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()
  __magic_name__		       =				logging.get_logger(__name__)
  
  __magic_name__		       =				'''Hello world! cécé herlolip'''
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
   """simple docstring"""
  
  
  
  
  
  
   _UpperCAmelCase =						FairseqRobertaModel.from_pretrained(UpperCamelCase__      )
   roberta.eval()  # disable dropout
   _UpperCAmelCase =						roberta.model.encoder.sentence_encoder
   _UpperCAmelCase =						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:
    _UpperCAmelCase =						roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
   print("Our BERT config:"				,     UpperCamelCase__      )
  
   _UpperCAmelCase =						RobertaForSequenceClassification(UpperCamelCase__      ) if classification_head else RobertaForMaskedLM(UpperCamelCase__      )
   model.eval()
  
   # Now let's copy all the weights.
   # Embeddings
   _UpperCAmelCase =						roberta_sent_encoder.embed_tokens.weight
   _UpperCAmelCase =						roberta_sent_encoder.embed_positions.weight
   _UpperCAmelCase =						torch.zeros_like(
       model.roberta.embeddings.token_type_embeddings.weight      )  # just zero them out b/c RoBERTa doesn't use them.
   _UpperCAmelCase =						roberta_sent_encoder.emb_layer_norm.weight
   _UpperCAmelCase =						roberta_sent_encoder.emb_layer_norm.bias
  
   for i in range(config.num_hidden_layers      ):
    # Encoder: start of layer
    _UpperCAmelCase =						model.roberta.encoder.layer[i]
    _UpperCAmelCase =						roberta_sent_encoder.layers[i]
  
    # self attention
    _UpperCAmelCase =						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)      )
    )
  
    _UpperCAmelCase =						roberta_layer.self_attn.q_proj.weight
    _UpperCAmelCase =						roberta_layer.self_attn.q_proj.bias
    _UpperCAmelCase =						roberta_layer.self_attn.k_proj.weight
    _UpperCAmelCase =						roberta_layer.self_attn.k_proj.bias
    _UpperCAmelCase =						roberta_layer.self_attn.v_proj.weight
    _UpperCAmelCase =						roberta_layer.self_attn.v_proj.bias
  
    # self-attention output
    _UpperCAmelCase =						layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    _UpperCAmelCase =						roberta_layer.self_attn.out_proj.weight
    _UpperCAmelCase =						roberta_layer.self_attn.out_proj.bias
    _UpperCAmelCase =						roberta_layer.self_attn_layer_norm.weight
    _UpperCAmelCase =						roberta_layer.self_attn_layer_norm.bias
  
    # intermediate
    _UpperCAmelCase =						layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    _UpperCAmelCase =						roberta_layer.fca.weight
    _UpperCAmelCase =						roberta_layer.fca.bias
  
    # output
    _UpperCAmelCase =						layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    _UpperCAmelCase =						roberta_layer.fca.weight
    _UpperCAmelCase =						roberta_layer.fca.bias
    _UpperCAmelCase =						roberta_layer.final_layer_norm.weight
    _UpperCAmelCase =						roberta_layer.final_layer_norm.bias
    # end of layer
  
   if classification_head:
    _UpperCAmelCase =						roberta.model.classification_heads["mnli"].dense.weight
    _UpperCAmelCase =						roberta.model.classification_heads["mnli"].dense.bias
    _UpperCAmelCase =						roberta.model.classification_heads["mnli"].out_proj.weight
    _UpperCAmelCase =						roberta.model.classification_heads["mnli"].out_proj.bias
   else:
    # LM Head
    _UpperCAmelCase =						roberta.model.encoder.lm_head.dense.weight
    _UpperCAmelCase =						roberta.model.encoder.lm_head.dense.bias
    _UpperCAmelCase =						roberta.model.encoder.lm_head.layer_norm.weight
    _UpperCAmelCase =						roberta.model.encoder.lm_head.layer_norm.bias
    _UpperCAmelCase =						roberta.model.encoder.lm_head.weight
    _UpperCAmelCase =						roberta.model.encoder.lm_head.bias
  
   # Let's check that we get the same results.
   _UpperCAmelCase =						roberta.encode(UpperCamelCase__      ).unsqueeze(0      )  # batch of size 1
  
   _UpperCAmelCase =						model(UpperCamelCase__      )[0]
   if classification_head:
    _UpperCAmelCase =						roberta.model.classification_heads["mnli"](roberta.extract_features(UpperCamelCase__      )      )
   else:
    _UpperCAmelCase =						roberta.model(UpperCamelCase__      )[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(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__":
     __magic_name__		       =				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.'''
     )
     __magic_name__		       =				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 typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
  
  
  __magic_name__		       =				{}
  
  try:
  			if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				['''CpmTokenizer''']
  
  try:
  			if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				['''CpmTokenizerFast''']
  
  
  if TYPE_CHECKING:
  			try:
  						if not is_sentencepiece_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_cpm import CpmTokenizer
  
  			try:
  						if not is_tokenizers_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_cpm_fast import CpmTokenizerFast
  
  else:
  			import sys
  
  			__magic_name__		       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  