


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def  a   (lowerCAmelCase__   ):
   __a					=  [
       """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  a   (lowerCAmelCase__   ):
   __a	,    __a					=  emb.weight.shape
   __a					=  nn.Linear(lowerCAmelCase__		,				lowerCAmelCase__		,				bias=lowerCAmelCase__   )
   __a					=  emb.weight.data
   return lin_layer




def  a   (lowerCAmelCase__		,				lowerCAmelCase__="facebook/mbart-large-en-ro"		,				lowerCAmelCase__=False		,				lowerCAmelCase__=False   ):
   __a					=  torch.load(lowerCAmelCase__		,				map_location="""cpu"""   )["""model"""]
   remove_ignore_keys_(lowerCAmelCase__   )
   __a					=  state_dict["""encoder.embed_tokens.weight"""].shape[0]

   __a					=  MBartConfig.from_pretrained(lowerCAmelCase__		,				vocab_size=lowerCAmelCase__   )
   if mbart_aa and finetuned:
      __a					=  """relu"""

   __a					=  state_dict["""decoder.embed_tokens.weight"""]
   __a					=  MBartForConditionalGeneration(lowerCAmelCase__   )
   model.model.load_state_dict(lowerCAmelCase__   )

   if finetuned:
      __a					=  make_linear_from_emb(model.model.shared   )

   return model


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE							    =							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')
  SCREAMING_SNAKE_CASE							    =							parser.parse_args()
  SCREAMING_SNAKE_CASE							    =							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)







import math




def  a   (lowerCAmelCase__   ):

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5		,				int(math.sqrt(lowerCAmelCase__   ) + 1   )		,				6   ):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True




def  a   (lowerCAmelCase__ = 10_001   ):

   try:
      __a					=  int(lowerCAmelCase__   )
   except (TypeError, ValueError):
      raise TypeError("""Parameter nth must be int or castable to int."""   ) from None
   if nth <= 0:
      raise ValueError("""Parameter nth must be greater than or equal to one."""   )
   __a					=  []
   __a					=  2
   while len(lowerCAmelCase__   ) < nth:
      if is_prime(lowerCAmelCase__   ):
         primes.append(lowerCAmelCase__   )
         num += 1
      else:
         num += 1
   return primes[len(lowerCAmelCase__   ) - 1]


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



