code: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import pathlib
  
  import fairseq
  import torch
  from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
  from fairseq.modules import TransformerSentenceEncoderLayer
  from packaging import version
  
  from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
  from transformers.models.bert.modeling_bert import (
      BertIntermediate,
      BertLayer,
      BertOutput,
      BertSelfAttention,
      BertSelfOutput,
  )
  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					=						logging.get_logger(__name__)
  
  __SCREAMING_SNAKE_CASE					=						"""Hello world! cécé herlolip"""
  
  
  
  def 						__a      (  a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				FairseqRobertaModel.from_pretrained(a					)
         roberta.eval()  # disable dropout
         _a          =				roberta.model.encoder.sentence_encoder
         _a          =				RobertaConfig(
             vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,		hidden_size=roberta.args.encoder_embed_dim,		num_hidden_layers=roberta.args.encoder_layers,		num_attention_heads=roberta.args.encoder_attention_heads,		intermediate_size=roberta.args.encoder_ffn_embed_dim,		max_position_embeddings=5_1_4,		type_vocab_size=1,		layer_norm_eps=1e-5,		)
         if classification_head:
                _a          =				roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
         print("Our BERT config:",		a					)
  
         _a          =				RobertaForSequenceClassification(a					) if classification_head else RobertaForMaskedLM(a					)
         model.eval()
  
         # Now let's copy all the weights.
         # Embeddings
         _a          =				roberta_sent_encoder.embed_tokens.weight
         _a          =				roberta_sent_encoder.embed_positions.weight
         _a          =				torch.zeros_like(
             model.roberta.embeddings.token_type_embeddings.weight					)  # just zero them out b/c RoBERTa doesn't use them.
         _a          =				roberta_sent_encoder.emb_layer_norm.weight
         _a          =				roberta_sent_encoder.emb_layer_norm.bias
  
         for i in range(config.num_hidden_layers					):
                # Encoder: start of layer
                _a          =				model.roberta.encoder.layer[i]
                _a          =				roberta_sent_encoder.layers[i]
  
                # self attention
                _a          =				layer.attention.self
                assert (
                    roberta_layer.self_attn.k_proj.weight.data.shape
                    == roberta_layer.self_attn.q_proj.weight.data.shape
                    == roberta_layer.self_attn.v_proj.weight.data.shape
                    == torch.Size((config.hidden_size, config.hidden_size)					)
                )
  
                _a          =				roberta_layer.self_attn.q_proj.weight
                _a          =				roberta_layer.self_attn.q_proj.bias
                _a          =				roberta_layer.self_attn.k_proj.weight
                _a          =				roberta_layer.self_attn.k_proj.bias
                _a          =				roberta_layer.self_attn.v_proj.weight
                _a          =				roberta_layer.self_attn.v_proj.bias
  
                # self-attention output
                _a          =				layer.attention.output
                assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
                _a          =				roberta_layer.self_attn.out_proj.weight
                _a          =				roberta_layer.self_attn.out_proj.bias
                _a          =				roberta_layer.self_attn_layer_norm.weight
                _a          =				roberta_layer.self_attn_layer_norm.bias
  
                # intermediate
                _a          =				layer.intermediate
                assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
                _a          =				roberta_layer.fca.weight
                _a          =				roberta_layer.fca.bias
  
                # output
                _a          =				layer.output
                assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
                _a          =				roberta_layer.fca.weight
                _a          =				roberta_layer.fca.bias
                _a          =				roberta_layer.final_layer_norm.weight
                _a          =				roberta_layer.final_layer_norm.bias
                # end of layer
  
         if classification_head:
                _a          =				roberta.model.classification_heads["mnli"].dense.weight
                _a          =				roberta.model.classification_heads["mnli"].dense.bias
                _a          =				roberta.model.classification_heads["mnli"].out_proj.weight
                _a          =				roberta.model.classification_heads["mnli"].out_proj.bias
         else:
                # LM Head
                _a          =				roberta.model.encoder.lm_head.dense.weight
                _a          =				roberta.model.encoder.lm_head.dense.bias
                _a          =				roberta.model.encoder.lm_head.layer_norm.weight
                _a          =				roberta.model.encoder.lm_head.layer_norm.bias
                _a          =				roberta.model.encoder.lm_head.weight
                _a          =				roberta.model.encoder.lm_head.bias
  
         # Let's check that we get the same results.
         _a          =				roberta.encode(a					).unsqueeze(0					)  # batch of size 1
  
         _a          =				model(a					)[0]
         if classification_head:
                _a          =				roberta.model.classification_heads["mnli"](roberta.extract_features(a					)					)
         else:
                _a          =				roberta.model(a					)[0]
         print(our_output.shape,		their_output.shape					)
         _a          =				torch.max(torch.abs(our_output - their_output					)					).item()
         print(F'max_absolute_diff = {max_absolute_diff}'					)  # ~ 1e-7
         _a          =				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__":
     __SCREAMING_SNAKE_CASE					=						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--roberta_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					=						parser.parse_args()
     convert_roberta_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterator
  from typing import Any
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:Any       ):
            _a          =				data
            _a          =				None
  
  
  
  
     def __str__(		self							:Any       ):
            return f'{self.data}'
  
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Any       ):
            _a          =				None
            _a          =				None
  
  
  
  
     def __iter__(		self							:List[str]       ):
            _a          =				self.front
            while node:
                   yield node.data
                   _a          =				node.next
  
  
  
  
     def __len__(		self							:List[str]       ):
            return len(tuple(iter(self       )       )       )
  
  
  
  
     def __str__(		self							:Union[str, Any]       ):
            return " <- ".join(str(UpperCamelCase__       ) for item in self       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            return len(self       ) == 0
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:Any       ):
            _a          =				Node(UpperCamelCase__       )
            if self.is_empty():
                   _a          =				_a          =				node
            else:
                   assert isinstance(self.rear				,  UpperCamelCase__       )
                   _a          =				node
                   _a          =				node
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            if self.is_empty():
                   raise IndexError("dequeue from empty queue"       )
            assert isinstance(self.front				,  UpperCamelCase__       )
            _a          =				self.front
            _a          =				self.front.next
            if self.front is None:
                   _a          =				None
            return node.data
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            _a          =				_a          =				None
  
  
  if __name__ == "__main__":
     from doctest import testmod
  
     testmod()
  