"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple	)       ->      Dict:
  _lowerCAmelCase						:       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(_lowerCamelCase			,_lowerCamelCase	)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple	)       ->      Union[str, Any]:
  _lowerCAmelCase			,	_lowerCAmelCase						:       List[Any]  			=     emb.weight.shape
  _lowerCAmelCase						:       Any  			=     nn.Linear(_lowerCamelCase			,_lowerCamelCase			,bias=_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[Any]  			=     emb.weight.data
  return lin_layer





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict			,_lowerCamelCase						:       Any="facebook/mbart-large-en-ro"			,_lowerCamelCase						:       List[Any]=False			,_lowerCamelCase						:       Any=False	)       ->      int:
  _lowerCAmelCase						:       Dict  			=     torch.load(_lowerCamelCase			,map_location="""cpu"""	)["""model"""]
  remove_ignore_keys_(_lowerCamelCase	)
  _lowerCAmelCase						:       List[str]  			=     state_dict["""encoder.embed_tokens.weight"""].shape[0]

  _lowerCAmelCase						:       Any  			=     MBartConfig.from_pretrained(_lowerCamelCase			,vocab_size=_lowerCamelCase	)
  if mbart_aa and finetuned:
    _lowerCAmelCase						:       Any  			=     """relu"""

  _lowerCAmelCase						:       Tuple  			=     state_dict["""decoder.embed_tokens.weight"""]
  _lowerCAmelCase						:       Optional[int]  			=     MBartForConditionalGeneration(_lowerCamelCase	)
  model.model.load_state_dict(_lowerCamelCase	)

  if finetuned:
    _lowerCAmelCase						:       str  			=     make_linear_from_emb(model.model.shared	)

  return model


if __name__ == "__main__":
     _a      :		Optional[Any]           =   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[Any]           =   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


def  snake_case_     (					_lowerCAmelCase  :						list[int | float]   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int  )      ->   int | float:
							if len(_lowerCAmelCase  ) == 0:
														raise ValueError('''find_max() arg is an empty sequence'''  )
							if (
							    left >= len(_lowerCAmelCase  )
							    or left < -len(_lowerCAmelCase  )
							    or right >= len(_lowerCAmelCase  )
							    or right < -len(_lowerCAmelCase  )
							):
														raise IndexError('''list index out of range'''  )
							if left == right:
														return nums[left]
							UpperCAmelCase :       List[Any] 							=		(left + right) >> 1  # the middle
							UpperCAmelCase :       Optional[Any] 							=		find_max(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )  # find max in range[left, mid]
							UpperCAmelCase :       Dict 							=		find_max(_lowerCAmelCase   ,      mid + 1   ,      _lowerCAmelCase  )  # find max in range[mid + 1, right]

							return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





