code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from dataclasses import dataclass
  from typing import Any, Dict, Optional
  
  import torch
  import torch.nn.functional as F
  from torch import nn
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..models.embeddings import ImagePositionalEmbeddings
  from ..utils import BaseOutput, deprecate
  from .attention import BasicTransformerBlock
  from .embeddings import PatchEmbed
  from .modeling_utils import ModelMixin
  
  
  
  
  
  
  @dataclass
  class 						lowercase     (   a_):
        __lowercase 		=  42
  
  
  
  
  
  
  class 						lowercase     (   a_				,			a_):
  
  
  
  
  
  
        @register_to_config
        def __init__(self ,  a__ = 1_6 ,  a__ = 8_8 ,  a__ = None ,  a__ = None ,  a__ = 1 ,  a__ = 0.0 ,  a__ = 3_2 ,  a__ = None ,  a__ = False ,  a__ = None ,  a__ = None ,  a__ = None ,  a__ = "geglu" ,  a__ = None ,  a__ = False ,  a__ = False ,  a__ = False ,  a__ = "layer_norm" ,  a__ = True ,  )       ->   int:
           super().__init__()
           SCREAMING_SNAKE_CASE__			:				Tuple       						= use_linear_projection
           SCREAMING_SNAKE_CASE__			:				Tuple       						= num_attention_heads
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= attention_head_dim
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= num_attention_heads * attention_head_dim
  
           # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
           # Define whether input is continuous or discrete depending on configuration
           SCREAMING_SNAKE_CASE__			:				int       						= (in_channels is not None) and (patch_size is None)
           SCREAMING_SNAKE_CASE__			:				Tuple       						= num_vector_embeds is not None
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= in_channels is not None and patch_size is not None
  
           if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
              SCREAMING_SNAKE_CASE__			:				int       						= (
                  f"""The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"""
                  ''' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'''
                  ''' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'''
                  ''' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'''
                  ''' would be very nice if you could open a Pull request for the `transformer/config.json` file'''
              )
              deprecate('''norm_type!=num_embeds_ada_norm''' ,  '''1.0.0''' ,  _snake_case ,  standard_warn=_snake_case					)
              SCREAMING_SNAKE_CASE__			:				List[str]       						= '''ada_norm'''
  
           if self.is_input_continuous and self.is_input_vectorized:
              raise ValueError(
                  f"""Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"""
                  ''' sure that either `in_channels` or `num_vector_embeds` is None.'''					)
           elif self.is_input_vectorized and self.is_input_patches:
              raise ValueError(
                  f"""Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"""
                  ''' sure that either `num_vector_embeds` or `num_patches` is None.'''					)
           elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
              raise ValueError(
                  f"""Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"""
                  f""" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."""					)
  
           # 2. Define input layers
           if self.is_input_continuous:
              SCREAMING_SNAKE_CASE__			:				List[str]       						= in_channels
  
              SCREAMING_SNAKE_CASE__			:				Dict       						= torch.nn.GroupNorm(num_groups=_snake_case ,  num_channels=_snake_case ,  eps=1E-6 ,  affine=_snake_case					)
              if use_linear_projection:
                 SCREAMING_SNAKE_CASE__			:				Dict       						= nn.Linear(_snake_case ,  _snake_case					)
              else:
                 SCREAMING_SNAKE_CASE__			:				Tuple       						= nn.Convad(_snake_case ,  _snake_case ,  kernel_size=1 ,  stride=1 ,  padding=0					)
           elif self.is_input_vectorized:
              assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
              assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
  
              SCREAMING_SNAKE_CASE__			:				Dict       						= sample_size
              SCREAMING_SNAKE_CASE__			:				Any       						= sample_size
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= num_vector_embeds
              SCREAMING_SNAKE_CASE__			:				Tuple       						= self.height * self.width
  
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= ImagePositionalEmbeddings(
                  num_embed=_snake_case ,  embed_dim=_snake_case ,  height=self.height ,  width=self.width					)
           elif self.is_input_patches:
              assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"
  
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= sample_size
              SCREAMING_SNAKE_CASE__			:				Any       						= sample_size
  
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= patch_size
              SCREAMING_SNAKE_CASE__			:				Any       						= PatchEmbed(
                  height=_snake_case ,  width=_snake_case ,  patch_size=_snake_case ,  in_channels=_snake_case ,  embed_dim=_snake_case ,  )
  
           # 3. Define transformers blocks
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= nn.ModuleList(
               [
                   BasicTransformerBlock(
                       _snake_case ,  _snake_case ,  _snake_case ,  dropout=_snake_case ,  cross_attention_dim=_snake_case ,  activation_fn=_snake_case ,  num_embeds_ada_norm=_snake_case ,  attention_bias=_snake_case ,  only_cross_attention=_snake_case ,  upcast_attention=_snake_case ,  norm_type=_snake_case ,  norm_elementwise_affine=_snake_case ,  )
                   for d in range(_snake_case					)
               ]					)
  
