




'''simple docstring'''


import random

from .binary_exp_mod import bin_exp_mod
def 						snake_case_       (UpperCamelCase			:    Tuple					,		UpperCamelCase			:    Optional[Any]=1000 ):





							'''simple docstring'''


							if n < 2:
														return False

							if n % 2 == 0:
														return n == 2

							# this means n is odd
							_a      	=							n - 1
							_a      	=							0
							while d % 2 == 0:
														d /= 2
														exp += 1

							# n - 1=d*(2**exp)
							_a      	=							0
							while count < prec:
														_a      	=							random.randint(2					,		n - 1 )
														_a      	=							bin_exp_mod(_UpperCamelCase					,		_UpperCamelCase					,		_UpperCamelCase )
														if b != 1:
																					_a      	=							True
																					for _ in range(_UpperCamelCase ):
																												if b == n - 1:
																																			_a      	=							False
																																			break
																												_a      	=							b * b
																												b %= n
																					if flag:
																												return False
																					count += 1
							return True


if __name__ == "__main__":
				_snake_case : List[Any]  				=						abs(int(input('Enter bound : ').strip()))
				print('Here\'s the list of primes:')
				print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))





"""simple docstring"""


import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


A	:  List[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										super().__init__()
										__lowerCAmelCase							       =     nn.ModuleList(__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = False       ,  __a = True       ,  ):
										for i, (image, scale, controlnet) in enumerate(zip(__a       ,  __a       ,  self.nets      )      ):
													__lowerCAmelCase       ,      __lowerCAmelCase							       =     controlnet(
													    __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  )

													# merge samples
													if i == 0:
																__lowerCAmelCase       ,      __lowerCAmelCase							       =     down_samples, mid_sample
													else:
																__lowerCAmelCase							       =     [
																    samples_prev + samples_curr
																    for samples_prev, samples_curr in zip(__a       ,  __a      )
																]
																mid_block_res_sample += mid_sample

										return down_block_res_samples, mid_block_res_sample

							def      snake_case							(	self       ,  __a       ,  __a = True       ,  __a = None       ,  __a = False       ,  __a = None       ,  ):
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     save_directory
										for controlnet in self.nets:
													controlnet.save_pretrained(
													    __a       ,  is_main_process=__a       ,  save_function=__a       ,  safe_serialization=__a       ,  variant=__a       ,  )

													idx += 1
													__lowerCAmelCase							       =     model_path_to_save + f"_{idx}"



							@classmethod
							def      snake_case							(	cls       ,  __a       ,  **__a      ):
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     []

										# load controlnet and append to list until no controlnet directory exists anymore
										# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
										# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
										__lowerCAmelCase							       =     pretrained_model_path
										while os.path.isdir(__a      ):
													__lowerCAmelCase							       =     ControlNetModel.from_pretrained(__a       ,  **__a      )
													controlnets.append(__a      )

													idx += 1
													__lowerCAmelCase							       =     pretrained_model_path + f"_{idx}"

										logger.info(f"{len(__a      )} controlnets loaded from {pretrained_model_path}."      )

										if len(__a      ) == 0:
													raise ValueError(
													    f"No ControlNets found under {os.path.dirname(__a      )}. Expected at least {pretrained_model_path + '_0'}."      )

										return cls(__a      )




