





import random





def lowerCAmelCase__(__snake_case							)       ->   bool:







	'''simple docstring'''







	lowerCamelCase__              =	num - 1
	lowerCamelCase__              =	0

	while s % 2 == 0:
		lowerCamelCase__              =	s // 2
		t += 1

	for _ in range(5							):
		lowerCamelCase__              =	random.randrange(2 ,num - 1							)
		lowerCamelCase__              =	pow(__snake_case ,__snake_case ,__snake_case							)
		if v != 1:
			lowerCamelCase__              =	0
			while v != (num - 1):
				if i == t - 1:
					return False
				else:
					lowerCamelCase__              =	i + 1
					lowerCamelCase__              =	(v**2) % num
	return True





def lowerCAmelCase__(__snake_case							)       ->   bool:







	'''simple docstring'''







	if num < 2:
		return False

	lowerCamelCase__              =	[
	    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(__snake_case							)





def lowerCAmelCase__(__snake_case = 1024							)       ->   int:







	'''simple docstring'''







	while True:
		lowerCamelCase__              =	random.randrange(2 ** (keysize - 1) ,2 ** (keysize)							)
		if is_prime_low_num(__snake_case							):
			return num


if __name__ == "__main__":
			_a						=   generate_large_prime()
			print(("Prime number:", num))
			print(("is_prime_low_num:", is_prime_low_num(num)))









from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		42






class       __A					(					lowerCAmelCase    ,       lowerCAmelCase				):

			'''simple docstring'''







			@register_to_config
			def __init__(					self ,						__lowerCAmelCase = 1_6 ,						__lowerCAmelCase = 8_8 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = 1 ,						__lowerCAmelCase = 0.0 ,						__lowerCAmelCase = 3_2 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = False ,						__lowerCAmelCase = None ,						__lowerCAmelCase = "geglu" ,						__lowerCAmelCase = True ,						__lowerCAmelCase = True ,						):



				'''simple docstring'''







				super().__init__()
				lowerCamelCase__              =	num_attention_heads
				lowerCamelCase__              =	attention_head_dim
				lowerCamelCase__              =	num_attention_heads * attention_head_dim

				lowerCamelCase__              =	in_channels

				lowerCamelCase__              =	torch.nn.GroupNorm(num_groups=__lowerCAmelCase ,						num_channels=__lowerCAmelCase ,						eps=1E-6 ,						affine=__lowerCAmelCase							)
				lowerCamelCase__              =	nn.Linear(__lowerCAmelCase ,						__lowerCAmelCase							)

				# 3. Define transformers blocks
				lowerCamelCase__              =	nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            __lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						dropout=__lowerCAmelCase ,						cross_attention_dim=__lowerCAmelCase ,						activation_fn=__lowerCAmelCase ,						attention_bias=__lowerCAmelCase ,						double_self_attention=__lowerCAmelCase ,						norm_elementwise_affine=__lowerCAmelCase ,						)
				        for d in range(__lowerCAmelCase							)
				    ]							)

				lowerCamelCase__              =	nn.Linear(__lowerCAmelCase ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=1 ,						__lowerCAmelCase=None ,						__lowerCAmelCase = True ,						):



				'''simple docstring'''







				lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__              =	hidden_states.shape
				lowerCamelCase__              =	batch_frames // num_frames

				lowerCamelCase__              =	hidden_states

				lowerCamelCase__              =	hidden_states[None, :].reshape(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)
				lowerCamelCase__              =	hidden_states.permute(0 ,						2 ,						1 ,						3 ,						4							)

				lowerCamelCase__              =	self.norm(__lowerCAmelCase							)
				lowerCamelCase__              =	hidden_states.permute(0 ,						3 ,						4 ,						2 ,						1							).reshape(batch_size * height * width ,						__lowerCAmelCase ,						__lowerCAmelCase							)

				lowerCamelCase__              =	self.proj_in(__lowerCAmelCase							)

				# 2. Blocks
				for block in self.transformer_blocks:
					lowerCamelCase__              =	block(
					    __lowerCAmelCase ,						encoder_hidden_states=__lowerCAmelCase ,						timestep=__lowerCAmelCase ,						cross_attention_kwargs=__lowerCAmelCase ,						class_labels=__lowerCAmelCase ,						)

				# 3. Output
				lowerCamelCase__              =	self.proj_out(__lowerCAmelCase							)
				lowerCamelCase__              =	(
				    hidden_states[None, None, :]
				    .reshape(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)
				    .permute(0 ,						3 ,						4 ,						1 ,						2							)
				    .contiguous()
				)
				lowerCamelCase__              =	hidden_states.reshape(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)

				lowerCamelCase__              =	hidden_states + residual

				if not return_dict:
					return (output,)

				return TransformerTemporalModelOutput(sample=__lowerCAmelCase							)



