

"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def 			_lowercase       (     __lowerCAmelCase				)							->					Dict:
      SCREAMING_SNAKE_CASE__ :   str   		=						[
          """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 			_lowercase       (     __lowerCAmelCase				)							->					str:
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__ :   int   		=						emb.weight.shape
      SCREAMING_SNAKE_CASE__ :   int   		=						nn.Linear(lowercase__	,       lowercase__	,       bias=lowercase__				)
      SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						emb.weight.data
      return lin_layer




def 			_lowercase       (     __lowerCAmelCase	,       __lowerCAmelCase="facebook/mbart-large-en-ro"	,       __lowerCAmelCase=False	,       __lowerCAmelCase=False				)							->					List[Any]:
      SCREAMING_SNAKE_CASE__ :   List[str]   		=						torch.load(lowercase__	,       map_location="""cpu"""				)["""model"""]
      remove_ignore_keys_(lowercase__				)
      SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						state_dict["""encoder.embed_tokens.weight"""].shape[0]

      SCREAMING_SNAKE_CASE__ :   str   		=						MBartConfig.from_pretrained(lowercase__	,       vocab_size=lowercase__				)
      if mbart_aa and finetuned:
            SCREAMING_SNAKE_CASE__ :   Tuple   		=						"""relu"""

      SCREAMING_SNAKE_CASE__ :   Dict   		=						state_dict["""decoder.embed_tokens.weight"""]
      SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						MBartForConditionalGeneration(lowercase__				)
      model.model.load_state_dict(lowercase__				)

      if finetuned:
            SCREAMING_SNAKE_CASE__ :   List[Any]   		=						make_linear_from_emb(model.model.shared				)

      return model


if __name__ == "__main__":
  a       :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")
  a       :Union[str, Any]     =			parser.parse_args()
  a       :Optional[int]     =			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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__magic_name__						: Tuple    =				{}


try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: str    =				['MLukeTokenizer']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mluke import MLukeTokenizer


else:
					import sys

					__magic_name__						: int    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




