





import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification







def       lowerCAmelCase__(	lowercase       :    List[str]				) ->    Any:
  __snake_case   :				Optional[Any]        =       [
      "encoder.version",
      "decoder.version",
      "model.encoder.version",
      "model.decoder.version",
      "_float_tensor",
      "decoder.output_projection.weight",
  ]
  for k in ignore_keys:
    state_dict.pop(lowercase   ,     lowercase				)







def       lowerCAmelCase__(	lowercase       :    Optional[Any]				) ->    str:
  __snake_case    ,   __snake_case   :				List[Any]        =       emb.weight.shape
  __snake_case   :				Any        =       nn.Linear(lowercase   ,     lowercase   ,     bias=lowercase				)
  __snake_case   :				str        =       emb.weight.data
  return lin_layer







def       lowerCAmelCase__(	lowercase       :    Union[str, Any]   ,     lowercase       :    Tuple="uclanlp/plbart-base"   ,     lowercase       :    Tuple=False   ,     lowercase       :    Union[str, Any]=False				) ->    Optional[int]:
  __snake_case   :				Any        =       torch.load(lowercase   ,     map_location="cpu"				)["model"]
  remove_ignore_keys_(lowercase				)
  __snake_case   :				Union[str, Any]        =       state_dict["encoder.embed_tokens.weight"].shape[0]

  __snake_case   :				str        =       PLBartConfig.from_pretrained(lowercase   ,     vocab_size=lowercase				)

  __snake_case   :				int        =       state_dict["decoder.embed_tokens.weight"]
  if not classification:
    __snake_case   :				Optional[Any]        =       PLBartForConditionalGeneration(lowercase				)
    model.model.load_state_dict(lowercase				)
    if finetuned:
      __snake_case   :				Optional[int]        =       make_linear_from_emb(model.model.shared				)

  else:
    __snake_case   :				int        =       {}
    for key, value in state_dict.copy().items():
      if key.startswith("classification_heads.sentence_classification_head"				):
        __snake_case   :				Any        =       value
        state_dict.pop(lowercase				)
    __snake_case   :				Dict        =       PLBartForSequenceClassification(lowercase				)
    model.model.load_state_dict(lowercase				)
    model.classification_head.load_state_dict(lowercase				)

  return model


if __name__ == "__main__":
     _UpperCamelCase       = 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'''
     )
     _UpperCamelCase       = parser.parse_args()
     _UpperCamelCase       = 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       lowerCAmelCase__(	lowercase       :    str				) ->    int:
  __snake_case   :				List[str]        =       str(lowercase				).strip()
  if not oct_string:
    raise ValueError("Empty string was passed to the function"				)
  __snake_case   :				int        =       oct_string[0] == "-"
  if is_negative:
    __snake_case   :				Union[str, Any]        =       oct_string[1:]
  if not oct_string.isdigit() or not all(0 <= int(lowercase				) <= 7 for char in oct_string				):
    raise ValueError("Non-octal value was passed to the function"				)
  __snake_case   :				List[Any]        =       0
  for char in oct_string:
    __snake_case   :				Optional[Any]        =       8 * decimal_number + int(lowercase				)
  if is_negative:
    __snake_case   :				Any        =       -decimal_number
  return decimal_number


if __name__ == "__main__":
     from doctest import testmod

     testmod()

