code: 
  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       a			(   a		,	a		,	a  )    ->Union[str, Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   FairseqRobertaModel.from_pretrained(a  )
  			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:'''		,	a  )
  
  			SCREAMING_SNAKE_CASE    	=   RobertaForSequenceClassification(a  ) if classification_head else RobertaForMaskedLM(a  )
  			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(a  ).unsqueeze(0  )  # batch of size 1
  
  			SCREAMING_SNAKE_CASE    	=   model(a  )[0]
  			if classification_head:
  						SCREAMING_SNAKE_CASE    	=   roberta.model.classification_heads['''mnli'''](roberta.extract_features(a  )  )
  			else:
  						SCREAMING_SNAKE_CASE    	=   roberta.model(a  )[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(a		,	a		,	atol=1E-3  )
  			print('''Do both models output the same tensors?'''		,	'''🔥''' if success else '''💩'''  )
  			if not success:
  						raise Exception('''Something went wRoNg'''  )
  
  			pathlib.Path(a  ).mkdir(parents=a		,	exist_ok=a  )
  			print(F"""Saving model to {pytorch_dump_folder_path}"""  )
  			model.save_pretrained(a  )
  
  
  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       a			(   a		,	a  )    ->str:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', '''''', ''''''
  			SCREAMING_SNAKE_CASE    	=   []
  
  			for character in message_part:
  						tmp.append(character_to_number[character]  )
  
  			for each in tmp:
  						one += each[0]
  						two += each[1]
  						three += each[2]
  
  			return one + two + three
  
  
  def       a			(   a		,	a  )    ->tuple[str, str, str]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', ''''''
  			SCREAMING_SNAKE_CASE    	=   []
  
  			for character in message_part:
  						this_part += character_to_number[character]
  
  			for digit in this_part:
  						tmp += digit
  						if len(a  ) == len(a  ):
  									result.append(a  )
  									SCREAMING_SNAKE_CASE    	=   ''''''
  
  			return result[0], result[1], result[2]
  
  
  def       a			(   a		,	a  )    ->tuple[str, str, dict[str, str], dict[str, str]]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   alphabet.replace(''' '''		,	''''''  ).upper()
  			SCREAMING_SNAKE_CASE    	=   message.replace(''' '''		,	''''''  ).upper()
  
  			# Check length and characters
  			if len(a  ) != 27:
  						raise KeyError('''Length of alphabet has to be 27.'''  )
  			for each in message:
  						if each not in alphabet:
  									raise ValueError('''Each message character has to be included in alphabet!'''  )
  
      # Generate dictionares
  			SCREAMING_SNAKE_CASE    	=   (
  			    '''111''',
  			    '''112''',
  			    '''113''',
  			    '''121''',
  			    '''122''',
  			    '''123''',
  			    '''131''',
  			    '''132''',
  			    '''133''',
  			    '''211''',
  			    '''212''',
  			    '''213''',
  			    '''221''',
  			    '''222''',
  			    '''223''',
  			    '''231''',
  			    '''232''',
  			    '''233''',
  			    '''311''',
  			    '''312''',
  			    '''313''',
  			    '''321''',
  			    '''322''',
  			    '''323''',
  			    '''331''',
  			    '''332''',
  			    '''333''',
  			)
  			SCREAMING_SNAKE_CASE    	=   {}
  			SCREAMING_SNAKE_CASE    	=   {}
  			for letter, number in zip(a		,	a  ):
  						SCREAMING_SNAKE_CASE    	=   number
  						SCREAMING_SNAKE_CASE    	=   letter
  
  			return message, alphabet, character_to_number, number_to_character
  
  
  def       a			(   a		,	a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		,	a = 5  )    ->str:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __prepare(
  			    a		,	a  )
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', ''''''
  
  			for i in range(0		,	len(a  ) + 1		,	a  ):
  						encrypted_numeric += __encrypt_part(
  						    message[i : i + period]		,	a  )
  
  			for i in range(0		,	len(a  )		,	3  ):
  						encrypted += number_to_character[encrypted_numeric[i : i + 3]]
  
  			return encrypted
  
  
  def       a			(   a		,	a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		,	a = 5  )    ->str:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __prepare(
  			    a		,	a  )
  			SCREAMING_SNAKE_CASE    	=   []
  			SCREAMING_SNAKE_CASE    	=   ''''''
  
  			for i in range(0		,	len(a  ) + 1		,	a  ):
  						SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __decrypt_part(message[i : i + period]		,	a  )
  
  						for j in range(0		,	len(a  )  ):
  									decrypted_numeric.append(a[j] + b[j] + c[j]  )
  
  			for each in decrypted_numeric:
  						decrypted += number_to_character[each]
  
  			return decrypted
  
  
  if __name__ == "__main__":
  							__lowerCAmelCase				      =					'DEFEND THE EAST WALL OF THE CASTLE.'
  							__lowerCAmelCase				      =					encrypt_message(msg, 'EPSDUCVWYM.ZLKXNBTFGORIJHAQ')
  							__lowerCAmelCase				      =					decrypt_message(encrypted, 'EPSDUCVWYM.ZLKXNBTFGORIJHAQ')
  							print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')