

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification



def    snake_case_(_UpperCamelCase  )							->			Optional[Any]:



  """simple docstring"""






  _snake_case		=							[
      '''encoder.version''',
      '''decoder.version''',
      '''model.encoder.version''',
      '''model.decoder.version''',
      '''_float_tensor''',
      '''decoder.output_projection.weight''',
  ]
  for k in ignore_keys:
    state_dict.pop(_UpperCamelCase				, _UpperCamelCase  )



def    snake_case_(_UpperCamelCase  )							->			Dict:



  """simple docstring"""






  _snake_case,  _snake_case		=							emb.weight.shape
  _snake_case		=							nn.Linear(_UpperCamelCase				, _UpperCamelCase				, bias=_UpperCamelCase  )
  _snake_case		=							emb.weight.data
  return lin_layer



def    snake_case_(_UpperCamelCase				, _UpperCamelCase="uclanlp/plbart-base"				, _UpperCamelCase=False				, _UpperCamelCase=False  )							->			Dict:



  """simple docstring"""






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

  _snake_case		=							PLBartConfig.from_pretrained(_UpperCamelCase				, vocab_size=_UpperCamelCase  )

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

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

  return model


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




import numpy as np



def    snake_case_(_UpperCamelCase  )							->			Any:



  """simple docstring"""






  _snake_case		=							np.exp(_UpperCamelCase  )

  # Add up the all the exponentials
  _snake_case		=							np.sum(_UpperCamelCase  )

  # Divide every exponent by the sum of all exponents
  _snake_case		=							exponent_vector / sum_of_exponents

  return softmax_vector


if __name__ == "__main__":
 print(softmax((0,)))

