





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def      A__       (    SCREAMING_SNAKE_CASE_   :      Dict						)  ->  Optional[int]:






   """simple docstring"""







   _UpperCAmelCase           =   [
       '''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      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase       ,      _UpperCAmelCase           =   emb.weight.shape
   _UpperCAmelCase           =   nn.Linear(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	bias=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   emb.weight.data
   return lin_layer
def      A__       (    SCREAMING_SNAKE_CASE_   :      Optional[int]						)  ->  Any:






   """simple docstring"""







   _UpperCAmelCase           =   torch.load(SCREAMING_SNAKE_CASE_				,	map_location='''cpu'''						)
   _UpperCAmelCase           =   mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
   _UpperCAmelCase           =   mam_aaa['''model''']
   remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   state_dict['''encoder.embed_tokens.weight'''].shape[0]

   _UpperCAmelCase           =   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'''				,	)

   _UpperCAmelCase           =   state_dict['''decoder.embed_tokens.weight''']
   _UpperCAmelCase           =   MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
   model.model.load_state_dict(SCREAMING_SNAKE_CASE_				,	strict=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   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="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.")
   UpperCAmelCase_           =	parser.parse_args()
   UpperCAmelCase_           =	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() = }''')