code: 
  
  
  import argparse
  import json
  
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from timm.models import create_model
  
  from transformers import (
      BeitImageProcessor,
      DataaVecVisionConfig,
      DataaVecVisionForImageClassification,
      DataaVecVisionModel,
  )
  
  
  
  
  def       a__						(		snake_case__				:       str	,      snake_case__				:       Any=False	,      snake_case__				:       Union[str, Any]=False	,      snake_case__				:       Dict="data2vec."    ):
    _UpperCAmelCase		:       Tuple       =    """backbone.""" if is_semantic else """"""
  
    _UpperCAmelCase		:       Tuple       =    []
    for i in range(config.num_hidden_layers    ):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (f'''{prefix}blocks.{i}.norm1.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')    )
      rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')    )
      rename_keys.append(
          (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')    )
      rename_keys.append(
          (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')    )
      rename_keys.append(
          (f'''{prefix}blocks.{i}.norm2.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')    )
      rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')    )
      rename_keys.append(
          (f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')    )
      rename_keys.append(
          (f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')    )
      rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')    )
      rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')    )
  
    # projection layer + position embeddings
    rename_keys.extend(
        [
            (f'''{prefix}cls_token''', f'''{hf_prefix}embeddings.cls_token'''),
            (f'''{prefix}patch_embed.proj.weight''', f'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
            (f'''{prefix}patch_embed.proj.bias''', f'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
        ]    )
  
    if has_lm_head:
      # mask token + shared relative position bias + layernorm
      rename_keys.extend(
          [
              ("""mask_token""", f'''{hf_prefix}embeddings.mask_token'''),
              (
                  """rel_pos_bias.relative_position_bias_table""",
                  f'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
              ),
              (
                  """rel_pos_bias.relative_position_index""",
                  f'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
              ),
              ("""norm.weight""", """layernorm.weight"""),
              ("""norm.bias""", """layernorm.bias"""),
          ]    )
    elif is_semantic:
      # semantic segmentation classification heads
      rename_keys.extend(
          [
              ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
              ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
              ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
              ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
          ]    )
    else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ("""fc_norm.weight""", f'''{hf_prefix}pooler.layernorm.weight'''),
              ("""fc_norm.bias""", f'''{hf_prefix}pooler.layernorm.bias'''),
              ("""head.weight""", """classifier.weight"""),
              ("""head.bias""", """classifier.bias"""),
          ]    )
  
    return rename_keys
  
  
  
  
  def       a__						(		snake_case__				:       Tuple	,      snake_case__				:       str	,      snake_case__				:       Dict=False	,      snake_case__				:       List[str]=False	,      snake_case__				:       Union[str, Any]="data2vec_vision."    ):
    for i in range(config.num_hidden_layers    ):
      _UpperCAmelCase		:       int       =    """backbone.""" if is_semantic else """"""
      # queries, keys and values
      _UpperCAmelCase		:       Any       =    state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''    )
      _UpperCAmelCase		:       Union[str, Any]       =    state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''    )
      _UpperCAmelCase		:       List[str]       =    state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''    )
  
      _UpperCAmelCase		:       Optional[int]       =    in_proj_weight[
          : config.hidden_size, :
      ]
      _UpperCAmelCase		:       Tuple       =    q_bias
      _UpperCAmelCase		:       Union[str, Any]       =    in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      _UpperCAmelCase		:       Optional[Any]       =    in_proj_weight[
          -config.hidden_size :, :
      ]
      _UpperCAmelCase		:       Any       =    v_bias
  
      # gamma_1 and gamma_2
      # we call them lambda because otherwise they are renamed when using .from_pretrained
      _UpperCAmelCase		:       List[Any]       =    state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''    )
      _UpperCAmelCase		:       Optional[Any]       =    state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''    )
  
      _UpperCAmelCase		:       Any       =    gamma_a
      _UpperCAmelCase		:       Optional[Any]       =    gamma_a
  
      # relative_position bias table + index
      if not has_lm_head:
        # each layer has its own relative position bias
        _UpperCAmelCase		:       List[Any]       =    state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''    )
        _UpperCAmelCase		:       Optional[Any]       =    state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''    )
  
        _UpperCAmelCase		:       str       =    table
        _UpperCAmelCase		:       str       =    index
  
  
  
  
  def       a__						(		):
    _UpperCAmelCase		:       Union[str, Any]       =    argparse.ArgumentParser(
        """Convert Data2VecVision to HF for image classification and pretraining"""	,      add_help=snake_case__    )
    parser.add_argument("""--hf_checkpoint_name"""	,      type=snake_case__    )
    parser.add_argument("""--input_size"""	,      default=224	,      type=snake_case__	,      help="""images input size"""    )
    parser.add_argument("""--beit_checkpoint"""	,      default=""""""	,      help="""beit checkpoint"""    )
  
    return parser.parse_args()
  
