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()
  lowercase_            =				logging.get_logger(__name__)
  
  lowercase_            =				"""Hello world! cécé herlolip"""
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str      , __lowerCamelCase				:					bool					)		->str:
      _SCREAMING_SNAKE_CASE             =    FairseqRobertaModel.from_pretrained(UpperCamelCase__					)
      roberta.eval()  # disable dropout
      _SCREAMING_SNAKE_CASE             =    roberta.model.encoder.sentence_encoder
      _SCREAMING_SNAKE_CASE             =    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:
          _SCREAMING_SNAKE_CASE             =    roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
      print("""Our BERT config:"""      , UpperCamelCase__					)
  
      _SCREAMING_SNAKE_CASE             =    RobertaForSequenceClassification(UpperCamelCase__					) if classification_head else RobertaForMaskedLM(UpperCamelCase__					)
      model.eval()
  
      # Now let's copy all the weights.
      # Embeddings
      _SCREAMING_SNAKE_CASE             =    roberta_sent_encoder.embed_tokens.weight
      _SCREAMING_SNAKE_CASE             =    roberta_sent_encoder.embed_positions.weight
      _SCREAMING_SNAKE_CASE             =    torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight					)  # just zero them out b/c RoBERTa doesn't use them.
      _SCREAMING_SNAKE_CASE             =    roberta_sent_encoder.emb_layer_norm.weight
      _SCREAMING_SNAKE_CASE             =    roberta_sent_encoder.emb_layer_norm.bias
  
      for i in range(config.num_hidden_layers					):
          # Encoder: start of layer
          _SCREAMING_SNAKE_CASE             =    model.roberta.encoder.layer[i]
          _SCREAMING_SNAKE_CASE             =    roberta_sent_encoder.layers[i]
  
          # self attention
          _SCREAMING_SNAKE_CASE             =    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)					)
          )
  
          _SCREAMING_SNAKE_CASE             =    roberta_layer.self_attn.q_proj.weight
          _SCREAMING_SNAKE_CASE             =    roberta_layer.self_attn.q_proj.bias
          _SCREAMING_SNAKE_CASE             =    roberta_layer.self_attn.k_proj.weight
          _SCREAMING_SNAKE_CASE             =    roberta_layer.self_attn.k_proj.bias
          _SCREAMING_SNAKE_CASE             =    roberta_layer.self_attn.v_proj.weight
          _SCREAMING_SNAKE_CASE             =    roberta_layer.self_attn.v_proj.bias
  
          # self-attention output
          _SCREAMING_SNAKE_CASE             =    layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          _SCREAMING_SNAKE_CASE             =    roberta_layer.self_attn.out_proj.weight
          _SCREAMING_SNAKE_CASE             =    roberta_layer.self_attn.out_proj.bias
          _SCREAMING_SNAKE_CASE             =    roberta_layer.self_attn_layer_norm.weight
          _SCREAMING_SNAKE_CASE             =    roberta_layer.self_attn_layer_norm.bias
  
          # intermediate
          _SCREAMING_SNAKE_CASE             =    layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          _SCREAMING_SNAKE_CASE             =    roberta_layer.fca.weight
          _SCREAMING_SNAKE_CASE             =    roberta_layer.fca.bias
  
          # output
          _SCREAMING_SNAKE_CASE             =    layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          _SCREAMING_SNAKE_CASE             =    roberta_layer.fca.weight
          _SCREAMING_SNAKE_CASE             =    roberta_layer.fca.bias
          _SCREAMING_SNAKE_CASE             =    roberta_layer.final_layer_norm.weight
          _SCREAMING_SNAKE_CASE             =    roberta_layer.final_layer_norm.bias
          # end of layer
  
      if classification_head:
          _SCREAMING_SNAKE_CASE             =    roberta.model.classification_heads["mnli"].dense.weight
          _SCREAMING_SNAKE_CASE             =    roberta.model.classification_heads["mnli"].dense.bias
          _SCREAMING_SNAKE_CASE             =    roberta.model.classification_heads["mnli"].out_proj.weight
          _SCREAMING_SNAKE_CASE             =    roberta.model.classification_heads["mnli"].out_proj.bias
      else:
          # LM Head
          _SCREAMING_SNAKE_CASE             =    roberta.model.encoder.lm_head.dense.weight
          _SCREAMING_SNAKE_CASE             =    roberta.model.encoder.lm_head.dense.bias
          _SCREAMING_SNAKE_CASE             =    roberta.model.encoder.lm_head.layer_norm.weight
          _SCREAMING_SNAKE_CASE             =    roberta.model.encoder.lm_head.layer_norm.bias
          _SCREAMING_SNAKE_CASE             =    roberta.model.encoder.lm_head.weight
          _SCREAMING_SNAKE_CASE             =    roberta.model.encoder.lm_head.bias
  
