dtype=torch.floataa )
         elif beta_schedule == "linear":
             UpperCamelCase				:   Optional[Any]          =							torch.linspace(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      dtype=torch.floataa )
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             UpperCamelCase				:   Optional[int]          =							(
                 torch.linspace(beta_start**0.5    ,      beta_end**0.5    ,      SCREAMING_SNAKE_CASE_    ,      dtype=torch.floataa ) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCamelCase				:   List[Any]          =							betas_for_alpha_bar(SCREAMING_SNAKE_CASE_ )
         else:
             raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}' )
  
         UpperCamelCase				:   int          =							1.0 - self.betas
         UpperCamelCase				:   Union[str, Any]          =							torch.cumprod(self.alphas    ,      dim=0 )
  
         UpperCamelCase				:   Optional[int]          =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
         UpperCamelCase				:   Tuple          =							np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
         UpperCamelCase				:   str          =							torch.from_numpy(SCREAMING_SNAKE_CASE_ )
  
         # setable values
         UpperCamelCase				:   Tuple          =							None
         UpperCamelCase				:   Optional[int]          =							np.linspace(0    ,      num_train_timesteps - 1    ,      SCREAMING_SNAKE_CASE_    ,      dtype=SCREAMING_SNAKE_CASE_ )[::-1].copy()
         UpperCamelCase				:   Any          =							torch.from_numpy(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Tuple          =							False
  
  
  
  
  
  
  
     @property
     def       a_							(							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       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         if isinstance(SCREAMING_SNAKE_CASE_    ,      torch.Tensor ):
             UpperCamelCase				:   str          =							timestep.to(self.timesteps.device )
         UpperCamelCase				:   str          =							(self.timesteps == timestep).nonzero().item()
         UpperCamelCase				:   Tuple          =							self.sigmas[step_index]
         UpperCamelCase				:   str          =							sample / ((sigma**2 + 1) ** 0.5)
         UpperCamelCase				:   Optional[int]          =							True
         return sample
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         UpperCamelCase				:   Optional[int]          =							num_inference_steps
  
         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
             UpperCamelCase				:   Dict          =							np.linspace(0    ,      self.config.num_train_timesteps - 1    ,      SCREAMING_SNAKE_CASE_    ,      dtype=SCREAMING_SNAKE_CASE_ )[
                 ::-1
             ].copy()
         elif self.config.timestep_spacing == "leading":
             UpperCamelCase				:   Optional[int]          =							self.config.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
             UpperCamelCase				:   Union[str, Any]          =							(np.arange(0    ,      SCREAMING_SNAKE_CASE_ ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_ )
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             UpperCamelCase				:   List[Any]          =							self.config.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
             UpperCamelCase				:   Optional[int]          =							(np.arange(self.config.num_train_timesteps    ,      0    ,      -step_ratio )).round().copy().astype(SCREAMING_SNAKE_CASE_ )
             timesteps -= 1
         else:
             raise ValueError(
                 f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.' )
  
         UpperCamelCase				:   List[str]          =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
         UpperCamelCase				:   Any          =							np.interp(SCREAMING_SNAKE_CASE_    ,      np.arange(0    ,      len(SCREAMING_SNAKE_CASE_ ) )    ,      SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Optional[int]          =							np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
         UpperCamelCase				:   Optional[int]          =							torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ )
         if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
             # mps does not support float64
             UpperCamelCase				:   str          =							torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_    ,      dtype=torch.floataa )
         else:
             UpperCamelCase				:   Tuple          =							torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = True    ,      ):
  
         if (
             isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
             or isinstance(SCREAMING_SNAKE_CASE_    ,      torch.IntTensor )
             or isinstance(SCREAMING_SNAKE_CASE_    ,      torch.LongTensor )
         ):
             raise ValueError(
                 (
                     """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                     """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
                     """ one of the `scheduler.timesteps` as a timestep."""
                 )    ,      )
  
         if not self.is_scale_input_called:
             logger.warning(
                 """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                 """See `StableDiffusionPipeline` for a usage example.""" )
  
         if isinstance(SCREAMING_SNAKE_CASE_    ,      torch.Tensor ):
             UpperCamelCase				:   Tuple          =							timestep.to(self.timesteps.device )
  