  
  
  
  def       a__						(		snake_case__				:       List[str]	,      snake_case__				:       Any	,      snake_case__				:       Dict    ):
    def load_state_dict(snake_case__				:       List[Any]	,      snake_case__				:       Any	,      snake_case__				:       Optional[int]=""	,      snake_case__				:       List[Any]="relative_position_index"    ):
      _UpperCAmelCase		:       Optional[int]       =    []
      _UpperCAmelCase		:       List[Any]       =    []
      _UpperCAmelCase		:       Dict       =    []
      # copy state_dict so _load_from_state_dict can modify it
      _UpperCAmelCase		:       Optional[int]       =    getattr(snake_case__	,      """_metadata"""	,      snake_case__    )
      _UpperCAmelCase		:       Dict       =    state_dict.copy()
      if metadata is not None:
        _UpperCAmelCase		:       Optional[int]       =    metadata
  
      def load(snake_case__				:       Dict	,      snake_case__				:       List[Any]=""    ):
        _UpperCAmelCase		:       Tuple       =    {} if metadata is None else metadata.get(prefix[:-1]	,      {}    )
        module._load_from_state_dict(
            snake_case__	,      snake_case__	,      snake_case__	,      snake_case__	,      snake_case__	,      snake_case__	,      snake_case__    )
        for name, child in module._modules.items():
          if child is not None:
            load(snake_case__	,      prefix + name + """."""    )
  
      load(snake_case__	,      prefix=snake_case__    )
  
      _UpperCAmelCase		:       Union[str, Any]       =    []
      _UpperCAmelCase		:       Optional[Any]       =    []
      for key in missing_keys:
        _UpperCAmelCase		:       str       =    True
        for ignore_key in ignore_missing.split("""|"""    ):
          if ignore_key in key:
            _UpperCAmelCase		:       Optional[int]       =    False
            break
        if keep_flag:
          warn_missing_keys.append(snake_case__    )
        else:
          ignore_missing_keys.append(snake_case__    )
  
      _UpperCAmelCase		:       str       =    warn_missing_keys
  
      if len(snake_case__    ) > 0:
        print(
            """Weights of {} not initialized from pretrained model: {}""".format(
                model.__class__.__name__	,      snake_case__    )    )
      if len(snake_case__    ) > 0:
        print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__	,      snake_case__    )    )
      if len(snake_case__    ) > 0:
        print(
            """Ignored weights of {} not initialized from pretrained model: {}""".format(
                model.__class__.__name__	,      snake_case__    )    )
      if len(snake_case__    ) > 0:
        print("""\n""".join(snake_case__    )    )
  
    _UpperCAmelCase		:       List[Any]       =    {
        """pretrained""": False,
        """use_shared_rel_pos_bias""": True,
        """use_abs_pos_emb""": False,
        """init_values""": 0.1,
    }
  
    if is_finetuned:
      model_kwargs.update(
          {
              """num_classes""": 1000,
              """use_mean_pooling""": True,
              """init_scale""": 0.001,
              """use_rel_pos_bias""": True,
          }    )
  
    _UpperCAmelCase		:       Dict       =    create_model(
        """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""	,      **snake_case__	,      )
    _UpperCAmelCase		:       Optional[int]       =    model.patch_embed.patch_size
    _UpperCAmelCase		:       Optional[int]       =    (args.input_size // patch_size[0], args.input_size // patch_size[1])
    _UpperCAmelCase		:       Union[str, Any]       =    torch.load(args.beit_checkpoint	,      map_location="""cpu"""    )
  
    print(f'''Load ckpt from {args.beit_checkpoint}'''    )
    _UpperCAmelCase		:       Any       =    None
    for model_key in ("model", "module"):
      if model_key in checkpoint:
        _UpperCAmelCase		:       List[str]       =    checkpoint[model_key]
        print(f'''Load state_dict by model_key = {model_key}'''    )
        break
  
    _UpperCAmelCase		:       List[Any]       =    list(checkpoint_model.keys()    )
    for key in all_keys:
      if "relative_position_index" in key:
        checkpoint_model.pop(snake_case__    )
  
      if "relative_position_bias_table" in key:
        _UpperCAmelCase		:       List[Any]       =    checkpoint_model[key]
        _UpperCAmelCase,_UpperCAmelCase		:       Tuple       =    rel_pos_bias.size()
        _UpperCAmelCase,_UpperCAmelCase		:       List[str]       =    model.state_dict()[key].size()
        _UpperCAmelCase		:       Tuple       =    model.patch_embed.patch_shape
        if dst_patch_shape[0] != dst_patch_shape[1]:
          raise NotImplementedError()
  
    load_state_dict(snake_case__	,      snake_case__	,      prefix=""""""    )
  
    return model
  
  
  
  
  def       a__						(		):
    _UpperCAmelCase		:       List[Any]       =    get_args()
  
