

def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = False	):
					'''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_3170_4406_4679_8873_8596_1981 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
					lowercase					=		[
					    2047,
					    137_3653,
					    2532_6001,
					    32_1503_1751,
					    2_1523_0289_8747,
					    3_4747_4966_0383,
					    341_5500_7172_8321,
					    1,
					    382_5123_0565_4641_3051,
					    1,
					    1,
					    3186_6585_7834_0311_5116_7461,
					    3_3170_4406_4679_8873_8596_1981,
					]

					lowercase					=		[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
					for idx, _p in enumerate(lowerCAmelCase__				,    1	):
										if n < _p:
															# then we have our last prime to check
															lowercase					=		primes[:idx]
															break
					lowercase			,		lowercase					=		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:
										lowercase					=		False
										for r in range(lowerCAmelCase__	):
															lowercase					=		pow(lowerCAmelCase__				,    d * 2**r				,    lowerCAmelCase__	)
															# see article for analysis explanation for m
															if (r == 0 and m == 1) or ((m + 1) % n == 0):
																				lowercase					=		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  UpperCamelCase					(							):
					'''simple docstring'''
					assert not miller_rabin(561	)
					assert miller_rabin(563	)
					# 2047

					assert not miller_rabin(83_8201	)
					assert miller_rabin(83_8207	)
					# 1_373_653

					assert not miller_rabin(1731_6001	)
					assert miller_rabin(1731_6017	)
					# 25_326_001

					assert not miller_rabin(30_7838_6641	)
					assert miller_rabin(30_7838_6653	)
					# 3_215_031_751

					assert not miller_rabin(1_7130_4557_4801	)
					assert miller_rabin(1_7130_4557_4819	)
					# 2_152_302_898_747

					assert not miller_rabin(2_7797_9972_8307	)
					assert miller_rabin(2_7797_9972_8327	)
					# 3_474_749_660_383

					assert not miller_rabin(113_8500_2390_9441	)
					assert miller_rabin(113_8500_2390_9527	)
					# 341_550_071_728_321

					assert not miller_rabin(127_5041_0188_4880_4351	)
					assert miller_rabin(127_5041_0188_4880_4391	)
					# 3_825_123_056_546_413_051

					assert not miller_rabin(796_6646_4458_5077_8779_1867	)
					assert miller_rabin(796_6646_4458_5077_8779_1951	)
					# 318_665_857_834_031_151_167_461

					assert not miller_rabin(5528_4067_7446_6478_9766_0333	)
					assert miller_rabin(5528_4067_7446_6478_9766_0359	)
					# 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  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		[
					    '''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(lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		emb.weight.shape
					lowercase					=		nn.Linear(lowerCAmelCase__				,    lowerCAmelCase__				,    bias=lowerCAmelCase__	)
					lowercase					=		emb.weight.data
					return lin_layer



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		torch.load(lowerCAmelCase__				,    map_location='''cpu'''	)
					lowercase					=		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
					lowercase					=		mam_aaa['''model''']
					remove_ignore_keys_(lowerCAmelCase__	)
					lowercase					=		state_dict['''encoder.embed_tokens.weight'''].shape[0]

					lowercase					=		MaMaaaConfig(
					    vocab_size=lowerCAmelCase__				,    max_position_embeddings=1024				,    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'''				,    )

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

					return model


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




