
'''simple docstring'''



def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = False						)     ->     bool:




   """simple docstring"""
   if n == 2:
      return True
   if not n % 2 or n < 2:
      return False
   if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
      return False
   if n > 3_31_70_44_06_46_79_88_73_85_96_19_81 and not allow_probable:
      raise ValueError(
          """Warning: upper bound of deterministic test is exceeded. """
          """Pass allow_probable=True to allow probabilistic test. """
          """A return value of True indicates a probable prime."""						)
   # array bounds provided by analysis
   _SCREAMING_SNAKE_CASE      =				[
       20_47,
       1_37_36_53,
       25_32_60_01,
       32_15_03_17_51,
       2_15_23_02_89_87_47,
       3_47_47_49_66_03_83,
       3_41_55_00_71_72_83_21,
       1,
       3_82_51_23_05_65_46_41_30_51,
       1,
       1,
       31_86_65_85_78_34_03_11_51_16_74_61,
       3_31_70_44_06_46_79_88_73_85_96_19_81,
   ]

   _SCREAMING_SNAKE_CASE      =				[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
   for idx, _p in enumerate(SCREAMING_SNAKE_CASE_    ,      1						):
      if n < _p:
         # then we have our last prime to check
         _SCREAMING_SNAKE_CASE      =				primes[:idx]
         break
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				n - 1, 0
   # break up n -1 into a power of 2 (s) and
   # remaining odd component
   # essentially, solve for d * 2 ** s == n - 1
   while d % 2 == 0:
      d //= 2
      s += 1
   for prime in plist:
      _SCREAMING_SNAKE_CASE      =				False
      for r in range(SCREAMING_SNAKE_CASE_						):
         _SCREAMING_SNAKE_CASE      =				pow(SCREAMING_SNAKE_CASE_    ,      d * 2**r    ,      SCREAMING_SNAKE_CASE_						)
         # see article for analysis explanation for m
         if (r == 0 and m == 1) or ((m + 1) % n == 0):
            _SCREAMING_SNAKE_CASE      =				True
            # this loop will not determine compositeness
            break
      if pr:
         continue
      # if pr is False, then the above loop never evaluated to true,
      # and the n MUST be composite
      return False
   return True
def    lowerCAmelCase_	(				)     ->     None:




   """simple docstring"""
   assert not miller_rabin(5_61						)
   assert miller_rabin(5_63						)
   # 2047

   assert not miller_rabin(83_82_01						)
   assert miller_rabin(83_82_07						)
   # 1_373_653

   assert not miller_rabin(17_31_60_01						)
   assert miller_rabin(17_31_60_17						)
   # 25_326_001

   assert not miller_rabin(30_78_38_66_41						)
   assert miller_rabin(30_78_38_66_53						)
   # 3_215_031_751

   assert not miller_rabin(1_71_30_45_57_48_01						)
   assert miller_rabin(1_71_30_45_57_48_19						)
   # 2_152_302_898_747

   assert not miller_rabin(2_77_97_99_72_83_07						)
   assert miller_rabin(2_77_97_99_72_83_27						)
   # 3_474_749_660_383

   assert not miller_rabin(1_13_85_00_23_90_94_41						)
   assert miller_rabin(1_13_85_00_23_90_95_27						)
   # 341_550_071_728_321

   assert not miller_rabin(1_27_50_41_01_88_48_80_43_51						)
   assert miller_rabin(1_27_50_41_01_88_48_80_43_91						)
   # 3_825_123_056_546_413_051

   assert not miller_rabin(7_96_66_46_44_58_50_77_87_79_18_67						)
   assert miller_rabin(7_96_66_46_44_58_50_77_87_79_19_51						)
   # 318_665_857_834_031_151_167_461

   assert not miller_rabin(55_28_40_67_74_46_64_78_97_66_03_33						)
   assert miller_rabin(55_28_40_67_74_46_64_78_97_66_03_59						)
   # 3_317_044_064_679_887_385_961_981
   # upper limit for probabilistic test


if __name__ == "__main__":
     test_miller_rabin()








'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[
       """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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				emb.weight.shape
   _SCREAMING_SNAKE_CASE      =				nn.Linear(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      bias=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				emb.weight.data
   return lin_layer
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




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

   _SCREAMING_SNAKE_CASE      =				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"""    ,      )

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

   return model


if __name__ == "__main__":
     UpperCamelCase__      :		Dict       							=  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__      :		List[str]       							=  parser.parse_args()
     UpperCamelCase__      :		Any       							=  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)






