


'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






     __UpperCAmelCase			:				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(lowercase__    ,      lowercase__						)




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[int]													=    emb.weight.shape
     __UpperCAmelCase			:				Tuple													=    nn.Linear(lowercase__    ,      lowercase__    ,      bias=lowercase__						)
     __UpperCAmelCase			:				Dict													=    emb.weight.data
     return lin_layer




def 			_a			(		_lowercase      :			int    ,      _lowercase      :			Tuple="facebook/mbart-large-en-ro"    ,      _lowercase      :			int=False    ,      _lowercase      :			Dict=False						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    torch.load(lowercase__    ,      map_location='''cpu'''						)['''model''']
     remove_ignore_keys_(lowercase__						)
     __UpperCAmelCase			:				Optional[int]													=    state_dict['''encoder.embed_tokens.weight'''].shape[0]

     __UpperCAmelCase			:				Optional[int]													=    MBartConfig.from_pretrained(lowercase__    ,      vocab_size=lowercase__						)
     if mbart_aa and finetuned:
          __UpperCAmelCase			:				Dict													=    '''relu'''

     __UpperCAmelCase			:				Union[str, Any]													=    state_dict['''decoder.embed_tokens.weight''']
     __UpperCAmelCase			:				Optional[Any]													=    MBartForConditionalGeneration(lowercase__						)
     model.model.load_state_dict(lowercase__						)

     if finetuned:
          __UpperCAmelCase			:				Any													=    make_linear_from_emb(model.model.shared						)

     return model


if __name__ == "__main__":
      __UpperCAmelCase    :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")
      __UpperCAmelCase    :Optional[Any]				       =     parser.parse_args()
      __UpperCAmelCase    :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'''



def 			_a			(		_lowercase      :			int = 4000000						):





     '''simple docstring'''






     __UpperCAmelCase			:				Tuple													=    []
     __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    0, 1
     while b <= n:
          if b % 2 == 0:
               even_fibs.append(_lowercase						)
          __UpperCAmelCase							,     __UpperCAmelCase			:				Dict													=    b, a + b
     return sum(_lowercase						)


if __name__ == "__main__":
      print(f"""{solution() = }""")