         UpperCamelCase				:   Tuple          =							(self.timesteps == timestep).nonzero().item()
         UpperCamelCase				:   Union[str, Any]          =							self.sigmas[step_index]
  
         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
             UpperCamelCase				:   Tuple          =							sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             UpperCamelCase				:   Tuple          =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         elif self.config.prediction_type == "sample":
             raise NotImplementedError("""prediction_type not implemented yet: sample""" )
         else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`' )
  
         UpperCamelCase				:   Optional[Any]          =							self.sigmas[step_index]
         UpperCamelCase				:   Tuple          =							self.sigmas[step_index + 1]
         UpperCamelCase				:   Dict          =							(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
         UpperCamelCase				:   Optional[Any]          =							(sigma_to**2 - sigma_up**2) ** 0.5
  
         # 2. Convert to an ODE derivative
         UpperCamelCase				:   Tuple          =							(sample - pred_original_sample) / sigma
  
         UpperCamelCase				:   Union[str, Any]          =							sigma_down - sigma
  
         UpperCamelCase				:   Optional[int]          =							sample + derivative * dt
  
         UpperCamelCase				:   Optional[int]          =							model_output.device
         UpperCamelCase				:   List[Any]          =							randn_tensor(model_output.shape    ,      dtype=model_output.dtype    ,      device=SCREAMING_SNAKE_CASE_    ,      generator=SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Optional[Any]          =							prev_sample + noise * sigma_up
  
         if not return_dict:
             return (prev_sample,)
  
         return EulerAncestralDiscreteSchedulerOutput(
             prev_sample=SCREAMING_SNAKE_CASE_    ,      pred_original_sample=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      ):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         UpperCamelCase				:   Optional[int]          =							self.sigmas.to(device=original_samples.device    ,      dtype=original_samples.dtype )
         if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_ ):
             # mps does not support float64
             UpperCamelCase				:   int          =							self.timesteps.to(original_samples.device    ,      dtype=torch.floataa )
             UpperCamelCase				:   Any          =							timesteps.to(original_samples.device    ,      dtype=torch.floataa )
         else:
             UpperCamelCase				:   List[Any]          =							self.timesteps.to(original_samples.device )
             UpperCamelCase				:   Any          =							timesteps.to(original_samples.device )
  
         UpperCamelCase				:   Any          =							[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
         UpperCamelCase				:   Optional[Any]          =							sigmas[step_indices].flatten()
         while len(sigma.shape ) < len(original_samples.shape ):
             UpperCamelCase				:   Optional[Any]          =							sigma.unsqueeze(-1 )
  
         UpperCamelCase				:   Optional[int]          =							original_samples + noise * sigma
         return noisy_samples
  
  
  
  
  
  
  
     def __len__(							self ):
         return self.config.num_train_timesteps
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from tokenizers import processors
  
  from ...tokenization_utils import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
    from .tokenization_mbartaa import MBartaaTokenizer
  else:
    __A :		Tuple      							=    None
  
  
  __A :		Optional[Any]      							=    logging.get_logger(__name__)
  
  __A :		Optional[Any]      							=    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  __A :		Optional[Any]      							=    {
      '''vocab_file''': {
          '''facebook/mbart-large-50-one-to-many-mmt''': (
              '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
          ),
      },
      '''tokenizer_file''': {
          '''facebook/mbart-large-50-one-to-many-mmt''': (
              '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
          ),
      },
  }
  
