


'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def    UpperCAmelCase					(						lowerCamelCase_     :Dict       ):







		'''simple docstring'''
		snake_case_						:							int					=  [
		    """encoder.version""",
		    """decoder.version""",
		    """model.encoder.version""",
		    """model.decoder.version""",
		    """_float_tensor""",
		    """decoder.output_projection.weight""",
		]
		for k in ignore_keys:
				state_dict.pop(__A ,					__A       )





def    UpperCAmelCase					(						lowerCamelCase_     :Any       ):







		'''simple docstring'''
		snake_case_						:							Dict					=  emb.weight.shape
		snake_case_						:							List[Any]					=  nn.Linear(__A ,					__A ,					bias=__A       )
		snake_case_						:							str					=  emb.weight.data
		return lin_layer





def    UpperCAmelCase					(						lowerCamelCase_     :str ,					lowerCamelCase_     :Dict="uclanlp/plbart-base" ,					lowerCamelCase_     :Optional[Any]=False ,					lowerCamelCase_     :Optional[int]=False       ):







		'''simple docstring'''
		snake_case_						:							Any					=  torch.load(__A ,					map_location="""cpu"""       )["""model"""]
		remove_ignore_keys_(__A       )
		snake_case_						:							List[str]					=  state_dict["""encoder.embed_tokens.weight"""].shape[0]

		snake_case_						:							Optional[Any]					=  PLBartConfig.from_pretrained(__A ,					vocab_size=__A       )

		snake_case_						:							Any					=  state_dict["""decoder.embed_tokens.weight"""]
		if not classification:
				snake_case_						:							Any					=  PLBartForConditionalGeneration(__A       )
				model.model.load_state_dict(__A       )
				if finetuned:
						snake_case_						:							List[Any]					=  make_linear_from_emb(model.model.shared       )

		else:
				snake_case_						:							Any					=  {}
				for key, value in state_dict.copy().items():
						if key.startswith("""classification_heads.sentence_classification_head"""       ):
								snake_case_						:							Tuple					=  value
								state_dict.pop(__A       )
				snake_case_						:							str					=  PLBartForSequenceClassification(__A       )
				model.model.load_state_dict(__A       )
				model.classification_head.load_state_dict(__A       )

		return model


if __name__ == "__main__":
							__A		:		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'
							)
							__A		:		Tuple   =   parser.parse_args()
							__A		:		int   =   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)


from math import sqrt





def 	snake_case_      (__A     :						int     )			->	list[int]:
   __lowerCAmelCase  :			List[str]					    =						[1]
   for i in range(2					,   int(sqrt(__A     )     ) + 1					,   1     ):
      if number % i == 0:
         values.append(__A     )
         if int(number // i     ) != i:
            values.append(int(number // i     )     )
   return sorted(__A     )





def 	snake_case_      (__A     :						int     )			->	bool:
   return sum(factors(__A     )     ) > n





def 	snake_case_      (__A     :						int     )			->	bool:
   __lowerCAmelCase  :			Any					    =						factors(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						len(__A     )
   __lowerCAmelCase  :			str					    =						[[0 for i in range(number + 1     )] for j in range(r + 1     )]
   for i in range(r + 1     ):
      __lowerCAmelCase  :			Tuple					    =						True

   for i in range(1					,   number + 1     ):
      __lowerCAmelCase  :			List[str]					    =						False

   for i in range(1					,   r + 1     ):
      for j in range(1					,   number + 1     ):
         if j < values[i - 1]:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j]
         else:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0





def 	snake_case_      (__A     :						int     )			->	bool:
   return abundant(__A     ) and not semi_perfect(__A     )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F'{number} is {"" if weird(number) else "not "}weird.')


