

'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			Optional[int]:
					lowerCamelCase_  				=      [
					    'encoder.version',
					    'decoder.version',
					    'model.encoder.version',
					    'model.decoder.version',
					    'decoder.output_projection.weight',
					    '_float_tensor',
					    'encoder.embed_positions._float_tensor',
					    'decoder.embed_positions._float_tensor',
					]
					for k in ignore_keys:
										state_dict.pop(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_	)







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			int:
					lowerCamelCase_				,lowerCamelCase_  				=      emb.weight.shape
					lowerCamelCase_  				=      nn.Linear(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,bias=SCREAMING_SNAKE_CASE_	)
					lowerCamelCase_  				=      emb.weight.data
					return lin_layer







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			Any:
					lowerCamelCase_  				=      torch.load(SCREAMING_SNAKE_CASE_    ,map_location='cpu'	)
					lowerCamelCase_  				=      mam_aaa['args'] or mam_aaa['cfg']['model']
					lowerCamelCase_  				=      mam_aaa['model']
					remove_ignore_keys_(SCREAMING_SNAKE_CASE_	)
					lowerCamelCase_  				=      state_dict['encoder.embed_tokens.weight'].shape[0]

					lowerCamelCase_  				=      MaMaaaConfig(
					    vocab_size=SCREAMING_SNAKE_CASE_    ,max_position_embeddings=10_24    ,encoder_layers=args.encoder_layers    ,decoder_layers=args.decoder_layers    ,encoder_attention_heads=args.encoder_attention_heads    ,decoder_attention_heads=args.decoder_attention_heads    ,encoder_ffn_dim=args.encoder_ffn_embed_dim    ,decoder_ffn_dim=args.decoder_ffn_embed_dim    ,d_model=args.encoder_embed_dim    ,encoder_layerdrop=args.encoder_layerdrop    ,decoder_layerdrop=args.decoder_layerdrop    ,dropout=args.dropout    ,attention_dropout=args.attention_dropout    ,activation_dropout=args.activation_dropout    ,activation_function='relu'    ,)

					lowerCamelCase_  				=      state_dict['decoder.embed_tokens.weight']
					lowerCamelCase_  				=      MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_	)
					model.model.load_state_dict(SCREAMING_SNAKE_CASE_    ,strict=SCREAMING_SNAKE_CASE_	)
					lowerCamelCase_  				=      make_linear_from_emb(model.model.shared	)

					return model


if __name__ == "__main__":
				A_	= argparse.ArgumentParser()
				# Required parameters
				parser.add_argument("fairseq_path", type=str, help="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.")
				A_	= parser.parse_args()
				A_	= convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
				model.save_pretrained(args.pytorch_dump_folder_path)










from math import sqrt
def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   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(sqrt(SCREAMING_SNAKE_CASE_						) + 1						)				,	6						):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True
def      A__       (    SCREAMING_SNAKE_CASE_   :      int = 1_00_01						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   0
   _UpperCAmelCase           =   1
   while count != nth and number < 3:
      number += 1
      if is_prime(SCREAMING_SNAKE_CASE_						):
         count += 1
   while count != nth:
      number += 2
      if is_prime(SCREAMING_SNAKE_CASE_						):
         count += 1
   return number


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