
'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	_snake_case						(      A_       :    Optional[Any]   ):
  """simple docstring"""







  a_      :	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(A_     ,		A_   )






def 	_snake_case						(      A_       :    Optional[Any]   ):
  """simple docstring"""







  a_      :	Optional[Any]      =							emb.weight.shape
  a_      :	Optional[Any]      =							nn.Linear(A_     ,		A_     ,		bias=A_   )
  a_      :	List[Any]      =							emb.weight.data
  return lin_layer






def 	_snake_case						(      A_       :    Optional[int]     ,		A_       :    str="facebook/mbart-large-en-ro"     ,		A_       :    int=False     ,		A_       :    Dict=False   ):
  """simple docstring"""







  a_      :	List[Any]      =							torch.load(A_     ,		map_location="""cpu"""   )["""model"""]
  remove_ignore_keys_(A_   )
  a_      :	Tuple      =							state_dict["""encoder.embed_tokens.weight"""].shape[0]

  a_      :	Optional[int]      =							MBartConfig.from_pretrained(A_     ,		vocab_size=A_   )
  if mbart_aa and finetuned:
    a_      :	List[str]      =							"""relu"""

  a_      :	Tuple      =							state_dict["""decoder.embed_tokens.weight"""]
  a_      :	Dict      =							MBartForConditionalGeneration(A_   )
  model.model.load_state_dict(A_   )

  if finetuned:
    a_      :	Union[str, Any]      =							make_linear_from_emb(model.model.shared   )

  return model


if __name__ == "__main__":
    __snake_case:  List[str]												=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a 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="facebook/mbart-large-cc25",
        type=str,
        help="Which huggingface architecture to use: mbart-large",
    )
    parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
    parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
    __snake_case:  Optional[Any]												=			parser.parse_args()
    __snake_case:  Any												=			convert_fairseq_mbart_checkpoint_from_disk(
        args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
    )
    model.save_pretrained(args.pytorch_dump_folder_path)




'''simple docstring'''

from __future__ import annotations






def 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  a_      :	Optional[Any]      =							2
  a_      :	int      =							[]
  while i * i <= n:
    if n % i:
      i += 1
    else:
      n //= i
      factors.append(A_   )
  if n > 1:
    factors.append(A_   )
  return factors


if __name__ == "__main__":
    import doctest

    doctest.testmod()


