


'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 							UpperCamelCase_     (       A__   :  str   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  [
      """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_     (       A__   :  List[str]   ):
  '''simple docstring'''





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







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  List[Any]="facebook/mbart-large-en-ro"				,       A__   :  Optional[Any]=False				,       A__   :  Union[str, Any]=False   ):
  '''simple docstring'''





  lowerCAmelCase_     : int     =  torch.load(A__				,       map_location="""cpu"""   )["""model"""]
  remove_ignore_keys_(A__   )
  lowerCAmelCase_     : List[Any]     =  state_dict["""encoder.embed_tokens.weight"""].shape[0]

  lowerCAmelCase_     : Any     =  MBartConfig.from_pretrained(A__				,       vocab_size=A__   )
  if mbart_aa and finetuned:
    lowerCAmelCase_     : int     =  """relu"""

  lowerCAmelCase_     : List[Any]     =  state_dict["""decoder.embed_tokens.weight"""]
  lowerCAmelCase_     : str     =  MBartForConditionalGeneration(A__   )
  model.model.load_state_dict(A__   )

  if finetuned:
    lowerCAmelCase_     : Optional[Any]     =  make_linear_from_emb(model.model.shared   )

  return model


if __name__ == "__main__":
  __A   :    int											= 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")
  __A   :    Optional[int]											= parser.parse_args()
  __A   :    List[str]											= 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

import numpy as np







def 							UpperCamelCase_     (       A__   :  np.ndarray   ):
  '''simple docstring'''





  lowerCAmelCase_,				lowerCAmelCase_     : List[str]     =  np.shape(A__   )
  if rows != columns:
    lowerCAmelCase_     : List[str]     =  (
        """'table' has to be of square shaped array but got a """
        f'{rows}x{columns} array:\n{table}'
    )
    raise ValueError(A__   )

  lowerCAmelCase_     : Optional[int]     =  np.zeros((rows, columns)   )
  lowerCAmelCase_     : Tuple     =  np.zeros((rows, columns)   )
  for i in range(A__   ):
    for j in range(A__   ):
      lowerCAmelCase_     : str     =  sum(lower[i][k] * upper[k][j] for k in range(A__   )   )
      if upper[j][j] == 0:
        raise ArithmeticError("""No LU decomposition exists"""   )
      lowerCAmelCase_     : Union[str, Any]     =  (table[i][j] - total) / upper[j][j]
    lowerCAmelCase_     : Dict     =  1
    for j in range(A__				,       A__   ):
      lowerCAmelCase_     : str     =  sum(lower[i][k] * upper[k][j] for k in range(A__   )   )
      lowerCAmelCase_     : List[str]     =  table[i][j] - total
  return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()



