





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   )      ->   Tuple:
 __lowerCAmelCase					:	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(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   )      ->   Union[str, Any]:
 __lowerCAmelCase ,					__lowerCAmelCase					:	Optional[Any]      =       emb.weight.shape
 __lowerCAmelCase					:	Optional[int]      =       nn.Linear(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   bias=SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[Any]      =       emb.weight.data
 return lin_layer






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:Tuple="facebook/mbart-large-en-ro"   ,   SCREAMING_SNAKE_CASE		:Optional[int]=False   ,   SCREAMING_SNAKE_CASE		:Union[str, Any]=False   )      ->   List[str]:
 __lowerCAmelCase					:	List[str]      =       torch.load(SCREAMING_SNAKE_CASE   ,   map_location="""cpu"""   )["""model"""]
 remove_ignore_keys_(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       state_dict["""encoder.embed_tokens.weight"""].shape[0]

 __lowerCAmelCase					:	List[str]      =       MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE   ,   vocab_size=SCREAMING_SNAKE_CASE   )
 if mbart_aa and finetuned:
  __lowerCAmelCase					:	Optional[int]      =       """relu"""

 __lowerCAmelCase					:	Dict      =       state_dict["""decoder.embed_tokens.weight"""]
 __lowerCAmelCase					:	Union[str, Any]      =       MBartForConditionalGeneration(SCREAMING_SNAKE_CASE   )
 model.model.load_state_dict(SCREAMING_SNAKE_CASE   )

 if finetuned:
  __lowerCAmelCase					:	Dict      =       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)






from math import isqrt






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   list[int]:
 __lowerCAmelCase					:	Tuple      =       [True] * max_number
 for i in range(2   ,   isqrt(max_number - 1   ) + 1   ):
  if is_prime[i]:
   for j in range(i**2   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Tuple      =       False

 return [i for i in range(2   ,   SCREAMING_SNAKE_CASE   ) if is_prime[i]]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int = 10**8   )      ->   int:
 __lowerCAmelCase					:	int      =       calculate_prime_numbers(max_number // 2   )

 __lowerCAmelCase					:	List[Any]      =       0
 __lowerCAmelCase					:	List[str]      =       0
 __lowerCAmelCase					:	List[Any]      =       len(SCREAMING_SNAKE_CASE   ) - 1
 while left <= right:
  while prime_numbers[left] * prime_numbers[right] >= max_number:
   right -= 1
  semiprimes_count += right - left + 1
  left += 1

 return semiprimes_count


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