code: 
  
  
  import argparse
  import os
  import pathlib
  
  import fairseq
  import torch
  from fairseq.modules import TransformerSentenceEncoderLayer
  from packaging import version
  
  from transformers import (
      DataaVecTextConfig,
      DataaVecTextForMaskedLM,
      DataaVecTextForSequenceClassification,
      DataaVecTextModel,
  )
  from transformers.models.bert.modeling_bert import (
      BertIntermediate,
      BertLayer,
      BertOutput,
      BertSelfAttention,
      BertSelfOutput,
  )
  
  # IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
  # File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
  from transformers.utils import logging
  
  
  if version.parse(fairseq.__version__) < version.parse('0.9.0'):
   raise Exception('requires fairseq >= 0.9.0')
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE__				:    Any         =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__				:    List[Any]         =    'Hello world! cécé herlolip'
  
  
  
  
  def       a__						(		snake_case__				:       str	,      snake_case__				:       str	,      snake_case__				:       bool    ):
    _UpperCAmelCase,_UpperCAmelCase		:       List[str]       =    os.path.split(snake_case__    )
    _UpperCAmelCase		:       Any       =    DataaVecTextModel.from_pretrained(
        snake_case__	,      checkpoint_file=snake_case__    )
    dataavec.eval()  # disable dropout
    _UpperCAmelCase		:       Any       =    dataavec.models[0]
    _UpperCAmelCase		:       Optional[int]       =    dataavec_model.encoder.sentence_encoder
    _UpperCAmelCase		:       int       =    DataaVecTextConfig(
        vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings	,      hidden_size=dataavec_model.args.encoder_embed_dim	,      num_hidden_layers=dataavec_model.args.encoder_layers	,      num_attention_heads=dataavec_model.args.encoder_attention_heads	,      intermediate_size=dataavec_model.args.encoder_ffn_embed_dim	,      max_position_embeddings=514	,      type_vocab_size=1	,      layer_norm_eps=1e-5	,      )
    if classification_head:
      _UpperCAmelCase		:       Optional[Any]       =    dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
    print("""Our BERT config:"""	,      snake_case__    )
  
    _UpperCAmelCase		:       int       =    DataaVecTextForSequenceClassification(snake_case__    ) if classification_head else DataaVecTextForMaskedLM(snake_case__    )
    model.eval()
  
    # Now let's copy all the weights.
    # Embeddings
    _UpperCAmelCase		:       List[str]       =    dataavec_sent_encoder.embed_tokens.weight
    _UpperCAmelCase		:       int       =    dataavec_sent_encoder.embed_positions.weight
    _UpperCAmelCase		:       int       =    torch.zeros_like(
        model.dataavec_text.embeddings.token_type_embeddings.weight    )  # just zero them out b/c data2vec doesn't use them.
    _UpperCAmelCase		:       Union[str, Any]       =    dataavec_sent_encoder.layernorm_embedding.weight
    _UpperCAmelCase		:       Optional[Any]       =    dataavec_sent_encoder.layernorm_embedding.bias
  
    for i in range(config.num_hidden_layers    ):
      # Encoder: start of layer
      _UpperCAmelCase		:       BertLayer       =    model.dataavec_text.encoder.layer[i]
      _UpperCAmelCase		:       TransformerSentenceEncoderLayer       =    dataavec_sent_encoder.layers[i]
  
      # self attention
      _UpperCAmelCase		:       BertSelfAttention       =    layer.attention.self
      assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
          (config.hidden_size, config.hidden_size)    ), (
          "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
          f''' {torch.Size((config.hidden_size, config.hidden_size)    )}'''
      )
      assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
          (config.hidden_size, config.hidden_size)    ), (
          "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
          f''' {torch.Size((config.hidden_size, config.hidden_size)    )}'''
      )
      assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
          (config.hidden_size, config.hidden_size)    ), (
          "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
          f''' {torch.Size((config.hidden_size, config.hidden_size)    )}'''
      )
  
      _UpperCAmelCase		:       List[str]       =    dataavec_layer.self_attn.q_proj.weight
      _UpperCAmelCase		:       List[str]       =    dataavec_layer.self_attn.q_proj.bias
      _UpperCAmelCase		:       List[str]       =    dataavec_layer.self_attn.k_proj.weight
      _UpperCAmelCase		:       int       =    dataavec_layer.self_attn.k_proj.bias
      _UpperCAmelCase		:       str       =    dataavec_layer.self_attn.v_proj.weight
      _UpperCAmelCase		:       Union[str, Any]       =    dataavec_layer.self_attn.v_proj.bias
  
