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()
  lowerCAmelCase_   =   logging.get_logger(__name__)
  
  lowerCAmelCase_   =   "Hello world! cécé herlolip"
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Optional[Any]:
         _snake_case				,    _snake_case      :							Optional[Any]     				=						os.path.split(lowercase_  )
         _snake_case      :							Any     				=						DataaVecTextModel.from_pretrained(
             lowercase_      ,		checkpoint_file=lowercase_  )
         dataavec.eval()  # disable dropout
         _snake_case      :							int     				=						dataavec.models[0]
         _snake_case      :							int     				=						dataavec_model.encoder.sentence_encoder
         _snake_case      :							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:
                _snake_case      :							Union[str, Any]     				=						dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
         print('''Our BERT config:'''      ,		lowercase_  )
  
         _snake_case      :							List[Any]     				=						DataaVecTextForSequenceClassification(lowercase_  ) if classification_head else DataaVecTextForMaskedLM(lowercase_  )
         model.eval()
  
         # Now let's copy all the weights.
         # Embeddings
         _snake_case      :							Tuple     				=						dataavec_sent_encoder.embed_tokens.weight
         _snake_case      :							Optional[int]     				=						dataavec_sent_encoder.embed_positions.weight
         _snake_case      :							Dict     				=						torch.zeros_like(
             model.dataavec_text.embeddings.token_type_embeddings.weight  )  # just zero them out b/c data2vec doesn't use them.
         _snake_case      :							Dict     				=						dataavec_sent_encoder.layernorm_embedding.weight
         _snake_case      :							Optional[Any]     				=						dataavec_sent_encoder.layernorm_embedding.bias
  
         for i in range(config.num_hidden_layers  ):
                # Encoder: start of layer
                _snake_case      :							BertLayer     				=						model.dataavec_text.encoder.layer[i]
                _snake_case      :							TransformerSentenceEncoderLayer     				=						dataavec_sent_encoder.layers[i]
  
                # self attention
                _snake_case      :							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)  )}'''
                )
  
                _snake_case      :							Optional[int]     				=						dataavec_layer.self_attn.q_proj.weight
                _snake_case      :							Union[str, Any]     				=						dataavec_layer.self_attn.q_proj.bias
                _snake_case      :							Union[str, Any]     				=						dataavec_layer.self_attn.k_proj.weight
                _snake_case      :							str     				=						dataavec_layer.self_attn.k_proj.bias
                _snake_case      :							Union[str, Any]     				=						dataavec_layer.self_attn.v_proj.weight
                _snake_case      :							List[Any]     				=						dataavec_layer.self_attn.v_proj.bias
  
                # self-attention output
                _snake_case      :							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}'''
                _snake_case      :							Dict     				=						dataavec_layer.self_attn.out_proj.weight
                _snake_case      :							str     				=						dataavec_layer.self_attn.out_proj.bias
                _snake_case      :							Any     				=						dataavec_layer.self_attn_layer_norm.weight
                _snake_case      :							Optional[Any]     				=						dataavec_layer.self_attn_layer_norm.bias
  
                # intermediate
                _snake_case      :							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}'''
                _snake_case      :							Optional[int]     				=						dataavec_layer.fca.weight
                _snake_case      :							List[str]     				=						dataavec_layer.fca.bias
  
                # output
                _snake_case      :							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}'''
                _snake_case      :							List[Any]     				=						dataavec_layer.fca.weight
                _snake_case      :							Any     				=						dataavec_layer.fca.bias
                _snake_case      :							Optional[Any]     				=						dataavec_layer.final_layer_norm.weight
                _snake_case      :							str     				=						dataavec_layer.final_layer_norm.bias
                # end of layer
  
         if classification_head:
                _snake_case      :							Optional[Any]     				=						dataavec.model.classification_heads['''mnli'''].dense.weight
                _snake_case      :							List[Any]     				=						dataavec.model.classification_heads['''mnli'''].dense.bias
                _snake_case      :							Optional[Any]     				=						dataavec.model.classification_heads['''mnli'''].out_proj.weight
                _snake_case      :							int     				=						dataavec.model.classification_heads['''mnli'''].out_proj.bias
         else:
                # LM Head
                _snake_case      :							Any     				=						dataavec_model.encoder.lm_head.dense.weight
                _snake_case      :							Any     				=						dataavec_model.encoder.lm_head.dense.bias
                _snake_case      :							Optional[int]     				=						dataavec_model.encoder.lm_head.layer_norm.weight
                _snake_case      :							Any     				=						dataavec_model.encoder.lm_head.layer_norm.bias
                _snake_case      :							int     				=						dataavec_model.encoder.lm_head.weight
                _snake_case      :							Tuple     				=						dataavec_model.encoder.lm_head.bias
  
         # Let's check that we get the same results.
         _snake_case      :							torch.Tensor     				=						dataavec.encode(lowercase_  ).unsqueeze(0  )  # batch of size 1
  
         _snake_case      :							Optional[Any]     				=						model(lowercase_  )[0]
         if classification_head:
                _snake_case      :							List[str]     				=						dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(lowercase_  )  )
         else:
                _snake_case      :							Dict     				=						dataavec_model(lowercase_  )[0]
         print(our_output.shape      ,		their_output.shape  )
         _snake_case      :							int     				=						torch.max(torch.abs(our_output - their_output  )  ).item()
         print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
         _snake_case      :							List[Any]     				=						torch.allclose(lowercase_      ,		lowercase_      ,		atol=1E-3  )
         print('''Do both models output the same tensors?'''      ,		'''🔥''' if success else '''💩'''  )
         if not success:
                raise Exception('''Something went wRoNg'''  )
  
         pathlib.Path(lowercase_  ).mkdir(parents=lowercase_      ,		exist_ok=lowercase_  )
         print(f'''Saving model to {pytorch_dump_folder_path}'''  )
         model.save_pretrained(lowercase_  )
  
  
  if __name__ == "__main__":
     lowerCAmelCase_   =   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."
     )
     lowerCAmelCase_   =   parser.parse_args()
     convert_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def 						A_			(  lowercase_  )			->						list:
         if len(lowercase_  ) <= 1:
                return data
         else:
                return [
                    *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                    data[0],
                    *quick_sort([e for e in data[1:] if e > data[0]]  ),
                ]
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  
  
  
  
  
  
  