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()
  _a	 =							logging.get_logger(__name__)
  
  _a	 =							'Hello world! cécé herlolip'
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
  			UpperCAmelCase_      :      Union[str, Any]      =					FairseqRobertaModel.from_pretrained(__lowerCamelCase						)
  			roberta.eval()  # disable dropout
  			UpperCAmelCase_      :      Union[str, Any]      =					roberta.model.encoder.sentence_encoder
  			UpperCAmelCase_      :      Any      =					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_      :      int      =					roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
  			print("Our BERT config:",	__lowerCamelCase						)
  
  			UpperCAmelCase_      :      str      =					RobertaForSequenceClassification(__lowerCamelCase						) if classification_head else RobertaForMaskedLM(__lowerCamelCase						)
  			model.eval()
  
  			# Now let's copy all the weights.
  			# Embeddings
  			UpperCAmelCase_      :      List[Any]      =					roberta_sent_encoder.embed_tokens.weight
  			UpperCAmelCase_      :      List[Any]      =					roberta_sent_encoder.embed_positions.weight
  			UpperCAmelCase_      :      Tuple      =					torch.zeros_like(
  			    model.roberta.embeddings.token_type_embeddings.weight						)  # just zero them out b/c RoBERTa doesn't use them.
  			UpperCAmelCase_      :      int      =					roberta_sent_encoder.emb_layer_norm.weight
  			UpperCAmelCase_      :      Dict      =					roberta_sent_encoder.emb_layer_norm.bias
  
  			for i in range(config.num_hidden_layers						):
  						# Encoder: start of layer
  						UpperCAmelCase_      :      Any      =					model.roberta.encoder.layer[i]
  						UpperCAmelCase_      :      Optional[int]      =					roberta_sent_encoder.layers[i]
  
  						# self attention
  						UpperCAmelCase_      :      Optional[int]      =					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_      :      Any      =					roberta_layer.self_attn.q_proj.weight
  						UpperCAmelCase_      :      Optional[Any]      =					roberta_layer.self_attn.q_proj.bias
  						UpperCAmelCase_      :      List[Any]      =					roberta_layer.self_attn.k_proj.weight
  						UpperCAmelCase_      :      List[str]      =					roberta_layer.self_attn.k_proj.bias
  						UpperCAmelCase_      :      List[Any]      =					roberta_layer.self_attn.v_proj.weight
  						UpperCAmelCase_      :      Tuple      =					roberta_layer.self_attn.v_proj.bias
  
  						# self-attention output
  						UpperCAmelCase_      :      Optional[Any]      =					layer.attention.output
  						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
  						UpperCAmelCase_      :      int      =					roberta_layer.self_attn.out_proj.weight
  						UpperCAmelCase_      :      str      =					roberta_layer.self_attn.out_proj.bias
  						UpperCAmelCase_      :      Tuple      =					roberta_layer.self_attn_layer_norm.weight
  						UpperCAmelCase_      :      int      =					roberta_layer.self_attn_layer_norm.bias
  
  						# intermediate
  						UpperCAmelCase_      :      Dict      =					layer.intermediate
  						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  						UpperCAmelCase_      :      Tuple      =					roberta_layer.fca.weight
  						UpperCAmelCase_      :      Any      =					roberta_layer.fca.bias
  
  						# output
  						UpperCAmelCase_      :      Optional[Any]      =					layer.output
  						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  						UpperCAmelCase_      :      Tuple      =					roberta_layer.fca.weight
  						UpperCAmelCase_      :      Any      =					roberta_layer.fca.bias
  						UpperCAmelCase_      :      Optional[Any]      =					roberta_layer.final_layer_norm.weight
  						UpperCAmelCase_      :      Optional[Any]      =					roberta_layer.final_layer_norm.bias
  						# end of layer
  
  			if classification_head:
  						UpperCAmelCase_      :      str      =					roberta.model.classification_heads["mnli"].dense.weight
  						UpperCAmelCase_      :      str      =					roberta.model.classification_heads["mnli"].dense.bias
  						UpperCAmelCase_      :      List[Any]      =					roberta.model.classification_heads["mnli"].out_proj.weight
  						UpperCAmelCase_      :      Any      =					roberta.model.classification_heads["mnli"].out_proj.bias
  			else:
  						# LM Head
  						UpperCAmelCase_      :      Dict      =					roberta.model.encoder.lm_head.dense.weight
  						UpperCAmelCase_      :      Dict      =					roberta.model.encoder.lm_head.dense.bias
  						UpperCAmelCase_      :      List[str]      =					roberta.model.encoder.lm_head.layer_norm.weight
  						UpperCAmelCase_      :      List[Any]      =					roberta.model.encoder.lm_head.layer_norm.bias
  						UpperCAmelCase_      :      Dict      =					roberta.model.encoder.lm_head.weight
  						UpperCAmelCase_      :      List[Any]      =					roberta.model.encoder.lm_head.bias
  
  			# Let's check that we get the same results.
  			UpperCAmelCase_      :      Union[str, Any]      =					roberta.encode(__lowerCamelCase						).unsqueeze(0						)  # batch of size 1
  
  			UpperCAmelCase_      :      str      =					model(__lowerCamelCase						)[0]
  			if classification_head:
  						UpperCAmelCase_      :      List[Any]      =					roberta.model.classification_heads["mnli"](roberta.extract_features(__lowerCamelCase						)						)
  			else:
  						UpperCAmelCase_      :      List[Any]      =					roberta.model(__lowerCamelCase						)[0]
  			print(our_output.shape,	their_output.shape						)
  			UpperCAmelCase_      :      Optional[Any]      =					torch.max(torch.abs(our_output - their_output						)						).item()
  			print(f"""max_absolute_diff = {max_absolute_diff}"""						)  # ~ 1e-7
  			UpperCAmelCase_      :      Optional[Any]      =					torch.allclose(__lowerCamelCase,	__lowerCamelCase,	atol=1E-3						)
  			print("Do both models output the same tensors?",	"🔥" if success else "💩"						)
  			if not success:
  						raise Exception("Something went wRoNg"						)
  
  			pathlib.Path(__lowerCamelCase						).mkdir(parents=__lowerCamelCase,	exist_ok=__lowerCamelCase						)
  			print(f"""Saving model to {pytorch_dump_folder_path}"""						)
  			model.save_pretrained(__lowerCamelCase						)
  
  
  if __name__ == "__main__":
  				_a	 =							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.'
  				)
  				_a	 =							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 .hash_table import HashTable
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     *lowercase_       ,     **lowercase_						):
  
       """simple docstring"""
       super().__init__(*lowercase_       ,     **lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_=None						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Tuple      =					1
       UpperCAmelCase_      :      List[str]      =					self.hash_function(key + i * i						)
  
       while self.values[new_key] is not None and self.values[new_key] != key:
          i += 1
          UpperCAmelCase_      :      List[Any]      =					(
              self.hash_function(key + i * i						)
              if not self.balanced_factor() >= self.lim_charge
              else None
          )
  
          if new_key is None:
             break
  
       return new_key
  
  
  