'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[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(_A  ,			_A    )







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__				,      UpperCAmelCase__							:				int   				=  emb.weight.shape
   UpperCAmelCase__							:				Dict   				=  nn.Linear(_A  ,			_A  ,			bias=_A    )
   UpperCAmelCase__							:				Dict   				=  emb.weight.data
   return lin_layer







def __UpperCamelCase(   _A					:				str  ,			_A					:				int="facebook/mbart-large-en-ro"  ,			_A					:				List[str]=False  ,			_A					:				str=False    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  torch.load(_A  ,			map_location='''cpu'''    )['''model''']
   remove_ignore_keys_(_A    )
   UpperCAmelCase__							:				Optional[int]   				=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase__							:				List[str]   				=  MBartConfig.from_pretrained(_A  ,			vocab_size=_A    )
   if mbart_aa and finetuned:
      UpperCAmelCase__							:				Optional[int]   				=  '''relu'''

   UpperCAmelCase__							:				int   				=  state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__							:				List[str]   				=  MBartForConditionalGeneration(_A    )
   model.model.load_state_dict(_A    )

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

   return model


if __name__ == "__main__":
  UpperCamelCase__     :	int				=			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[int]				=			parser.parse_args()
  UpperCamelCase__     :	Union[str, Any]				=			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 __UpperCamelCase(   _A					:				int    ):
   '''simple docstring'''

   if length <= 0 or not isinstance(_A  ,			_A    ):
      raise ValueError('''Length must be a positive integer.'''    )
   return [n * (2 * n - 1) for n in range(_A    )]


if __name__ == "__main__":
  print(hexagonal_numbers(length=5))
  print(hexagonal_numbers(length=10))