           # 4. Define output layers
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= in_channels if out_channels is None else out_channels
           if self.is_input_continuous:
              # TODO: should use out_channels for continuous projections
              if use_linear_projection:
                 SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= nn.Linear(_snake_case ,  _snake_case					)
              else:
                 SCREAMING_SNAKE_CASE__			:				Dict       						= nn.Convad(_snake_case ,  _snake_case ,  kernel_size=1 ,  stride=1 ,  padding=0					)
           elif self.is_input_vectorized:
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= nn.LayerNorm(_snake_case					)
              SCREAMING_SNAKE_CASE__			:				List[str]       						= nn.Linear(_snake_case ,  self.num_vector_embeds - 1					)
           elif self.is_input_patches:
              SCREAMING_SNAKE_CASE__			:				Dict       						= nn.LayerNorm(_snake_case ,  elementwise_affine=_snake_case ,  eps=1E-6					)
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= nn.Linear(_snake_case ,  2 * inner_dim					)
              SCREAMING_SNAKE_CASE__			:				Tuple       						= nn.Linear(_snake_case ,  patch_size * patch_size * self.out_channels					)
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None ,  a__ = None ,  a__ = None ,  a__ = None ,  a__ = None ,  a__ = None ,  a__ = True ,  )       ->   Any:
           # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
           #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
           #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
           # expects mask of shape:
           #   [batch, key_tokens]
           # adds singleton query_tokens dimension:
           #   [batch,                    1, key_tokens]
           # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
           #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
           #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
           if attention_mask is not None and attention_mask.ndim == 2:
              # assume that mask is expressed as:
              #   (1 = keep,      0 = discard)
              # convert mask into a bias that can be added to attention scores:
              #       (keep = +0,     discard = -10000.0)
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= (1 - attention_mask.to(hidden_states.dtype					)) * -1_0_0_0_0.0
              SCREAMING_SNAKE_CASE__			:				List[str]       						= attention_mask.unsqueeze(1					)
  
           # convert encoder_attention_mask to a bias the same way we do for attention_mask
           if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= (1 - encoder_attention_mask.to(hidden_states.dtype					)) * -1_0_0_0_0.0
              SCREAMING_SNAKE_CASE__			:				Any       						= encoder_attention_mask.unsqueeze(1					)
  
           # 1. Input
           if self.is_input_continuous:
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= hidden_states.shape
              SCREAMING_SNAKE_CASE__			:				Dict       						= hidden_states
  
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.norm(_snake_case					)
              if not self.use_linear_projection:
                 SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.proj_in(_snake_case					)
                 SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= hidden_states.shape[1]
                 SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= hidden_states.permute(0 ,  2 ,  3 ,  1					).reshape(_snake_case ,  height * width ,  _snake_case					)
              else:
                 SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= hidden_states.shape[1]
                 SCREAMING_SNAKE_CASE__			:				Dict       						= hidden_states.permute(0 ,  2 ,  3 ,  1					).reshape(_snake_case ,  height * width ,  _snake_case					)
                 SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.proj_in(_snake_case					)
           elif self.is_input_vectorized:
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.latent_image_embedding(_snake_case					)
           elif self.is_input_patches:
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.pos_embed(_snake_case					)
  
           # 2. Blocks
           for block in self.transformer_blocks:
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= block(
                  _snake_case ,  attention_mask=_snake_case ,  encoder_hidden_states=_snake_case ,  encoder_attention_mask=_snake_case ,  timestep=_snake_case ,  cross_attention_kwargs=_snake_case ,  class_labels=_snake_case ,  )
  
           # 3. Output
           if self.is_input_continuous:
              if not self.use_linear_projection:
                 SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= hidden_states.reshape(_snake_case ,  _snake_case ,  _snake_case ,  _snake_case					).permute(0 ,  3 ,  1 ,  2					).contiguous()
                 SCREAMING_SNAKE_CASE__			:				Dict       						= self.proj_out(_snake_case					)
              else:
                 SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.proj_out(_snake_case					)
                 SCREAMING_SNAKE_CASE__			:				List[str]       						= hidden_states.reshape(_snake_case ,  _snake_case ,  _snake_case ,  _snake_case					).permute(0 ,  3 ,  1 ,  2					).contiguous()
  
              SCREAMING_SNAKE_CASE__			:				str       						= hidden_states + residual
           elif self.is_input_vectorized:
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.norm_out(_snake_case					)
              SCREAMING_SNAKE_CASE__			:				Any       						= self.out(_snake_case					)
              # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= logits.permute(0 ,  2 ,  1					)
  
