






def   __lowercase       (			snake_case,	snake_case = False			):







				"""simple docstring"""






				if n == 2:
								return True
				if not n % 2 or n < 2:
								return False
				if n > 5 and n % 1_0 not in (1, 3, 7, 9):  # can quickly check last digit
								return False
				if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 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
				__magic_name__		:Optional[int]   =   [
				    2_0_4_7,
				    1_3_7_3_6_5_3,
				    2_5_3_2_6_0_0_1,
				    3_2_1_5_0_3_1_7_5_1,
				    2_1_5_2_3_0_2_8_9_8_7_4_7,
				    3_4_7_4_7_4_9_6_6_0_3_8_3,
				    3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
				    1,
				    3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
				    1,
				    1,
				    3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
				    3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
				]

				__magic_name__		:List[Any]   =   [2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
				for idx, _p in enumerate(snake_case,	1			):
								if n < _p:
												# then we have our last prime to check
												__magic_name__		:int   =   primes[:idx]
												break
				__magic_name__  ,					__magic_name__		:Tuple   =   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:
								__magic_name__		:str   =   False
								for r in range(snake_case			):
												__magic_name__		:List[Any]   =   pow(snake_case,	d * 2**r,	snake_case			)
												# see article for analysis explanation for m
												if (r == 0 and m == 1) or ((m + 1) % n == 0):
																__magic_name__		:int   =   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   __lowercase       (			):







				"""simple docstring"""






				assert not miller_rabin(5_6_1			)
				assert miller_rabin(5_6_3			)
				# 2047

				assert not miller_rabin(8_3_8_2_0_1			)
				assert miller_rabin(8_3_8_2_0_7			)
				# 1_373_653

				assert not miller_rabin(1_7_3_1_6_0_0_1			)
				assert miller_rabin(1_7_3_1_6_0_1_7			)
				# 25_326_001

				assert not miller_rabin(3_0_7_8_3_8_6_6_4_1			)
				assert miller_rabin(3_0_7_8_3_8_6_6_5_3			)
				# 3_215_031_751

				assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1			)
				assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9			)
				# 2_152_302_898_747

				assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7			)
				assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7			)
				# 3_474_749_660_383

				assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1			)
				assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7			)
				# 341_550_071_728_321

				assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1			)
				assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1			)
				# 3_825_123_056_546_413_051

				assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7			)
				assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1			)
				# 318_665_857_834_031_151_167_461

				assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3			)
				assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9			)
				# 3_317_044_064_679_887_385_961_981
				# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()














import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   [
				    '''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(snake_case,	snake_case			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__  ,					__magic_name__		:Tuple   =   emb.weight.shape
				__magic_name__		:int   =   nn.Linear(snake_case,	snake_case,	bias=snake_case			)
				__magic_name__		:str   =   emb.weight.data
				return lin_layer

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   torch.load(snake_case,	map_location='''cpu'''			)
				__magic_name__		:Optional[Any]   =   mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
				__magic_name__		:List[Any]   =   mam_aaa['''model''']
				remove_ignore_keys_(snake_case			)
				__magic_name__		:Tuple   =   state_dict['''encoder.embed_tokens.weight'''].shape[0]

				__magic_name__		:List[str]   =   MaMaaaConfig(
				    vocab_size=snake_case,	max_position_embeddings=1_0_2_4,	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''',	)

				__magic_name__		:int   =   state_dict['''decoder.embed_tokens.weight''']
				__magic_name__		:List[str]   =   MaMaaaForConditionalGeneration(snake_case			)
				model.model.load_state_dict(snake_case,	strict=snake_case			)
				__magic_name__		:List[str]   =   make_linear_from_emb(model.model.shared			)

				return model


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		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.""")
					SCREAMING_SNAKE_CASE__   :      int				=		parser.parse_args()
					SCREAMING_SNAKE_CASE__   :      Any				=		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)