    _UpperCAmelCase		:       List[Any]       =    """ft1k""" in args.hf_checkpoint_name
    _UpperCAmelCase		:       List[str]       =    """large""" in args.hf_checkpoint_name
  
    if is_finetuned:
      # To convert Beit's data2vec_vision to HF you need to copy
      # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
      # into this folder.
      import modeling_finetune  # noqa: F401
    else:
      # To convert Beit's data2vec_vision to HF you need to copy
      # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
      # into this folder
      # IMPORTANT: Note that for now we've only converted the down-stream
      # model and not the full pretrained model. This means for the integration
      # test you need to add a `return x` after the following line:
      # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
      # to make the integration test pass.
      import modeling_cyclical  # noqa: F401
  
    # 1. Create model config
    _UpperCAmelCase		:       str       =    DataaVecVisionConfig()
    if is_finetuned:
      _UpperCAmelCase		:       Union[str, Any]       =    True
      _UpperCAmelCase		:       Optional[Any]       =    False
      _UpperCAmelCase		:       Dict       =    True
      _UpperCAmelCase		:       List[Any]       =    1000
  
      _UpperCAmelCase		:       str       =    """huggingface/label-files"""
      _UpperCAmelCase		:       Any       =    """imagenet-1k-id2label.json"""
      _UpperCAmelCase		:       str       =    json.load(open(hf_hub_download(snake_case__	,      snake_case__	,      repo_type="""dataset"""    )	,      """r"""    )    )
      _UpperCAmelCase		:       Any       =    {int(snake_case__    ): v for k, v in idalabel.items()}
      _UpperCAmelCase		:       Optional[int]       =    idalabel
      _UpperCAmelCase		:       Any       =    {v: k for k, v in idalabel.items()}
    else:
      _UpperCAmelCase		:       int       =    False
      _UpperCAmelCase		:       Dict       =    True
      _UpperCAmelCase		:       List[Any]       =    False
  
    if is_large:
      _UpperCAmelCase		:       Dict       =    1024
      _UpperCAmelCase		:       Optional[int]       =    4096
      _UpperCAmelCase		:       Dict       =    24
      _UpperCAmelCase		:       Optional[Any]       =    16
  
    # 2. Load Beit model
    _UpperCAmelCase		:       List[str]       =    load_beit_model(snake_case__	,      snake_case__	,      snake_case__    )
    orig_model.eval()
  
    # 3. Forward Beit model
    _UpperCAmelCase		:       Any       =    BeitImageProcessor(size=config.image_size	,      do_center_crop=snake_case__    )
    _UpperCAmelCase		:       Union[str, Any]       =    Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""    )
    _UpperCAmelCase		:       Union[str, Any]       =    image_processor(images=snake_case__	,      return_tensors="""pt"""    )
    _UpperCAmelCase		:       List[Any]       =    encoding["""pixel_values"""]
  
    _UpperCAmelCase		:       int       =    (pixel_values,) if is_finetuned else (pixel_values, None)
    with torch.no_grad():
      _UpperCAmelCase		:       List[str]       =    orig_model(*snake_case__    )
  
    # 4. Load HF Data2VecVision model
    if is_finetuned:
      _UpperCAmelCase		:       Optional[Any]       =    DataaVecVisionForImageClassification(snake_case__    )
      hf_model.eval()
      _UpperCAmelCase		:       Dict       =    False
      _UpperCAmelCase		:       Any       =    """data2vec_vision."""
    else:
      _UpperCAmelCase		:       Union[str, Any]       =    DataaVecVisionModel(snake_case__    )
      hf_model.eval()
      _UpperCAmelCase		:       List[str]       =    True
      _UpperCAmelCase		:       Tuple       =    """"""
  
    _UpperCAmelCase		:       Optional[Any]       =    create_rename_keys(snake_case__	,      hf_prefix=snake_case__	,      has_lm_head=snake_case__    )
    _UpperCAmelCase		:       List[str]       =    orig_model.state_dict()
    for src, dest in rename_keys:
      _UpperCAmelCase		:       str       =    state_dict.pop(snake_case__    )
      _UpperCAmelCase		:       List[Any]       =    val
  
    read_in_q_k_v(snake_case__	,      snake_case__	,      hf_prefix=snake_case__	,      has_lm_head=snake_case__    )
    _UpperCAmelCase,_UpperCAmelCase		:       Union[str, Any]       =    hf_model.load_state_dict(snake_case__	,      strict=snake_case__    )
    print("""HF missing"""	,      snake_case__    )
    print("""HF unexpected_keys"""	,      snake_case__    )
  