              # log(p(x_0))
              SCREAMING_SNAKE_CASE__			:				List[str]       						= F.log_softmax(logits.double() ,  dim=1					).float()
           elif self.is_input_patches:
              # TODO: cleanup!
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.transformer_blocks[0].norma.emb(
                  _snake_case ,  _snake_case ,  hidden_dtype=hidden_states.dtype					)
              SCREAMING_SNAKE_CASE__			:				List[str]       						= self.proj_out_a(F.silu(_snake_case					)					).chunk(2 ,  dim=1					)
              SCREAMING_SNAKE_CASE__			:				List[str]       						= self.norm_out(_snake_case					) * (1 + scale[:, None]) + shift[:, None]
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.proj_out_a(_snake_case					)
  
              # unpatchify
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= int(hidden_states.shape[1] ** 0.5					)
              SCREAMING_SNAKE_CASE__			:				Dict       						= hidden_states.reshape(
                  shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)					)
              SCREAMING_SNAKE_CASE__			:				str       						= torch.einsum('''nhwpqc->nchpwq''' ,  _snake_case					)
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= hidden_states.reshape(
                  shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)					)
  
           if not return_dict:
              return (output,)
  
           return TransformeraDModelOutput(sample=_snake_case					)
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import os
  import re
  import unicodedata
  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
  
  
  lowercase__			=	logging.get_logger(__name__)
  lowercase__			=	{'''vocab_file''': '''spiece.model'''}
  
  lowercase__			=	{
      '''vocab_file''': {
          '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
          '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
      },
  }
  
  lowercase__			=	{
      '''google/fnet-base''': 5_1_2,
      '''google/fnet-large''': 5_1_2,
  }
  
  lowercase__			=	'''▁'''
  
  
  
  
  
  
  class 						lowercase     (   UpperCAmelCase_):
        __lowercase 		=  VOCAB_FILES_NAMES
        __lowercase 		=  PRETRAINED_VOCAB_FILES_MAP
        __lowercase 		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
        __lowercase 		=  ['''input_ids''', '''token_type_ids''']
  
  
  
  
  
  
        def __init__(self ,  a__ ,  a__=False ,  a__=True ,  a__=True ,  a__="<unk>" ,  a__="[SEP]" ,  a__="<pad>" ,  a__="[CLS]" ,  a__="[MASK]" ,  a__ = None ,  **a__ ,  )       ->   None:
           # Mask token behave like a normal word, i.e. include the space before it and
           # is included in the raw text, there should be a match in a non-normalized sentence.
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= (
               AddedToken(a__ ,  lstrip=a__ ,  rstrip=a__ ,  normalized=a__					)
               if isinstance(a__ ,  a__					)
               else mask_token
           )
  
           SCREAMING_SNAKE_CASE__			:				Any       						= {} if sp_model_kwargs is None else sp_model_kwargs
  
           super().__init__(
               do_lower_case=a__ ,  remove_space=a__ ,  keep_accents=a__ ,  unk_token=a__ ,  sep_token=a__ ,  pad_token=a__ ,  cls_token=a__ ,  mask_token=a__ ,  sp_model_kwargs=self.sp_model_kwargs ,  **a__ ,  )
  
           SCREAMING_SNAKE_CASE__			:				Dict       						= do_lower_case
           SCREAMING_SNAKE_CASE__			:				Tuple       						= remove_space
           SCREAMING_SNAKE_CASE__			:				List[str]       						= keep_accents
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= vocab_file
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
           self.sp_model.Load(a__					)
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   Union[str, Any]:
           return len(self.sp_model					)
  
  
  
  
  
  
        def    A__(self					)       ->   Any:
           SCREAMING_SNAKE_CASE__			:				int       						= {self.convert_ids_to_tokens(a__					): i for i in range(self.vocab_size					)}
           vocab.update(self.added_tokens_encoder					)
           return vocab
  
  
  
  
  
  
        def __getstate__(self					)       ->   Any:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= self.__dict__.copy()
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= None
           return state
  
  
  
  
  
  
        def __setstate__(self ,  a__					)       ->   int:
           SCREAMING_SNAKE_CASE__			:				Any       						= d
  
           # for backward compatibility
           if not hasattr(self ,  '''sp_model_kwargs'''					):
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {}
  
