code: 
  
  
  
  
  
  
  from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  
  from ... import PreTrainedTokenizer, TensorType, is_torch_available
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast, PatchingSpec
  from ...utils import logging
  
  
  __A  							=  logging.get_logger(__name__)
  
  __A  							=  {
      'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
      'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
      'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
      'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
      'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
  }
  
  
  
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Union[str, Any]   =   'gpt2'
      lowerCamelCase  :       Any   =   ['past_key_values']
      lowerCamelCase  :       Tuple   =   {
          'hidden_size': 'n_embd',
          'max_position_embeddings': 'n_positions',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }
  
  
  
      def __init__(							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    Dict=50257   ,    __SCREAMING_SNAKE_CASE			:    Dict=1024   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=768   ,    __SCREAMING_SNAKE_CASE			:    Tuple=12   ,    __SCREAMING_SNAKE_CASE			:    Tuple=12   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=None   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]="gelu_new"   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0.1   ,    __SCREAMING_SNAKE_CASE			:    int=0.1   ,    __SCREAMING_SNAKE_CASE			:    Any=0.1   ,    __SCREAMING_SNAKE_CASE			:    Tuple=1e-5   ,    __SCREAMING_SNAKE_CASE			:    str=0.02   ,    __SCREAMING_SNAKE_CASE			:    int="cls_index"   ,    __SCREAMING_SNAKE_CASE			:    Dict=True   ,    __SCREAMING_SNAKE_CASE			:    str=None   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0.1   ,    __SCREAMING_SNAKE_CASE			:    str=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=50256   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=50256   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=False   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=False   ,    **__SCREAMING_SNAKE_CASE			:    Dict   ,    )			->       Union[str, Any]:
          __UpperCAmelCase    							=vocab_size
          __UpperCAmelCase    							=n_positions
          __UpperCAmelCase    							=n_embd
          __UpperCAmelCase    							=n_layer
          __UpperCAmelCase    							=n_head
          __UpperCAmelCase    							=n_inner
          __UpperCAmelCase    							=activation_function
          __UpperCAmelCase    							=resid_pdrop
          __UpperCAmelCase    							=embd_pdrop
          __UpperCAmelCase    							=attn_pdrop
          __UpperCAmelCase    							=layer_norm_epsilon
          __UpperCAmelCase    							=initializer_range
          __UpperCAmelCase    							=summary_type
          __UpperCAmelCase    							=summary_use_proj
          __UpperCAmelCase    							=summary_activation
          __UpperCAmelCase    							=summary_first_dropout
          __UpperCAmelCase    							=summary_proj_to_labels
          __UpperCAmelCase    							=scale_attn_weights
          __UpperCAmelCase    							=use_cache
          __UpperCAmelCase    							=scale_attn_by_inverse_layer_idx
          __UpperCAmelCase    							=reorder_and_upcast_attn
  
          __UpperCAmelCase    							=bos_token_id
          __UpperCAmelCase    							=eos_token_id
  
          super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE   ,    eos_token_id=__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			)
  
  
  
  
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      def __init__(							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    List[str] = "default"   ,    __SCREAMING_SNAKE_CASE			:    Tuple = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[int] = False   ,    )			->       str:
          super().__init__(__SCREAMING_SNAKE_CASE   ,    task=__SCREAMING_SNAKE_CASE   ,    patching_specs=__SCREAMING_SNAKE_CASE   ,    use_past=__SCREAMING_SNAKE_CASE			)
          if not getattr(self._config   ,    """pad_token_id"""   ,    __SCREAMING_SNAKE_CASE			):
              # TODO: how to do that better?
              __UpperCAmelCase    							=0
  
  
  
      @property
      def _a       (							self			:    List[str]			)			->       Mapping[str, Mapping[int, str]]:
          __UpperCAmelCase    							=OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}			)
          if self.use_past:
              self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE   ,    direction="""inputs"""			)
              __UpperCAmelCase    							={0: '''batch''', 1: '''past_sequence + sequence'''}
          else:
              __UpperCAmelCase    							={0: '''batch''', 1: '''sequence'''}
  
          return common_inputs
  
  
  
      @property
      def _a       (							self			:    Optional[Any]			)			->       int:
          return self._config.n_layer
  
  
  
      @property
      def _a       (							self			:    int			)			->       int:
          return self._config.n_head
  
  
  
      def _a       (							self			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    str = -1   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any] = -1   ,    __SCREAMING_SNAKE_CASE			:    Dict = False   ,    __SCREAMING_SNAKE_CASE			:    Tuple = None   ,    )			->       Mapping[str, Any]:
          __UpperCAmelCase    							=super(__SCREAMING_SNAKE_CASE   ,    self			).generate_dummy_inputs(
              __SCREAMING_SNAKE_CASE   ,    batch_size=__SCREAMING_SNAKE_CASE   ,    seq_length=__SCREAMING_SNAKE_CASE   ,    is_pair=__SCREAMING_SNAKE_CASE   ,    framework=__SCREAMING_SNAKE_CASE			)
  