  __A :		List[Any]      							=    {
      '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
  }
  
  # fmt: off
  __A :		Dict      							=    ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  List[Any]      =				VOCAB_FILES_NAMES
     lowercase       :  Optional[Any]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase       :  List[Any]      =				PRETRAINED_VOCAB_FILES_MAP
     lowercase       :  Tuple      =				['input_ids', 'attention_mask']
     lowercase       :  Optional[int]      =				MBartaaTokenizer
  
     lowercase       :  List[int]      =				[]
     lowercase       :  List[int]      =				[]
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_="</s>"    ,      SCREAMING_SNAKE_CASE_="</s>"    ,      SCREAMING_SNAKE_CASE_="<s>"    ,      SCREAMING_SNAKE_CASE_="<unk>"    ,      SCREAMING_SNAKE_CASE_="<pad>"    ,      SCREAMING_SNAKE_CASE_="<mask>"    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCamelCase				:   Optional[int]          =							AddedToken(SCREAMING_SNAKE_CASE_    ,      lstrip=SCREAMING_SNAKE_CASE_    ,      rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) else mask_token
  
         UpperCamelCase				:   Dict          =							kwargs.get("""additional_special_tokens"""    ,      [] )
         kwargs["additional_special_tokens"] += [
             code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
         ]
  
         super().__init__(
             SCREAMING_SNAKE_CASE_    ,      src_lang=SCREAMING_SNAKE_CASE_    ,      tgt_lang=SCREAMING_SNAKE_CASE_    ,      tokenizer_file=SCREAMING_SNAKE_CASE_    ,      eos_token=SCREAMING_SNAKE_CASE_    ,      sep_token=SCREAMING_SNAKE_CASE_    ,      cls_token=SCREAMING_SNAKE_CASE_    ,      unk_token=SCREAMING_SNAKE_CASE_    ,      pad_token=SCREAMING_SNAKE_CASE_    ,      mask_token=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_    ,      )
  
         UpperCamelCase				:   int          =							vocab_file
         UpperCamelCase				:   Optional[Any]          =							False if not self.vocab_file else True
  
         UpperCamelCase				:   List[Any]          =							{
             lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }
  
         UpperCamelCase				:   List[str]          =							src_lang if src_lang is not None else """en_XX"""
         UpperCamelCase				:   Union[str, Any]          =							tgt_lang
         UpperCamelCase				:   Union[str, Any]          =							self.lang_code_to_id[self._src_lang]
         self.set_src_lang_special_tokens(self._src_lang )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self._src_lang
  
  
  
  
  
  
  
     @src_lang.setter
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   str          =							new_src_lang
         self.set_src_lang_special_tokens(self._src_lang )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = "en_XX"    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = "ro_RO"    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         UpperCamelCase				:   str          =							src_lang
         UpperCamelCase				:   Optional[Any]          =							tgt_lang
         return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.set_src_lang_special_tokens(self.src_lang )
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.set_tgt_lang_special_tokens(self.tgt_lang )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[Any]          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Tuple          =							[self.cur_lang_code_id]
         UpperCamelCase				:   Optional[int]          =							[self.eos_token_id]
  
         UpperCamelCase				:   Dict          =							self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCamelCase				:   Any          =							self.convert_ids_to_tokens(self.suffix_tokens )
  
         UpperCamelCase				:   Any          =							processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens ) )    ,      )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Any          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Optional[Any]          =							[self.cur_lang_code_id]
         UpperCamelCase				:   Tuple          =							[self.eos_token_id]
  
         UpperCamelCase				:   str          =							self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCamelCase				:   Dict          =							self.convert_ids_to_tokens(self.suffix_tokens )
  
         UpperCamelCase				:   Dict          =							processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens ) )    ,      )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         if src_lang is None or tgt_lang is None:
             raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
         UpperCamelCase				:   Optional[int]          =							src_lang
         UpperCamelCase				:   Optional[int]          =							self(SCREAMING_SNAKE_CASE_    ,      add_special_tokens=SCREAMING_SNAKE_CASE_    ,      return_tensors=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Dict          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   str          =							tgt_lang_id
         return inputs
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer.""" )
  
         if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
             return
         UpperCamelCase				:   Optional[int]          =							os.path.join(
             SCREAMING_SNAKE_CASE_    ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
  
         if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ):
             copyfile(self.vocab_file    ,      SCREAMING_SNAKE_CASE_ )
  
         return (out_vocab_file,)
  
  
  
  
  