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()
  __a   				=      logging.get_logger(__name__)
  
  __a   				=      '''Hello world! cécé herlolip'''
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase   ,			lowercase			:   List[Any]					   =		os.path.split(_UpperCamelCase     )
        lowercase			:   str					   =		DataaVecTextModel.from_pretrained(
            _UpperCamelCase,    checkpoint_file=_UpperCamelCase     )
        dataavec.eval()  # disable dropout
        lowercase			:   Any					   =		dataavec.models[0]
        lowercase			:   List[Any]					   =		dataavec_model.encoder.sentence_encoder
        lowercase			:   List[Any]					   =		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:
              lowercase			:   Any					   =		dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
        print('''Our BERT config:''',    _UpperCamelCase     )
  
        lowercase			:   List[Any]					   =		DataaVecTextForSequenceClassification(_UpperCamelCase     ) if classification_head else DataaVecTextForMaskedLM(_UpperCamelCase     )
        model.eval()
  
        # Now let's copy all the weights.
        # Embeddings
        lowercase			:   str					   =		dataavec_sent_encoder.embed_tokens.weight
        lowercase			:   str					   =		dataavec_sent_encoder.embed_positions.weight
        lowercase			:   Optional[Any]					   =		torch.zeros_like(
            model.dataavec_text.embeddings.token_type_embeddings.weight     )  # just zero them out b/c data2vec doesn't use them.
        lowercase			:   str					   =		dataavec_sent_encoder.layernorm_embedding.weight
        lowercase			:   Tuple					   =		dataavec_sent_encoder.layernorm_embedding.bias
  
        for i in range(config.num_hidden_layers     ):
              # Encoder: start of layer
              lowercase			:   BertLayer					   =		model.dataavec_text.encoder.layer[i]
              lowercase			:   TransformerSentenceEncoderLayer					   =		dataavec_sent_encoder.layers[i]
  
              # self attention
              lowercase			:   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)     )}"""
              )
  
              lowercase			:   Dict					   =		dataavec_layer.self_attn.q_proj.weight
              lowercase			:   str					   =		dataavec_layer.self_attn.q_proj.bias
              lowercase			:   List[str]					   =		dataavec_layer.self_attn.k_proj.weight
              lowercase			:   Any					   =		dataavec_layer.self_attn.k_proj.bias
              lowercase			:   Optional[int]					   =		dataavec_layer.self_attn.v_proj.weight
              lowercase			:   List[Any]					   =		dataavec_layer.self_attn.v_proj.bias
  
              # self-attention output
              lowercase			:   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}"""
              lowercase			:   int					   =		dataavec_layer.self_attn.out_proj.weight
              lowercase			:   List[Any]					   =		dataavec_layer.self_attn.out_proj.bias
              lowercase			:   Union[str, Any]					   =		dataavec_layer.self_attn_layer_norm.weight
              lowercase			:   Tuple					   =		dataavec_layer.self_attn_layer_norm.bias
  
              # intermediate
              lowercase			:   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}"""
              lowercase			:   List[Any]					   =		dataavec_layer.fca.weight
              lowercase			:   str					   =		dataavec_layer.fca.bias
  
              # output
              lowercase			:   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}"""
              lowercase			:   Tuple					   =		dataavec_layer.fca.weight
              lowercase			:   Union[str, Any]					   =		dataavec_layer.fca.bias
              lowercase			:   List[Any]					   =		dataavec_layer.final_layer_norm.weight
              lowercase			:   str					   =		dataavec_layer.final_layer_norm.bias
              # end of layer
  
        if classification_head:
              lowercase			:   List[str]					   =		dataavec.model.classification_heads['''mnli'''].dense.weight
              lowercase			:   Optional[int]					   =		dataavec.model.classification_heads['''mnli'''].dense.bias
              lowercase			:   Tuple					   =		dataavec.model.classification_heads['''mnli'''].out_proj.weight
              lowercase			:   Tuple					   =		dataavec.model.classification_heads['''mnli'''].out_proj.bias
        else:
              # LM Head
              lowercase			:   List[str]					   =		dataavec_model.encoder.lm_head.dense.weight
              lowercase			:   List[str]					   =		dataavec_model.encoder.lm_head.dense.bias
              lowercase			:   Any					   =		dataavec_model.encoder.lm_head.layer_norm.weight
              lowercase			:   Tuple					   =		dataavec_model.encoder.lm_head.layer_norm.bias
              lowercase			:   str					   =		dataavec_model.encoder.lm_head.weight
              lowercase			:   str					   =		dataavec_model.encoder.lm_head.bias
  
        # Let's check that we get the same results.
        lowercase			:   torch.Tensor					   =		dataavec.encode(_UpperCamelCase     ).unsqueeze(0     )  # batch of size 1
  
        lowercase			:   Optional[Any]					   =		model(_UpperCamelCase     )[0]
        if classification_head:
              lowercase			:   Tuple					   =		dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(_UpperCamelCase     )     )
        else:
              lowercase			:   Tuple					   =		dataavec_model(_UpperCamelCase     )[0]
        print(our_output.shape,    their_output.shape     )
        lowercase			:   Optional[Any]					   =		torch.max(torch.abs(our_output - their_output     )     ).item()
        print(f"""max_absolute_diff = {max_absolute_diff}"""     )  # ~ 1e-7
        lowercase			:   Optional[Any]					   =		torch.allclose(_UpperCamelCase,    _UpperCamelCase,    atol=1e-3     )
        print('''Do both models output the same tensors?''',    '''🔥''' if success else '''💩'''     )
        if not success:
              raise Exception('''Something went wRoNg'''     )
  
        pathlib.Path(_UpperCamelCase     ).mkdir(parents=_UpperCamelCase,    exist_ok=_UpperCamelCase     )
        print(f"""Saving model to {pytorch_dump_folder_path}"""     )
        model.save_pretrained(_UpperCamelCase     )
  
  
  if __name__ == "__main__":
    __a   				=      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.'''
    )
    __a   				=      parser.parse_args()
    convert_dataavec_checkpoint_to_pytorch(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __a   				=      {'''configuration_yoso''': ['''YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YosoConfig''']}
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __a   				=      [
        '''YOSO_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''YosoForMaskedLM''',
        '''YosoForMultipleChoice''',
        '''YosoForQuestionAnswering''',
        '''YosoForSequenceClassification''',
        '''YosoForTokenClassification''',
        '''YosoLayer''',
        '''YosoModel''',
        '''YosoPreTrainedModel''',
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_yoso import (
          YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
          YosoForMaskedLM,
          YosoForMultipleChoice,
          YosoForQuestionAnswering,
          YosoForSequenceClassification,
          YosoForTokenClassification,
          YosoLayer,
          YosoModel,
          YosoPreTrainedModel,
      )
  
  
  else:
    import sys
  
    __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)
  
  