


'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification

def _SCREAMING_SNAKE_CASE				(A	)	->	Any:






       """simple docstring"""







       lowercase__				      =			[
           '''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 _SCREAMING_SNAKE_CASE				(A	)	->	Optional[int]:






       """simple docstring"""







       lowercase__      ,lowercase__				      =			emb.weight.shape
       lowercase__				      =			nn.Linear(A  ,			A  ,			bias=A	)
       lowercase__				      =			emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE				(A  ,			A="uclanlp/plbart-base"  ,			A=False  ,			A=False	)	->	Union[str, Any]:






       """simple docstring"""







       lowercase__				      =			torch.load(A  ,			map_location='''cpu'''	)['''model''']
       remove_ignore_keys_(A	)
       lowercase__				      =			state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__				      =			PLBartConfig.from_pretrained(A  ,			vocab_size=A	)

       lowercase__				      =			state_dict['''decoder.embed_tokens.weight''']
       if not classification:
              lowercase__				      =			PLBartForConditionalGeneration(A	)
              model.model.load_state_dict(A	)
              if finetuned:
                     lowercase__				      =			make_linear_from_emb(model.model.shared	)

       else:
              lowercase__				      =			{}
              for key, value in state_dict.copy().items():
                     if key.startswith('''classification_heads.sentence_classification_head'''	):
                            lowercase__				      =			value
                            state_dict.pop(A	)
              lowercase__				      =			PLBartForSequenceClassification(A	)
              model.model.load_state_dict(A	)
              model.classification_head.load_state_dict(A	)

       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							:       int  						=     parser.parse_args()
 lowerCamelCase							:       Optional[Any]  						=     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)








'''simple docstring'''

def _SCREAMING_SNAKE_CASE				(A	)	->	list:






       """simple docstring"""







       lowercase__				      =			len(A	)

       # Set the gap value to be decreased by a factor of 1.3
       # after each iteration
       lowercase__				      =			1.3

       # Continue sorting until the gap is 1
       while gap > 1:
              # Decrease the gap value
              lowercase__				      =			int(gap / shrink	)

              # Sort the elements using insertion sort
              for i in range(A  ,			len(A	)	):
                     lowercase__				      =			collection[i]
                     lowercase__				      =			i
                     while j >= gap and collection[j - gap] > temp:
                            lowercase__				      =			collection[j - gap]
                            j -= gap
                     lowercase__				      =			temp

       return collection


if __name__ == "__main__":
 import doctest

 doctest.testmod()