      # self-attention output
      _UpperCAmelCase		:       BertSelfOutput       =    layer.attention.output
      assert (
          self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
      ), f'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
      _UpperCAmelCase		:       int       =    dataavec_layer.self_attn.out_proj.weight
      _UpperCAmelCase		:       Any       =    dataavec_layer.self_attn.out_proj.bias
      _UpperCAmelCase		:       Dict       =    dataavec_layer.self_attn_layer_norm.weight
      _UpperCAmelCase		:       str       =    dataavec_layer.self_attn_layer_norm.bias
  
      # intermediate
      _UpperCAmelCase		:       BertIntermediate       =    layer.intermediate
      assert (
          intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
      ), f'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
      _UpperCAmelCase		:       Any       =    dataavec_layer.fca.weight
      _UpperCAmelCase		:       Optional[int]       =    dataavec_layer.fca.bias
  
      # output
      _UpperCAmelCase		:       BertOutput       =    layer.output
      assert (
          bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
      ), f'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
      _UpperCAmelCase		:       Union[str, Any]       =    dataavec_layer.fca.weight
      _UpperCAmelCase		:       List[str]       =    dataavec_layer.fca.bias
      _UpperCAmelCase		:       Union[str, Any]       =    dataavec_layer.final_layer_norm.weight
      _UpperCAmelCase		:       Any       =    dataavec_layer.final_layer_norm.bias
      # end of layer
  
    if classification_head:
      _UpperCAmelCase		:       str       =    dataavec.model.classification_heads["""mnli"""].dense.weight
      _UpperCAmelCase		:       Optional[Any]       =    dataavec.model.classification_heads["""mnli"""].dense.bias
      _UpperCAmelCase		:       Dict       =    dataavec.model.classification_heads["""mnli"""].out_proj.weight
      _UpperCAmelCase		:       Optional[int]       =    dataavec.model.classification_heads["""mnli"""].out_proj.bias
    else:
      # LM Head
      _UpperCAmelCase		:       int       =    dataavec_model.encoder.lm_head.dense.weight
      _UpperCAmelCase		:       int       =    dataavec_model.encoder.lm_head.dense.bias
      _UpperCAmelCase		:       Optional[int]       =    dataavec_model.encoder.lm_head.layer_norm.weight
      _UpperCAmelCase		:       List[str]       =    dataavec_model.encoder.lm_head.layer_norm.bias
      _UpperCAmelCase		:       Union[str, Any]       =    dataavec_model.encoder.lm_head.weight
      _UpperCAmelCase		:       Any       =    dataavec_model.encoder.lm_head.bias
  
    # Let's check that we get the same results.
    _UpperCAmelCase		:       torch.Tensor       =    dataavec.encode(snake_case__    ).unsqueeze(0    )  # batch of size 1
  
    _UpperCAmelCase		:       Dict       =    model(snake_case__    )[0]
    if classification_head:
      _UpperCAmelCase		:       Tuple       =    dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(snake_case__    )    )
    else:
      _UpperCAmelCase		:       Union[str, Any]       =    dataavec_model(snake_case__    )[0]
    print(our_output.shape	,      their_output.shape    )
    _UpperCAmelCase		:       Dict       =    torch.max(torch.abs(our_output - their_output    )    ).item()
    print(f'''max_absolute_diff = {max_absolute_diff}'''    )  # ~ 1e-7
    _UpperCAmelCase		:       Optional[int]       =    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"""    )
  
    pathlib.Path(snake_case__    ).mkdir(parents=snake_case__	,      exist_ok=snake_case__    )
    print(f'''Saving model to {pytorch_dump_folder_path}'''    )
    model.save_pretrained(snake_case__    )
  
  
  if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__				:    Any         =    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--classification_head', action='store_true', help='Whether to convert a final classification head.'
   )
   SCREAMING_SNAKE_CASE__				:    int         =    parser.parse_args()
   convert_dataavec_checkpoint_to_pytorch(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )
  
  
  
  
  
  
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,)
  
  
  
  
  
  