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  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__			)		->			List[Any]:
  
    '''simple docstring'''
  
  
  
  
  
  
  
    __lowercase=    FairseqRobertaModel.from_pretrained(lowercase__			)
    roberta.eval()  # disable dropout
    __lowercase=    roberta.model.encoder.sentence_encoder
    __lowercase=    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=5_1_4     ,				type_vocab_size=1     ,				layer_norm_eps=1E-5     ,				)
    if classification_head:
      __lowercase=    roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
    print('Our BERT config:'     ,				lowercase__			)
  
    __lowercase=    RobertaForSequenceClassification(lowercase__			) if classification_head else RobertaForMaskedLM(lowercase__			)
    model.eval()
  
    # Now let's copy all the weights.
    # Embeddings
    __lowercase=    roberta_sent_encoder.embed_tokens.weight
    __lowercase=    roberta_sent_encoder.embed_positions.weight
    __lowercase=    torch.zeros_like(
        model.roberta.embeddings.token_type_embeddings.weight			)  # just zero them out b/c RoBERTa doesn't use them.
    __lowercase=    roberta_sent_encoder.emb_layer_norm.weight
    __lowercase=    roberta_sent_encoder.emb_layer_norm.bias
  
    for i in range(config.num_hidden_layers			):
      # Encoder: start of layer
      __lowercase=    model.roberta.encoder.layer[i]
      __lowercase=    roberta_sent_encoder.layers[i]
  
      # self attention
      __lowercase=    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)			)
      )
  
      __lowercase=    roberta_layer.self_attn.q_proj.weight
      __lowercase=    roberta_layer.self_attn.q_proj.bias
      __lowercase=    roberta_layer.self_attn.k_proj.weight
      __lowercase=    roberta_layer.self_attn.k_proj.bias
      __lowercase=    roberta_layer.self_attn.v_proj.weight
      __lowercase=    roberta_layer.self_attn.v_proj.bias
  
      # self-attention output
      __lowercase=    layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      __lowercase=    roberta_layer.self_attn.out_proj.weight
      __lowercase=    roberta_layer.self_attn.out_proj.bias
      __lowercase=    roberta_layer.self_attn_layer_norm.weight
      __lowercase=    roberta_layer.self_attn_layer_norm.bias
  
      # intermediate
      __lowercase=    layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      __lowercase=    roberta_layer.fca.weight
      __lowercase=    roberta_layer.fca.bias
  
      # output
      __lowercase=    layer.output
      assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
      __lowercase=    roberta_layer.fca.weight
      __lowercase=    roberta_layer.fca.bias
      __lowercase=    roberta_layer.final_layer_norm.weight
      __lowercase=    roberta_layer.final_layer_norm.bias
      # end of layer
  
    if classification_head:
      __lowercase=    roberta.model.classification_heads['mnli'].dense.weight
      __lowercase=    roberta.model.classification_heads['mnli'].dense.bias
      __lowercase=    roberta.model.classification_heads['mnli'].out_proj.weight
      __lowercase=    roberta.model.classification_heads['mnli'].out_proj.bias
    else:
      # LM Head
      __lowercase=    roberta.model.encoder.lm_head.dense.weight
      __lowercase=    roberta.model.encoder.lm_head.dense.bias
      __lowercase=    roberta.model.encoder.lm_head.layer_norm.weight
      __lowercase=    roberta.model.encoder.lm_head.layer_norm.bias
      __lowercase=    roberta.model.encoder.lm_head.weight
      __lowercase=    roberta.model.encoder.lm_head.bias
  
    # Let's check that we get the same results.
    __lowercase=    roberta.encode(lowercase__			).unsqueeze(0			)  # batch of size 1
  
    __lowercase=    model(lowercase__			)[0]
    if classification_head:
      __lowercase=    roberta.model.classification_heads['mnli'](roberta.extract_features(lowercase__			)			)
    else:
      __lowercase=    roberta.model(lowercase__			)[0]
    print(our_output.shape     ,				their_output.shape			)
    __lowercase=    torch.max(torch.abs(our_output - their_output			)			).item()
    print(F'max_absolute_diff = {max_absolute_diff}'			)  # ~ 1e-7
    __lowercase=    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__":
   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: 
  
  
  def  _lowerCamelCase(			lowercase__			)		->			str:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if len(lowercase__			) == 0 or not all(len(lowercase__			) == 2 for pair in coordinates			):
  				raise ValueError('The program cannot work out a fitting polynomial.'			)
  
  		if len({tuple(lowercase__			) for pair in coordinates}			) != len(lowercase__			):
  				raise ValueError('The program cannot work out a fitting polynomial.'			)
  
  		__lowercase=    {x for x, _ in coordinates}
  		if len(lowercase__			) == 1:
  				return F'x={coordinates[0][0]}'
  
  		if len(lowercase__			) != len(lowercase__			):
  				raise ValueError('The program cannot work out a fitting polynomial.'			)
  
  		__lowercase=    len(lowercase__			)
  
  		__lowercase=    0
  		__lowercase=    []
  		# put the x and x to the power values in a matrix
  		while count_of_line < x:
  				__lowercase=    0
  				__lowercase=    coordinates[count_of_line][0]
  				__lowercase=    []
  				while count_in_line < x:
  						count_line.append(a ** (x - (count_in_line + 1))			)
  						count_in_line += 1
  				matrix.append(lowercase__			)
  				count_of_line += 1
  
  		__lowercase=    0
  		# put the y values into a vector
  		__lowercase=    []
  		while count_of_line < x:
  				vector.append(coordinates[count_of_line][1]			)
  				count_of_line += 1
  
  		__lowercase=    0
  
  		while count < x:
  				__lowercase=    0
  				while zahlen < x:
  						if count == zahlen:
  								zahlen += 1
  						if zahlen == x:
  								break
  						__lowercase=    matrix[zahlen][count] / matrix[count][count]
  						for counting_columns, item in enumerate(matrix[count]			):
  								# manipulating all the values in the matrix
  								matrix[zahlen][counting_columns] -= item * bruch
  						# manipulating the values in the vector
  						vector[zahlen] -= vector[count] * bruch
  						zahlen += 1
  				count += 1
  
  		__lowercase=    0
  		# make solutions
  		__lowercase=    []
  		while count < x:
  				solution.append(str(vector[count] / matrix[count][count]			)			)
  				count += 1
  
  		__lowercase=    0
  		__lowercase=    'f(x)='
  
  		while count < x:
  				__lowercase=    solution[count].split('E'			)
  				if len(lowercase__			) > 1:
  						__lowercase=    F'{remove_e[0]}*10^{remove_e[1]}'
  				solved += F'x^{x - (count + 1)}*{solution[count]}'
  				if count + 1 != x:
  						solved += "+"
  				count += 1
  
  		return solved
  
  
  if __name__ == "__main__":
  	print(points_to_polynomial([]))
  	print(points_to_polynomial([[]]))
  	print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
  	print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
  	print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
  	print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
  	print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
  	print(points_to_polynomial([[1, 3], [2, 6], [3, 1_1]]))
  	print(points_to_polynomial([[1, -3], [2, -6], [3, -1_1]]))
  	print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))
  
  
  
  
  
  
  