      # Let's check that we get the same results.
      _SCREAMING_SNAKE_CASE             =    roberta.encode(UpperCamelCase__					).unsqueeze(0					)  # batch of size 1
  
      _SCREAMING_SNAKE_CASE             =    model(UpperCamelCase__					)[0]
      if classification_head:
          _SCREAMING_SNAKE_CASE             =    roberta.model.classification_heads["mnli"](roberta.extract_features(UpperCamelCase__					)					)
      else:
          _SCREAMING_SNAKE_CASE             =    roberta.model(UpperCamelCase__					)[0]
      print(our_output.shape      , their_output.shape					)
      _SCREAMING_SNAKE_CASE             =    torch.max(torch.abs(our_output - their_output					)					).item()
      print(F'max_absolute_diff = {max_absolute_diff}'					)  # ~ 1e-7
      _SCREAMING_SNAKE_CASE             =    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__":
        lowercase_            =				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."""
        )
        lowercase_            =				parser.parse_args()
        convert_roberta_checkpoint_to_pytorch(
            args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
        )
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  # Copyright 2022 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import os
  from typing import List
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					str					)		->Tuple:
      if name != "training_function" and name != "main":
          raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''					)
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    [], False
      for line in lines:
          if not found_start and F'def {name}' in line:
              _SCREAMING_SNAKE_CASE             =    True
              good_lines.append(__lowerCamelCase					)
              continue
          if found_start:
              if name == "training_function" and "def main" in line:
                  return good_lines
              if name == "main" and "if __name__" in line:
                  return good_lines
              good_lines.append(__lowerCamelCase					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]					)		->List[str]:
      return [line for line in lines if not line.lstrip().startswith("""#"""					) and line != "\n"]
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str      , __lowerCamelCase				:					bool      , __lowerCamelCase				:					str = None					)		->List[str]:
      with open(__lowerCamelCase      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      with open(os.path.abspath(os.path.join("""examples"""      , """nlp_example.py"""					)					)      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      with open(__lowerCamelCase      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      if secondary_filename is not None:
          with open(__lowerCamelCase      , """r"""					) as f:
              _SCREAMING_SNAKE_CASE             =    f.readlines()
  
      # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
      if parser_only:
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          if secondary_filename is not None:
              _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
      else:
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          if secondary_filename is not None:
              _SCREAMING_SNAKE_CASE             =    clean_lines(
                  get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
  
      _SCREAMING_SNAKE_CASE             =    """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""
  
      # Specific code in our script that differs from the full version, aka what is new
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
      _SCREAMING_SNAKE_CASE             =    iter(__lowerCamelCase					)
      for i in range(len(__lowerCamelCase					) - 1					):
          if i not in passed_idxs:
              _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_feature_code.append(__lowerCamelCase					)
                      passed_idxs.append(__lowerCamelCase					)
                  else:
                      # Skip over the `config['num_epochs'] = 2` statement
                      _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
  
      # Extract out just the new parts from the full_file_training_func
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
      for i, line in enumerate(__lowerCamelCase					):
          if i not in passed_idxs:
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_full_example_parts.append(__lowerCamelCase					)
                      passed_idxs.append(__lowerCamelCase					)
  
      # Finally, get the overall diff
      _SCREAMING_SNAKE_CASE             =    [line for line in new_feature_code if line not in new_full_example_parts]
      if secondary_filename is not None:
          _SCREAMING_SNAKE_CASE             =    [line for line in full_file_contents if line not in secondary_file_func]
          _SCREAMING_SNAKE_CASE             =    [line for line in diff_from_example if line not in diff_from_two]
  
      return diff_from_example
  
  
  
  