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()
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  '''Hello world! cécé herlolip'''
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	FairseqRobertaModel.from_pretrained(_lowercase      )
      roberta.eval()  # disable dropout
      _lowercase     =	roberta.model.encoder.sentence_encoder
      _lowercase     =	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=514,      type_vocab_size=1,      layer_norm_eps=1e-5,      )
      if classification_head:
          _lowercase     =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
      print('Our BERT config:',      _lowercase      )
  
      _lowercase     =	RobertaForSequenceClassification(_lowercase      ) if classification_head else RobertaForMaskedLM(_lowercase      )
      model.eval()
  
      # Now let's copy all the weights.
      # Embeddings
      _lowercase     =	roberta_sent_encoder.embed_tokens.weight
      _lowercase     =	roberta_sent_encoder.embed_positions.weight
      _lowercase     =	torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight      )  # just zero them out b/c RoBERTa doesn't use them.
      _lowercase     =	roberta_sent_encoder.emb_layer_norm.weight
      _lowercase     =	roberta_sent_encoder.emb_layer_norm.bias
  
      for i in range(config.num_hidden_layers      ):
          # Encoder: start of layer
          _lowercase     =	model.roberta.encoder.layer[i]
          _lowercase     =	roberta_sent_encoder.layers[i]
  
          # self attention
          _lowercase     =	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)      )
          )
  
          _lowercase     =	roberta_layer.self_attn.q_proj.weight
          _lowercase     =	roberta_layer.self_attn.q_proj.bias
          _lowercase     =	roberta_layer.self_attn.k_proj.weight
          _lowercase     =	roberta_layer.self_attn.k_proj.bias
          _lowercase     =	roberta_layer.self_attn.v_proj.weight
          _lowercase     =	roberta_layer.self_attn.v_proj.bias
  
          # self-attention output
          _lowercase     =	layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          _lowercase     =	roberta_layer.self_attn.out_proj.weight
          _lowercase     =	roberta_layer.self_attn.out_proj.bias
          _lowercase     =	roberta_layer.self_attn_layer_norm.weight
          _lowercase     =	roberta_layer.self_attn_layer_norm.bias
  
          # intermediate
          _lowercase     =	layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          _lowercase     =	roberta_layer.fca.weight
          _lowercase     =	roberta_layer.fca.bias
  
          # output
          _lowercase     =	layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          _lowercase     =	roberta_layer.fca.weight
          _lowercase     =	roberta_layer.fca.bias
          _lowercase     =	roberta_layer.final_layer_norm.weight
          _lowercase     =	roberta_layer.final_layer_norm.bias
          # end of layer
  
      if classification_head:
          _lowercase     =	roberta.model.classification_heads['mnli'].dense.weight
          _lowercase     =	roberta.model.classification_heads['mnli'].dense.bias
          _lowercase     =	roberta.model.classification_heads['mnli'].out_proj.weight
          _lowercase     =	roberta.model.classification_heads['mnli'].out_proj.bias
      else:
          # LM Head
          _lowercase     =	roberta.model.encoder.lm_head.dense.weight
          _lowercase     =	roberta.model.encoder.lm_head.dense.bias
          _lowercase     =	roberta.model.encoder.lm_head.layer_norm.weight
          _lowercase     =	roberta.model.encoder.lm_head.layer_norm.bias
          _lowercase     =	roberta.model.encoder.lm_head.weight
          _lowercase     =	roberta.model.encoder.lm_head.bias
  
      # Let's check that we get the same results.
      _lowercase     =	roberta.encode(_lowercase      ).unsqueeze(0      )  # batch of size 1
  
      _lowercase     =	model(_lowercase      )[0]
      if classification_head:
          _lowercase     =	roberta.model.classification_heads['mnli'](roberta.extract_features(_lowercase      )      )
      else:
          _lowercase     =	roberta.model(_lowercase      )[0]
      print(our_output.shape,      their_output.shape      )
      _lowercase     =	torch.max(torch.abs(our_output - their_output      )      ).item()
      print(f'''max_absolute_diff = {max_absolute_diff}'''      )  # ~ 1e-7
      _lowercase     =	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(
         '''--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.'''
     )
     lowerCamelCase										=  parser.parse_args()
     convert_roberta_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	[None] * (len(_lowercase      ) + len(_lowercase      ))
  
      _lowercase     =	0  # pointer to current index for left Half
      _lowercase     =	0  # pointer to current index for the right Half
      _lowercase     =	0  # pointer to current index for the sorted array Half
  
      while pointera < len(_lowercase      ) and pointera < len(_lowercase      ):
          if left_half[pointera] < right_half[pointera]:
              _lowercase     =	left_half[pointera]
              pointera += 1
              index += 1
          else:
              _lowercase     =	right_half[pointera]
              pointera += 1
              index += 1
      while pointera < len(_lowercase      ):
          _lowercase     =	left_half[pointera]
          pointera += 1
          index += 1
  
      while pointera < len(_lowercase      ):
          _lowercase     =	right_half[pointera]
          pointera += 1
          index += 1
  
      return sorted_array
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      if len(_lowercase      ) <= 1:
          return array
      # the actual formula to calculate the middle element = left + (right - left) // 2
      # this avoids integer overflow in case of large N
      _lowercase     =	0 + (len(_lowercase      ) - 0) // 2
  
      # Split the array into halves till the array length becomes equal to One
      # merge the arrays of single length returned by mergeSort function and
      # pass them into the merge arrays function which merges the array
      _lowercase     =	array[:middle]
      _lowercase     =	array[middle:]
  
      return merge(merge_sort(_lowercase      ),      merge_sort(_lowercase      )      )
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  