    # 5. Forward HF Data2VecVision model
    with torch.no_grad():
      _UpperCAmelCase		:       List[str]       =    hf_model(snake_case__    )
  
    _UpperCAmelCase		:       List[Any]       =    hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state
  
    # 6. Compare
    _UpperCAmelCase		:       Union[str, Any]       =    torch.max(torch.abs(hf_output - orig_model_output    )    ).item()
  
    print(f'''max_absolute_diff = {max_absolute_diff}'''    )
    _UpperCAmelCase		:       Tuple       =    torch.allclose(snake_case__	,      snake_case__	,      atol=1e-3    )
    print("""Do both models output the same tensors?"""	,      """🔥""" if success else """💩"""    )
    if not success:
      raise Exception("""Something went wRoNg"""    )
  
    # 7. Save
    print(f'''Saving to {args.hf_checkpoint_name}'''    )
    hf_model.save_pretrained(args.hf_checkpoint_name    )
    image_processor.save_pretrained(args.hf_checkpoint_name    )
  
  
  if __name__ == "__main__":
   main()
   # Run the following to convert checkpoints
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./pretrained_base.pt \
   #          --hf_checkpoint_name "./data2vec-vision-base"
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./finetuned_base.pt \
   #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./pretrained_large.pt \
   #          --hf_checkpoint_name "./data2vec-vision-large"
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./finetuned_large.pt \
   #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"
  
  
  
  
  
  
style_context: 
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
   from .tokenization_xlm_roberta import XLMRobertaTokenizer
  else:
   SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    None
  
  
  SCREAMING_SNAKE_CASE__				:    Dict         =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__				:    List[Any]         =    {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
  
  SCREAMING_SNAKE_CASE__				:    Any         =    {
      'vocab_file': {
          'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
          'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
          'xlm-roberta-large-finetuned-conll02-dutch': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
          ),
          'xlm-roberta-large-finetuned-conll02-spanish': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
          ),
          'xlm-roberta-large-finetuned-conll03-english': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
          ),
          'xlm-roberta-large-finetuned-conll03-german': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
          ),
      },
      'tokenizer_file': {
          'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
          'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
          'xlm-roberta-large-finetuned-conll02-dutch': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
          ),
          'xlm-roberta-large-finetuned-conll02-spanish': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
          ),
          'xlm-roberta-large-finetuned-conll03-english': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
          ),
          'xlm-roberta-large-finetuned-conll03-german': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
          ),
      },
  }
  
  SCREAMING_SNAKE_CASE__				:    Optional[int]         =    {
      'xlm-roberta-base': 512,
      'xlm-roberta-large': 512,
      'xlm-roberta-large-finetuned-conll02-dutch': 512,
      'xlm-roberta-large-finetuned-conll02-spanish': 512,
      'xlm-roberta-large-finetuned-conll03-english': 512,
      'xlm-roberta-large-finetuned-conll03-german': 512,
  }
  
  
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
       __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    				=						XLMRobertaTokenizer
  
  
       def __init__(     self       ,				A_=None       ,				A_=None       ,				A_="<s>"       ,				A_="</s>"       ,				A_="</s>"       ,				A_="<s>"       ,				A_="<unk>"       ,				A_="<pad>"       ,				A_="<mask>"       ,				**A_       ,				):
         # Mask token behave like a normal word, i.e. include the space before it
         _UpperCAmelCase		:       int       =    AddedToken(A_       ,				lstrip=A_       ,				rstrip=A_						) if isinstance(A_       ,				A_						) else mask_token
  
         super().__init__(
             A_       ,				tokenizer_file=A_       ,				bos_token=A_       ,				eos_token=A_       ,				sep_token=A_       ,				cls_token=A_       ,				unk_token=A_       ,				pad_token=A_       ,				mask_token=A_       ,				**A_       ,				)
  
         _UpperCAmelCase		:       Union[str, Any]       =    vocab_file
         _UpperCAmelCase		:       List[str]       =    False if not self.vocab_file else True
  
  
       def 					__snake_case(     self       ,				A_       ,				A_ = None						):
  
         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _UpperCAmelCase		:       Optional[Any]       =    [self.cls_token_id]
         _UpperCAmelCase		:       Union[str, Any]       =    [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
       def 					__snake_case(     self       ,				A_       ,				A_ = None						):
         _UpperCAmelCase		:       Tuple       =    [self.sep_token_id]
         _UpperCAmelCase		:       Optional[int]       =    [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 + sep + token_ids_a + sep						) * [0]
  
  
  
  
       def 					__snake_case(     self       ,				A_       ,				A_ = 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(A_						):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''						)
           return
         _UpperCAmelCase		:       List[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_						):
           copyfile(self.vocab_file       ,				A_						)
  
         return (out_vocab_file,)
  
  
  
  
  
  