


'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration





def lowerCamelCase__      (						__lowerCamelCase	:							Tuple							):







   '''simple docstring'''




   _UpperCAmelCase       :       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(__lowerCamelCase	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							List[Any]							):







   '''simple docstring'''




   _UpperCAmelCase   ,	_UpperCAmelCase       :       Any							   =emb.weight.shape
   _UpperCAmelCase       :       Dict							   =nn.Linear(__lowerCamelCase	,   __lowerCamelCase	,   bias=__lowerCamelCase							)
   _UpperCAmelCase       :       Any							   =emb.weight.data
   return lin_layer





def lowerCamelCase__      (						__lowerCamelCase	:							str	,   __lowerCamelCase	:							List[Any]="facebook/mbart-large-en-ro"	,   __lowerCamelCase	:							List[Any]=False	,   __lowerCamelCase	:							Union[str, Any]=False							):







   '''simple docstring'''




   _UpperCAmelCase       :       Dict							   =torch.load(__lowerCamelCase	,   map_location='cpu'							)['model']
   remove_ignore_keys_(__lowerCamelCase							)
   _UpperCAmelCase       :       Any							   =state_dict['encoder.embed_tokens.weight'].shape[0]

   _UpperCAmelCase       :       int							   =MBartConfig.from_pretrained(__lowerCamelCase	,   vocab_size=__lowerCamelCase							)
   if mbart_aa and finetuned:
      _UpperCAmelCase       :       Union[str, Any]							   ='relu'

   _UpperCAmelCase       :       List[Any]							   =state_dict['decoder.embed_tokens.weight']
   _UpperCAmelCase       :       Union[str, Any]							   =MBartForConditionalGeneration(__lowerCamelCase							)
   model.model.load_state_dict(__lowerCamelCase							)

   if finetuned:
      _UpperCAmelCase       :       int							   =make_linear_from_emb(model.model.shared							)

   return model


if __name__ == "__main__":
 lowercase							=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')
 lowercase							=parser.parse_args()
 lowercase							=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 lowerCamelCase__      (						__lowerCamelCase	:							list[int]							):







   '''simple docstring'''




   if not nums:
      return 0
   _UpperCAmelCase       :       Tuple							   =nums[0]
   _UpperCAmelCase       :       int							   =0
   for num in nums[1:]:
      _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =(
          max_excluding + num,
          max(__lowerCamelCase	,   __lowerCamelCase							),
      )
   return max(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

