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__	:   List[Any]          =							logging.get_logger(__name__)
  
  __magic_name__	:   Optional[int]          =							'Hello world! cécé herlolip'
  
  
  
  
  
  def 			a_   (  lowercase__   :str, lowercase__   :str, lowercase__   :bool    ):
  			__lowerCamelCase        =			FairseqRobertaModel.from_pretrained(lowercase__    )
  			roberta.eval()  # disable dropout
  			__lowerCamelCase        =			roberta.model.encoder.sentence_encoder
  			__lowerCamelCase        =			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:
  						__lowerCamelCase        =			roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
  			print("""Our BERT config:""", lowercase__    )
  
  			__lowerCamelCase        =			RobertaForSequenceClassification(lowercase__    ) if classification_head else RobertaForMaskedLM(lowercase__    )
  			model.eval()
  
  			# Now let's copy all the weights.
  			# Embeddings
  			__lowerCamelCase        =			roberta_sent_encoder.embed_tokens.weight
  			__lowerCamelCase        =			roberta_sent_encoder.embed_positions.weight
  			__lowerCamelCase        =			torch.zeros_like(
  			    model.roberta.embeddings.token_type_embeddings.weight    )  # just zero them out b/c RoBERTa doesn't use them.
  			__lowerCamelCase        =			roberta_sent_encoder.emb_layer_norm.weight
  			__lowerCamelCase        =			roberta_sent_encoder.emb_layer_norm.bias
  
  			for i in range(config.num_hidden_layers    ):
  						# Encoder: start of layer
  						__lowerCamelCase        =			model.roberta.encoder.layer[i]
  						__lowerCamelCase        =			roberta_sent_encoder.layers[i]
  
  						# self attention
  						__lowerCamelCase        =			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)    )
  						)
  
  						__lowerCamelCase        =			roberta_layer.self_attn.q_proj.weight
  						__lowerCamelCase        =			roberta_layer.self_attn.q_proj.bias
  						__lowerCamelCase        =			roberta_layer.self_attn.k_proj.weight
  						__lowerCamelCase        =			roberta_layer.self_attn.k_proj.bias
  						__lowerCamelCase        =			roberta_layer.self_attn.v_proj.weight
  						__lowerCamelCase        =			roberta_layer.self_attn.v_proj.bias
  
  						# self-attention output
  						__lowerCamelCase        =			layer.attention.output
  						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
  						__lowerCamelCase        =			roberta_layer.self_attn.out_proj.weight
  						__lowerCamelCase        =			roberta_layer.self_attn.out_proj.bias
  						__lowerCamelCase        =			roberta_layer.self_attn_layer_norm.weight
  						__lowerCamelCase        =			roberta_layer.self_attn_layer_norm.bias
  
  						# intermediate
  						__lowerCamelCase        =			layer.intermediate
  						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  						__lowerCamelCase        =			roberta_layer.fca.weight
  						__lowerCamelCase        =			roberta_layer.fca.bias
  
  						# output
  						__lowerCamelCase        =			layer.output
  						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  						__lowerCamelCase        =			roberta_layer.fca.weight
  						__lowerCamelCase        =			roberta_layer.fca.bias
  						__lowerCamelCase        =			roberta_layer.final_layer_norm.weight
  						__lowerCamelCase        =			roberta_layer.final_layer_norm.bias
  						# end of layer
  
  			if classification_head:
  						__lowerCamelCase        =			roberta.model.classification_heads["""mnli"""].dense.weight
  						__lowerCamelCase        =			roberta.model.classification_heads["""mnli"""].dense.bias
  						__lowerCamelCase        =			roberta.model.classification_heads["""mnli"""].out_proj.weight
  						__lowerCamelCase        =			roberta.model.classification_heads["""mnli"""].out_proj.bias
  			else:
  						# LM Head
  						__lowerCamelCase        =			roberta.model.encoder.lm_head.dense.weight
  						__lowerCamelCase        =			roberta.model.encoder.lm_head.dense.bias
  						__lowerCamelCase        =			roberta.model.encoder.lm_head.layer_norm.weight
  						__lowerCamelCase        =			roberta.model.encoder.lm_head.layer_norm.bias
  						__lowerCamelCase        =			roberta.model.encoder.lm_head.weight
  						__lowerCamelCase        =			roberta.model.encoder.lm_head.bias
  
  			# Let's check that we get the same results.
  			__lowerCamelCase        =			roberta.encode(lowercase__    ).unsqueeze(0    )  # batch of size 1
  
  			__lowerCamelCase        =			model(lowercase__    )[0]
  			if classification_head:
  						__lowerCamelCase        =			roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowercase__    )    )
  			else:
  						__lowerCamelCase        =			roberta.model(lowercase__    )[0]
  			print(our_output.shape, their_output.shape    )
  			__lowerCamelCase        =			torch.max(torch.abs(our_output - their_output    )    ).item()
  			print(f'max_absolute_diff = {max_absolute_diff}'    )  # ~ 1e-7
  			__lowerCamelCase        =			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__":
  					__magic_name__	:   Optional[int]          =							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__	:   Optional[Any]          =							parser.parse_args()
  					convert_roberta_checkpoint_to_pytorch(
  					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  					)
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  
  
  
  
  
  def 			a_   (  lowercase__   :List[str]    ):
  			__lowerCamelCase        =			np.exp(lowercase__    )
  
  			# Add up the all the exponentials
  			__lowerCamelCase        =			np.sum(lowercase__    )
  
  			# Divide every exponent by the sum of all exponents
  			__lowerCamelCase        =			exponent_vector / sum_of_exponents
  
  			return softmax_vector
  
  
  if __name__ == "__main__":
  					print(softmax((0,)))
  
  
  
  
  