






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)







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]]))