code: 
  
  
  
  
  
  
  import argparse
  
  import torch
  from torch import nn
  
  from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			Union[str, Any]:
       UpperCamelCase__    :     Tuple        =						[
           "encoder.version",
           "decoder.version",
           "model.encoder.version",
           "model.decoder.version",
           "_float_tensor",
           "decoder.output_projection.weight",
       ]
       for k in ignore_keys:
            state_dict.pop(__lowerCAmelCase      ,					__lowerCAmelCase    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			Dict:
       UpperCamelCase__       ,  UpperCamelCase__    :     List[Any]        =						emb.weight.shape
       UpperCamelCase__    :     List[str]        =						nn.Linear(__lowerCAmelCase      ,					__lowerCAmelCase      ,					bias=__lowerCAmelCase    )
       UpperCamelCase__    :     str        =						emb.weight.data
       return lin_layer
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase="uclanlp/plbart-base"      ,					__lowerCAmelCase=False      ,					__lowerCAmelCase=False    )				->			Optional[int]:
       UpperCamelCase__    :     Any        =						torch.load(__lowerCAmelCase      ,					map_location="cpu"    )["model"]
       remove_ignore_keys_(__lowerCAmelCase    )
       UpperCamelCase__    :     Union[str, Any]        =						state_dict["encoder.embed_tokens.weight"].shape[0]
  
       UpperCamelCase__    :     int        =						PLBartConfig.from_pretrained(__lowerCAmelCase      ,					vocab_size=__lowerCAmelCase    )
  
       UpperCamelCase__    :     Union[str, Any]        =						state_dict["decoder.embed_tokens.weight"]
       if not classification:
            UpperCamelCase__    :     Optional[int]        =						PLBartForConditionalGeneration(__lowerCAmelCase    )
            model.model.load_state_dict(__lowerCAmelCase    )
            if finetuned:
                 UpperCamelCase__    :     List[Any]        =						make_linear_from_emb(model.model.shared    )
  
       else:
            UpperCamelCase__    :     List[Any]        =						{}
            for key, value in state_dict.copy().items():
                 if key.startswith("classification_heads.sentence_classification_head"    ):
                      UpperCamelCase__    :     List[str]        =						value
                      state_dict.pop(__lowerCAmelCase    )
            UpperCamelCase__    :     List[Any]        =						PLBartForSequenceClassification(__lowerCAmelCase    )
            model.model.load_state_dict(__lowerCAmelCase    )
            model.classification_head.load_state_dict(__lowerCAmelCase    )
  
       return model
  
  
  if __name__ == "__main__":
   lowerCamelCase			:							Union[str, Any]       =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('''fairseq_path''', type=str, help='''model.pt on local filesystem.''')
   parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument(
       '''--hf_config''',
       default='''uclanlp/plbart-base''',
       type=str,
       help='''Which huggingface architecture to use: plbart-base''',
   )
   parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
   parser.add_argument(
       '''--classification''', action='''store_true''', help='''whether the model is a classification checkpoint'''
   )
   lowerCamelCase			:							Optional[Any]       =parser.parse_args()
   lowerCamelCase			:							Tuple       =convert_fairseq_plbart_checkpoint_from_disk(
       args.fairseq_path,
       hf_config_path=args.hf_config,
       finetuned=args.finetuned,
       classification=args.classification,
   )
   model.save_pretrained(args.pytorch_dump_folder_path)
style_context: 
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			str:
       if len(__lowerCAmelCase    ) == 0 or not all(len(__lowerCAmelCase    ) == 2 for pair in coordinates    ):
            raise ValueError("The program cannot work out a fitting polynomial."    )
  
       if len({tuple(__lowerCAmelCase    ) for pair in coordinates}    ) != len(__lowerCAmelCase    ):
            raise ValueError("The program cannot work out a fitting polynomial."    )
  
       UpperCamelCase__    :     List[str]        =						{x for x, _ in coordinates}
       if len(__lowerCAmelCase    ) == 1:
            return f'x={coordinates[0][0]}'
  
       if len(__lowerCAmelCase    ) != len(__lowerCAmelCase    ):
            raise ValueError("The program cannot work out a fitting polynomial."    )
  
       UpperCamelCase__    :     Any        =						len(__lowerCAmelCase    )
  
       UpperCamelCase__    :     str        =						0
       UpperCamelCase__    :     list[list[float]]        =						[]
       # put the x and x to the power values in a matrix
       while count_of_line < x:
            UpperCamelCase__    :     int        =						0
            UpperCamelCase__    :     List[Any]        =						coordinates[count_of_line][0]
            UpperCamelCase__    :     list[float]        =						[]
            while count_in_line < x:
                 count_line.append(a ** (x - (count_in_line + 1))    )
                 count_in_line += 1
            matrix.append(__lowerCAmelCase    )
            count_of_line += 1
  
       UpperCamelCase__    :     List[Any]        =						0
       # put the y values into a vector
       UpperCamelCase__    :     list[float]        =						[]
       while count_of_line < x:
            vector.append(coordinates[count_of_line][1]    )
            count_of_line += 1
  
       UpperCamelCase__    :     Tuple        =						0
  
       while count < x:
            UpperCamelCase__    :     Dict        =						0
            while zahlen < x:
                 if count == zahlen:
                      zahlen += 1
                 if zahlen == x:
                      break
                 UpperCamelCase__    :     Optional[Any]        =						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
  
       UpperCamelCase__    :     Tuple        =						0
       # make solutions
       UpperCamelCase__    :     list[str]        =						[]
       while count < x:
            solution.append(str(vector[count] / matrix[count][count]    )    )
            count += 1
  
       UpperCamelCase__    :     int        =						0
       UpperCamelCase__    :     List[str]        =						"f(x)="
  
       while count < x:
            UpperCamelCase__    :     list[str]        =						solution[count].split("E"    )
            if len(__lowerCAmelCase    ) > 1:
                 UpperCamelCase__    :     Union[str, Any]        =						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, 11]]))
   print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
   print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))