import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def       lowerCAmelCase_     (		lowercase:							Optional[int]		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= [
     '''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       lowerCAmelCase_     (		lowercase:							Union[str, Any]		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase	,							_UpperCamelCase:     int							= emb.weight.shape
 _UpperCamelCase:     Tuple							= nn.Linear(lowercase  ,					lowercase  ,					bias=lowercase		)
 _UpperCamelCase:     str							= emb.weight.data
 return lin_layer




def       lowerCAmelCase_     (		lowercase:							List[str]  ,					lowercase:							Tuple="facebook/mbart-large-en-ro"  ,					lowercase:							str=False  ,					lowercase:							List[str]=False		)    ->				str:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= torch.load(lowercase  ,					map_location='''cpu'''		)['''model''']
 remove_ignore_keys_(lowercase		)
 _UpperCamelCase:     Union[str, Any]							= state_dict['''encoder.embed_tokens.weight'''].shape[0]

 _UpperCamelCase:     Tuple							= MBartConfig.from_pretrained(lowercase  ,					vocab_size=lowercase		)
 if mbart_aa and finetuned:
  _UpperCamelCase:     Optional[Any]							= '''relu'''

 _UpperCamelCase:     List[Any]							= state_dict['''decoder.embed_tokens.weight''']
 _UpperCamelCase:     Optional[int]							= MBartForConditionalGeneration(lowercase		)
 model.model.load_state_dict(lowercase		)

 if finetuned:
  _UpperCamelCase:     List[str]							= make_linear_from_emb(model.model.shared		)

 return model


if __name__ == "__main__":
  UpperCAmelCase_			    =							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_			    =							parser.parse_args()
  UpperCAmelCase_			    =							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)
def       lowerCAmelCase_     (		lowercase:							int = 10**9		)    ->				int:







 '''simple docstring'''
 _UpperCamelCase:     List[Any]							= 1
 _UpperCamelCase:     Dict							= 2

 _UpperCamelCase:     Tuple							= 0
 _UpperCamelCase:     int							= 0
 _UpperCamelCase:     Dict							= 0
 while perimeter <= max_perimeter:
  perimeters_sum += perimeter

  prev_value += 2 * value
  value += prev_value

  _UpperCamelCase:     str							= 2 * value + 2 if i % 2 == 0 else 2 * value - 2
  i += 1

 return perimeters_sum


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