






"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _a			(		_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case				,	_snake_case="facebook/mbart-large-en-ro"				,	_snake_case=False				,	_snake_case=False							):







      """simple docstring"""






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

      UpperCAmelCase          =	MBartConfig.from_pretrained(_snake_case				,	vocab_size=_snake_case							)
      if mbart_aa and finetuned:
            UpperCAmelCase          =	"""relu"""

      UpperCAmelCase          =	state_dict["""decoder.embed_tokens.weight"""]
      UpperCAmelCase          =	MBartForConditionalGeneration(_snake_case							)
      model.model.load_state_dict(_snake_case							)

      if finetuned:
            UpperCAmelCase          =	make_linear_from_emb(model.model.shared							)

      return model


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







import numpy as np

def _a			(		_snake_case							):







      """simple docstring"""






      return 1 / (1 + np.exp(-vector							))


if __name__ == "__main__":
  import doctest

  doctest.testmod()



