



"""simple docstring"""






import random


def     _lowercase       (						__snake_case			)		->     bool:
			__lowerCAmelCase   :						Dict				= num - 1
			__lowerCAmelCase   :						Union[str, Any]				= 0

			while s % 2 == 0:
						__lowerCAmelCase   :						Any				= s // 2
						t += 1

			for _ in range(5			):
						__lowerCAmelCase   :						Any				= random.randrange(2      ,num - 1			)
						__lowerCAmelCase   :						Optional[Any]				= pow(__A      ,__A      ,__A			)
						if v != 1:
									__lowerCAmelCase   :						str				= 0
									while v != (num - 1):
												if i == t - 1:
															return False
												else:
															__lowerCAmelCase   :						List[str]				= i + 1
															__lowerCAmelCase   :						Tuple				= (v**2) % num
			return True


def     _lowercase       (						__snake_case			)		->     bool:




			if num < 2:
						return False

			__lowerCAmelCase   :						Any				= [
			    2,
			    3,
			    5,
			    7,
			    11,
			    13,
			    17,
			    19,
			    23,
			    29,
			    31,
			    37,
			    41,
			    43,
			    47,
			    53,
			    59,
			    61,
			    67,
			    71,
			    73,
			    79,
			    83,
			    89,
			    97,
			    101,
			    103,
			    107,
			    109,
			    113,
			    127,
			    131,
			    137,
			    139,
			    149,
			    151,
			    157,
			    163,
			    167,
			    173,
			    179,
			    181,
			    191,
			    193,
			    197,
			    199,
			    211,
			    223,
			    227,
			    229,
			    233,
			    239,
			    241,
			    251,
			    257,
			    263,
			    269,
			    271,
			    277,
			    281,
			    283,
			    293,
			    307,
			    311,
			    313,
			    317,
			    331,
			    337,
			    347,
			    349,
			    353,
			    359,
			    367,
			    373,
			    379,
			    383,
			    389,
			    397,
			    401,
			    409,
			    419,
			    421,
			    431,
			    433,
			    439,
			    443,
			    449,
			    457,
			    461,
			    463,
			    467,
			    479,
			    487,
			    491,
			    499,
			    503,
			    509,
			    521,
			    523,
			    541,
			    547,
			    557,
			    563,
			    569,
			    571,
			    577,
			    587,
			    593,
			    599,
			    601,
			    607,
			    613,
			    617,
			    619,
			    631,
			    641,
			    643,
			    647,
			    653,
			    659,
			    661,
			    673,
			    677,
			    683,
			    691,
			    701,
			    709,
			    719,
			    727,
			    733,
			    739,
			    743,
			    751,
			    757,
			    761,
			    769,
			    773,
			    787,
			    797,
			    809,
			    811,
			    821,
			    823,
			    827,
			    829,
			    839,
			    853,
			    857,
			    859,
			    863,
			    877,
			    881,
			    883,
			    887,
			    907,
			    911,
			    919,
			    929,
			    937,
			    941,
			    947,
			    953,
			    967,
			    971,
			    977,
			    983,
			    991,
			    997,
			]

			if num in low_primes:
						return True

			for prime in low_primes:
						if (num % prime) == 0:
									return False

			return rabin_miller(__A			)


def     _lowercase       (						__snake_case = 1_024			)		->     int:




			while True:
						__lowerCAmelCase   :						Tuple				= random.randrange(2 ** (keysize - 1)      ,2 ** (keysize)			)
						if is_prime_low_num(__A			):
									return num


if __name__ == "__main__":
	__snake_case							:     Optional[Any]   =   generate_large_prime()
	print(('Prime number:', num))
	print(('is_prime_low_num:', is_prime_low_num(num)))




"""simple docstring"""






import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)


def     _lowercase       (						__snake_case			)		->     int:
			if hor == 128:
						__lowerCAmelCase   :						str				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						int				= (32, 128, 256)
						__lowerCAmelCase   :						Optional[Any]				= ("UpResnetBlock1D", "UpResnetBlock1D")

			elif hor == 32:
						__lowerCAmelCase   :						List[str]				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						Optional[Any]				= (32, 64, 128, 256)
						__lowerCAmelCase   :						Any				= ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
			__lowerCAmelCase   :						Union[str, Any]				= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""			)
			__lowerCAmelCase   :						List[Any]				= model.state_dict()
			__lowerCAmelCase   :						Optional[Any]				= {
			    "down_block_types": down_block_types,
			    "block_out_channels": block_out_channels,
			    "up_block_types": up_block_types,
			    "layers_per_block": 1,
			    "use_timestep_embedding": True,
			    "out_block_type": "OutConv1DBlock",
			    "norm_num_groups": 8,
			    "downsample_each_block": False,
			    "in_channels": 14,
			    "out_channels": 14,
			    "extra_in_channels": 0,
			    "time_embedding_type": "positional",
			    "flip_sin_to_cos": False,
			    "freq_shift": 1,
			    "sample_size": 65_536,
			    "mid_block_type": "MidResTemporalBlock1D",
			    "act_fn": "mish",
			}
			__lowerCAmelCase   :						Dict				= UNetaDModel(**__snake_case			)
			print(F"""length of state dict: {len(state_dict.keys()			)}"""			)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()			)}"""			)
			__lowerCAmelCase   :						Dict				= dict(zip(model.state_dict().keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						int				= state_dict.pop(__snake_case			)
			hf_value_function.load_state_dict(__snake_case			)

			torch.save(hf_value_function.state_dict()      ,F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""			)
			with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""      ,"w"			) as f:
						json.dump(__snake_case      ,__snake_case			)


def     _lowercase       (						)		->     List[str]:
			__lowerCAmelCase   :						Union[str, Any]				= {
			    "in_channels": 14,
			    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
			    "up_block_types": (),
			    "out_block_type": "ValueFunction",
			    "mid_block_type": "ValueFunctionMidBlock1D",
			    "block_out_channels": (32, 64, 128, 256),
			    "layers_per_block": 1,
			    "downsample_each_block": True,
			    "sample_size": 65_536,
			    "out_channels": 14,
			    "extra_in_channels": 0,
			    "time_embedding_type": "positional",
			    "use_timestep_embedding": True,
			    "flip_sin_to_cos": False,
			    "freq_shift": 1,
			    "norm_num_groups": 8,
			    "act_fn": "mish",
			}

			__lowerCAmelCase   :						int				= torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"			)
			__lowerCAmelCase   :						Any				= model
			__lowerCAmelCase   :						Optional[int]				= UNetaDModel(**__snake_case			)
			print(F"""length of state dict: {len(state_dict.keys()			)}"""			)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()			)}"""			)

			__lowerCAmelCase   :						Any				= dict(zip(state_dict.keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						Union[str, Any]				= state_dict.pop(__snake_case			)

			hf_value_function.load_state_dict(__snake_case			)

			torch.save(hf_value_function.state_dict()      ,"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"			)
			with open("hub/hopper-medium-v2/value_function/config.json"      ,"w"			) as f:
						json.dump(__snake_case      ,__snake_case			)


if __name__ == "__main__":
	unet(32)
	# unet(128)
	value_function()