          # We need to order the input in the way they appears in the forward()
          __UpperCAmelCase    							=OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}			)
  
          # Need to add the past_keys
          if self.use_past:
              if not is_torch_available():
                  raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""			)
              else:
                  import torch
  
                  __UpperCAmelCase    							=common_inputs['''input_ids'''].shape
                  # Not using the same length for past_key_values
                  __UpperCAmelCase    							=seqlen + 2
                  __UpperCAmelCase    							=(
                      batch,
                      self.num_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // self.num_attention_heads,
                  )
                  __UpperCAmelCase    							=[
                      (torch.zeros(__SCREAMING_SNAKE_CASE			), torch.zeros(__SCREAMING_SNAKE_CASE			)) for _ in range(self.num_layers			)
                  ]
  
          __UpperCAmelCase    							=common_inputs['''attention_mask''']
          if self.use_past:
              __UpperCAmelCase    							=ordered_inputs['''attention_mask'''].dtype
              __UpperCAmelCase    							=torch.cat(
                  [ordered_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    dtype=__SCREAMING_SNAKE_CASE			)]   ,    dim=1			)
  
          return ordered_inputs
  
  
  
      @property
      def _a       (							self			:    Union[str, Any]			)			->       int:
          return 13
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  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__  (				A_:	str     ,			A_:	str     ,			A_:	bool		)  ->							str:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase							,   __UpperCAmelCase    							=os.path.split(A_		)
         __UpperCAmelCase    							=DataaVecTextModel.from_pretrained(
             A_     ,			checkpoint_file=A_		)
         dataavec.eval()  # disable dropout
         __UpperCAmelCase    							=dataavec.models[0]
         __UpperCAmelCase    							=dataavec_model.encoder.sentence_encoder
         __UpperCAmelCase    							=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    							=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
         print("""Our BERT config:"""     ,			A_		)
  
         __UpperCAmelCase    							=DataaVecTextForSequenceClassification(A_		) if classification_head else DataaVecTextForMaskedLM(A_		)
         model.eval()
  
         # Now let's copy all the weights.
         # Embeddings
         __UpperCAmelCase    							=dataavec_sent_encoder.embed_tokens.weight
         __UpperCAmelCase    							=dataavec_sent_encoder.embed_positions.weight
         __UpperCAmelCase    							=torch.zeros_like(
             model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
         __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.weight
         __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.bias
  
         for i in range(config.num_hidden_layers		):
                # Encoder: start of layer
                __UpperCAmelCase    							=model.dataavec_text.encoder.layer[i]
                __UpperCAmelCase    							=dataavec_sent_encoder.layers[i]
  
                # self attention
                __UpperCAmelCase    							=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    							=dataavec_layer.self_attn.q_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.bias
  
                # self-attention output
                __UpperCAmelCase    							=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    							=dataavec_layer.self_attn.out_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.bias
  
                # intermediate
                __UpperCAmelCase    							=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    							=dataavec_layer.fca.weight
                __UpperCAmelCase    							=dataavec_layer.fca.bias
  
                # output
                __UpperCAmelCase    							=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    							=dataavec_layer.fca.weight
                __UpperCAmelCase    							=dataavec_layer.fca.bias
                __UpperCAmelCase    							=dataavec_layer.final_layer_norm.weight
                __UpperCAmelCase    							=dataavec_layer.final_layer_norm.bias
                # end of layer
  
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].dense.weight
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].dense.bias
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.weight
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.bias
         else:
                # LM Head
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.dense.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.dense.bias
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.layer_norm.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.layer_norm.bias
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.bias
  
         # Let's check that we get the same results.
         __UpperCAmelCase    							=dataavec.encode(A_		).unsqueeze(0		)  # batch of size 1
  
         __UpperCAmelCase    							=model(A_		)[0]
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(A_		)		)
         else:
                __UpperCAmelCase    							=dataavec_model(A_		)[0]
         print(our_output.shape     ,			their_output.shape		)
         __UpperCAmelCase    							=torch.max(torch.abs(our_output - their_output		)		).item()
         print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
         __UpperCAmelCase    							=torch.allclose(A_     ,			A_     ,			atol=1e-3		)
         print("""Do both models output the same tensors?"""     ,			"""🔥""" if success else """💩"""		)
         if not success:
                raise Exception("""Something went wRoNg"""		)
  
         pathlib.Path(A_		).mkdir(parents=A_     ,			exist_ok=A_		)
         print(F'''Saving model to {pytorch_dump_folder_path}'''		)
         model.save_pretrained(A_		)
  
  
  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
     )
  
  
  
  
  