           SCREAMING_SNAKE_CASE__			:				int       						= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
           self.sp_model.Load(self.vocab_file					)
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   str:
           if self.remove_space:
              SCREAMING_SNAKE_CASE__			:				List[str]       						= ''' '''.join(inputs.strip().split()					)
           else:
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= inputs
           SCREAMING_SNAKE_CASE__			:				Dict       						= outputs.replace('''``''' ,  '''"'''					).replace('''\'\'''' ,  '''"'''					)
  
           if not self.keep_accents:
              SCREAMING_SNAKE_CASE__			:				Dict       						= unicodedata.normalize('''NFKD''' ,  a__					)
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= ''''''.join([c for c in outputs if not unicodedata.combining(a__					)]					)
           if self.do_lower_case:
              SCREAMING_SNAKE_CASE__			:				str       						= outputs.lower()
  
           return outputs
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   List[str]:
           SCREAMING_SNAKE_CASE__			:				Dict       						= self.preprocess_text(a__					)
           SCREAMING_SNAKE_CASE__			:				List[str]       						= self.sp_model.encode(a__ ,  out_type=a__					)
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= []
           for piece in pieces:
              if len(a__					) > 1 and piece[-1] == str(''','''					) and piece[-2].isdigit():
                 SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.sp_model.EncodeAsPieces(piece[:-1].replace(a__ ,  ''''''					)					)
                 if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                    if len(cur_pieces[0]					) == 1:
                       SCREAMING_SNAKE_CASE__			:				List[str]       						= cur_pieces[1:]
                    else:
                       SCREAMING_SNAKE_CASE__			:				str       						= cur_pieces[0][1:]
                 cur_pieces.append(piece[-1]					)
                 new_pieces.extend(a__					)
              else:
                 new_pieces.append(a__					)
  
           return new_pieces
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   str:
           return self.sp_model.PieceToId(a__					)
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   Optional[int]:
           return self.sp_model.IdToPiece(a__					)
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   Optional[Any]:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= []
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= ''''''
           SCREAMING_SNAKE_CASE__			:				Any       						= 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(a__					) + token
                 SCREAMING_SNAKE_CASE__			:				List[Any]       						= True
                 SCREAMING_SNAKE_CASE__			:				int       						= []
              else:
                 current_sub_tokens.append(a__					)
                 SCREAMING_SNAKE_CASE__			:				Dict       						= False
           out_string += self.sp_model.decode(a__					)
           return out_string.strip()
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = False ,  a__ = None ,  a__ = True ,  **a__ ,  )       ->   str:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= kwargs.pop('''use_source_tokenizer''' ,  a__					)
  
           SCREAMING_SNAKE_CASE__			:				int       						= self.convert_ids_to_tokens(a__ ,  skip_special_tokens=a__					)
  
           # 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
           SCREAMING_SNAKE_CASE__			:				Dict       						= []
           SCREAMING_SNAKE_CASE__			:				List[str]       						= []
           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(a__					)					)
                    SCREAMING_SNAKE_CASE__			:				List[str]       						= []
                 sub_texts.append(a__					)
              else:
                 current_sub_text.append(a__					)
           if current_sub_text:
              sub_texts.append(self.convert_tokens_to_string(a__					)					)
  
           # Mimic the behavior of the Rust tokenizer:
           # No space after <unk>
           if spaces_between_special_tokens:
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= re.sub(R'''(<unk>) ''' ,  R'''\1''' ,  ''' '''.join(a__					)					)
           else:
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= ''''''.join(a__					)
  
           SCREAMING_SNAKE_CASE__			:				int       						= (
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
              SCREAMING_SNAKE_CASE__			:				str       						= self.clean_up_tokenization(a__					)
              return clean_text
           else:
              return text
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
           SCREAMING_SNAKE_CASE__			:				str       						= [self.sep_token_id]
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [self.cls_token_id]
           if token_ids_a is None:
              return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None ,  a__ = False					)       ->   List[int]:
  
           if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=a__ ,  token_ids_a=a__ ,  already_has_special_tokens=a__					)
  
           if token_ids_a is not None:
              return [1] + ([0] * len(a__					)) + [1] + ([0] * len(a__					)) + [1]
           return [1] + ([0] * len(a__					)) + [1]
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= [self.sep_token_id]
           SCREAMING_SNAKE_CASE__			:				Tuple       						= [self.cls_token_id]
  
           if token_ids_a is None:
              return len(cls + token_ids_a + sep					) * [0]
           return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   Tuple[str]:
           if not os.path.isdir(a__					):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
              return
           SCREAMING_SNAKE_CASE__			:				str       						= os.path.join(
               a__ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
  
           if os.path.abspath(self.vocab_file					) != os.path.abspath(a__					) and os.path.isfile(self.vocab_file					):
              copyfile(self.vocab_file ,  a__					)
           elif not os.path.isfile(self.vocab_file					):
              with open(a__ ,  '''wb'''					) as fi:
                 SCREAMING_SNAKE_CASE__			:				int       						= self.sp_model.serialized_model_proto()
                 fi.write(a__					)
  
           return (out_vocab_file,)
  
  