



'''simple docstring'''






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


UpperCamelCase__:   str								=			transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]  )      ->   List[str]:
							if isinstance(_lowerCAmelCase   ,      torch.Tensor  ):
														return image
							elif isinstance(_lowerCAmelCase   ,      PIL.Image.Image  ):
														UpperCAmelCase :       List[Any] 							=		[image]

							UpperCAmelCase :       str 							=		[trans(img.convert('''RGB'''  )  ) for img in image]
							UpperCAmelCase :       List[Any] 							=		torch.stack(_lowerCAmelCase  )
							return image




class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     int		,			__snake_case			:     List[Any]		)			->		List[Any]:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								UpperCAmelCase :       Union[str, Any] 							=		DDIMScheduler.from_config(scheduler.config		)

								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	def      A	(						self			:     Tuple		,			__snake_case			:     Union[str, Any]		)			->		str:
								if strength < 0 or strength > 1:
															raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""		)







	def      A	(						self			:     List[str]		,			__snake_case			:     List[str]		,			__snake_case			:     Any		,			__snake_case			:     List[Any]		)			->		Optional[Any]:
								# get the original timestep using init_timestep
								UpperCAmelCase :       Optional[int] 							=		min(int(num_inference_steps * strength		)		,			__snake_case		)

								UpperCAmelCase :       Tuple 							=		max(num_inference_steps - init_timestep		,			0		)
								UpperCAmelCase :       Tuple 							=		self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     List[str]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Tuple		,			__snake_case			:     str=None		)			->		List[Any]:
								if not isinstance(__snake_case		,			(torch.Tensor, PIL.Image.Image, list)		):
															raise ValueError(
															    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case		)}"""		)

								UpperCAmelCase :       Optional[Any] 							=		image.to(device=__snake_case		,			dtype=__snake_case		)

								if isinstance(__snake_case		,			__snake_case		) and len(__snake_case		) != batch_size:
															raise ValueError(
															    F"""You have passed a list of generators of length {len(__snake_case		)}, but requested an effective batch"""
															    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""		)

								UpperCAmelCase :       Optional[int] 							=		init_latents.shape
								UpperCAmelCase :       Tuple 							=		randn_tensor(__snake_case		,			generator=__snake_case		,			device=__snake_case		,			dtype=__snake_case		)

								# get latents
								print('''add noise to latents at timestep'''		,			__snake_case		)
								UpperCAmelCase :       List[Any] 							=		self.scheduler.add_noise(__snake_case		,			__snake_case		,			__snake_case		)
								UpperCAmelCase :       Any 							=		init_latents

								return latents







	@torch.no_grad()
	def __call__(						self			:     Union[str, Any]		,			__snake_case			:     Union[torch.FloatTensor, PIL.Image.Image] = None		,			__snake_case			:     float = 0.8		,			__snake_case			:     int = 1		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     float = 0.0		,			__snake_case			:     int = 50		,			__snake_case			:     Optional[bool] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			)			->		Union[ImagePipelineOutput, Tuple]:
								self.check_inputs(__snake_case		)

								# 2. Preprocess image
								UpperCAmelCase :       int 							=		preprocess(__snake_case		)

								# 3. set timesteps
								self.scheduler.set_timesteps(__snake_case		,			device=self.device		)
								UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		self.get_timesteps(__snake_case		,			__snake_case		,			self.device		)
								UpperCAmelCase :       List[Any] 							=		timesteps[:1].repeat(__snake_case		)

								# 4. Prepare latent variables
								UpperCAmelCase :       Union[str, Any] 							=		self.prepare_latents(__snake_case		,			__snake_case		,			__snake_case		,			self.unet.dtype		,			self.device		,			__snake_case		)
								UpperCAmelCase :       Dict 							=		latents

								# 5. Denoising loop
								for t in self.progress_bar(__snake_case		):
															# 1. predict noise model_output
															UpperCAmelCase :       List[Any] 							=		self.unet(__snake_case		,			__snake_case		).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															UpperCAmelCase :       Any 							=		self.scheduler.step(
															    __snake_case		,			__snake_case		,			__snake_case		,			eta=__snake_case		,			use_clipped_model_output=__snake_case		,			generator=__snake_case		,			).prev_sample

								UpperCAmelCase :       Optional[int] 							=		(image / 2 + 0.5).clamp(0		,			1		)
								UpperCAmelCase :       List[Any] 							=		image.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Dict 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (image, latent_timestep.item())

								return ImagePipelineOutput(images=__snake_case		)










'''simple docstring'''






import random

from .binary_exp_mod import bin_exp_mod


def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Optional[Any]=1000  )      ->   int:
							if n < 2:
														return False

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

							# this means n is odd
							UpperCAmelCase :       str 							=		n - 1
							UpperCAmelCase :       List[Any] 							=		0
							while d % 2 == 0:
														d /= 2
														exp += 1

							# n - 1=d*(2**exp)
							UpperCAmelCase :       List[str] 							=		0
							while count < prec:
														UpperCAmelCase :       int 							=		random.randint(2   ,      n - 1  )
														UpperCAmelCase :       List[str] 							=		bin_exp_mod(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														if b != 1:
																					UpperCAmelCase :       int 							=		True
																					for _ in range(_lowerCAmelCase  ):
																												if b == n - 1:
																																			UpperCAmelCase :       Dict 							=		False
																																			break
																												UpperCAmelCase :       str 							=		b * b
																												b %= n
																					if flag:
																												return False
																					count += 1
							return True


if __name__ == "__main__":
						UpperCamelCase__:   Optional[int]								=			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)))





