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       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Union[str, Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					int     =	FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
  		roberta.eval()  # disable dropout
  		snake_case     :					Any     =	roberta.model.encoder.sentence_encoder
  		snake_case     :					Optional[int]     =	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:
  				snake_case     :					Tuple     =	roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
  		print('''Our BERT config:'''   ,					SCREAMING_SNAKE_CASE__ )
  
  		snake_case     :					List[Any]     =	RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__ ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__ )
  		model.eval()
  
  		# Now let's copy all the weights.
  		# Embeddings
  		snake_case     :					List[Any]     =	roberta_sent_encoder.embed_tokens.weight
  		snake_case     :					Dict     =	roberta_sent_encoder.embed_positions.weight
  		snake_case     :					str     =	torch.zeros_like(
  		    model.roberta.embeddings.token_type_embeddings.weight )  # just zero them out b/c RoBERTa doesn't use them.
  		snake_case     :					Dict     =	roberta_sent_encoder.emb_layer_norm.weight
  		snake_case     :					List[Any]     =	roberta_sent_encoder.emb_layer_norm.bias
  
  		for i in range(config.num_hidden_layers ):
  				# Encoder: start of layer
  				snake_case     :					int     =	model.roberta.encoder.layer[i]
  				snake_case     :					Tuple     =	roberta_sent_encoder.layers[i]
  
  				# self attention
  				snake_case     :					str     =	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) )
  				)
  
  				snake_case     :					int     =	roberta_layer.self_attn.q_proj.weight
  				snake_case     :					Union[str, Any]     =	roberta_layer.self_attn.q_proj.bias
  				snake_case     :					Optional[int]     =	roberta_layer.self_attn.k_proj.weight
  				snake_case     :					Tuple     =	roberta_layer.self_attn.k_proj.bias
  				snake_case     :					List[str]     =	roberta_layer.self_attn.v_proj.weight
  				snake_case     :					Optional[Any]     =	roberta_layer.self_attn.v_proj.bias
  
  				# self-attention output
  				snake_case     :					Any     =	layer.attention.output
  				assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
  				snake_case     :					Optional[int]     =	roberta_layer.self_attn.out_proj.weight
  				snake_case     :					Optional[int]     =	roberta_layer.self_attn.out_proj.bias
  				snake_case     :					Any     =	roberta_layer.self_attn_layer_norm.weight
  				snake_case     :					Dict     =	roberta_layer.self_attn_layer_norm.bias
  
  				# intermediate
  				snake_case     :					List[Any]     =	layer.intermediate
  				assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  				snake_case     :					int     =	roberta_layer.fca.weight
  				snake_case     :					List[str]     =	roberta_layer.fca.bias
  
  				# output
  				snake_case     :					List[str]     =	layer.output
  				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  				snake_case     :					Optional[Any]     =	roberta_layer.fca.weight
  				snake_case     :					Optional[Any]     =	roberta_layer.fca.bias
  				snake_case     :					Optional[int]     =	roberta_layer.final_layer_norm.weight
  				snake_case     :					Union[str, Any]     =	roberta_layer.final_layer_norm.bias
  				# end of layer
  
  		if classification_head:
  				snake_case     :					Optional[int]     =	roberta.model.classification_heads['''mnli'''].dense.weight
  				snake_case     :					Tuple     =	roberta.model.classification_heads['''mnli'''].dense.bias
  				snake_case     :					List[Any]     =	roberta.model.classification_heads['''mnli'''].out_proj.weight
  				snake_case     :					List[str]     =	roberta.model.classification_heads['''mnli'''].out_proj.bias
  		else:
  				# LM Head
  				snake_case     :					Optional[int]     =	roberta.model.encoder.lm_head.dense.weight
  				snake_case     :					Tuple     =	roberta.model.encoder.lm_head.dense.bias
  				snake_case     :					Union[str, Any]     =	roberta.model.encoder.lm_head.layer_norm.weight
  				snake_case     :					Tuple     =	roberta.model.encoder.lm_head.layer_norm.bias
  				snake_case     :					Union[str, Any]     =	roberta.model.encoder.lm_head.weight
  				snake_case     :					Union[str, Any]     =	roberta.model.encoder.lm_head.bias
  
  		# Let's check that we get the same results.
  		snake_case     :					str     =	roberta.encode(SCREAMING_SNAKE_CASE__ ).unsqueeze(0 )  # batch of size 1
  
  		snake_case     :					Optional[Any]     =	model(SCREAMING_SNAKE_CASE__ )[0]
  		if classification_head:
  				snake_case     :					Union[str, Any]     =	roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE__ ) )
  		else:
  				snake_case     :					List[str]     =	roberta.model(SCREAMING_SNAKE_CASE__ )[0]
  		print(our_output.shape   ,					their_output.shape )
  		snake_case     :					Tuple     =	torch.max(torch.abs(our_output - their_output ) ).item()
  		print(F'max_absolute_diff = {max_absolute_diff}' )  # ~ 1e-7
  		snake_case     :					Dict     =	torch.allclose(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					atol=1E-3 )
  		print('''Do both models output the same tensors?'''   ,					'''🔥''' if success else '''💩''' )
  		if not success:
  				raise Exception('''Something went wRoNg''' )
  
  		pathlib.Path(SCREAMING_SNAKE_CASE__ ).mkdir(parents=SCREAMING_SNAKE_CASE__   ,					exist_ok=SCREAMING_SNAKE_CASE__ )
  		print(F'Saving model to {pytorch_dump_folder_path}' )
  		model.save_pretrained(SCREAMING_SNAKE_CASE__ )
  
  
  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'''
  def       _UpperCamelCase	(					)     ->       int:
  
  
  		'''simple docstring'''
  
  
  		for a in range(300 ):
  				for b in range(a + 1   ,					400 ):
  						for c in range(b + 1   ,					500 ):
  								if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
  										return a * b * c
  
  		return -1
  
  
  
  
  def       _UpperCamelCase	(					)     ->       int:
  
  
  		'''simple docstring'''
  
  
  		for a in range(300 ):
  				for b in range(400 ):
  						snake_case     :					Dict     =	1000 - a - b
  						if a < b < c and (a**2) + (b**2) == (c**2):
  								return a * b * c
  
  		return -1
  
  
  
  
  def       _UpperCamelCase	(					)     ->       None:
  
  
  		'''simple docstring'''
  
  
  		import timeit
  
  		print(
  		    timeit.timeit('''solution()'''   ,					setup='''from __main__ import solution'''   ,					number=1000 ) )
  		print(
  		    timeit.timeit(
  		        '''solution_fast()'''   ,					setup='''from __main__ import solution_fast'''   ,					number=1000 ) )
  
  
  if __name__ == "__main__":
  						print(f"{solution() = }")
  
  
  