



import math


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					assert isinstance(snake_case	,						snake_case			) and (
					    number >= 0
					), "'number' must been an int and positive"

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or not number % 2:
										# Negatives, 0, 1 and all even numbers are not primes
										return False

					_lowerCAmelCase						=    range(3	,						int(math.sqrt(snake_case			) + 1			)	,						2			)
					return not any(not number % i for i in odd_numbers			)


def 		_UpperCAmelCase				(      snake_case	,						snake_case=1	,						**snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    factor * value
					_lowerCAmelCase						=    value

					while not is_prime(snake_case			):
										value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

					if value == first_value_val:
										return next_prime(value + 1	,						**snake_case			)
					return value










from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 	A_       (a_			):
	UpperCAmelCase__					=			42
	UpperCAmelCase__					=			42
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				super().__init__()
				self.register_modules(unet=_A							,					scheduler=_A		)







	@torch.no_grad()
	def __call__(    self							,					_A = 1							,					_A = 5_0							,					_A = None							,					_A = "pil"							,					_A = True							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				self.unet.config.sample_size
				UpperCAmelCase   		=				(batch_size, 3, img_size, img_size)

				UpperCAmelCase   		=				self.unet

				# sample x_0 ~ N(0, sigma_0^2 * I)
				UpperCAmelCase   		=				randn_tensor(_A							,					generator=_A							,					device=self.device		) * self.scheduler.init_noise_sigma

				self.scheduler.set_timesteps(_A		)

				for t in self.progress_bar(self.scheduler.timesteps		):
							# here sigma_t == t_i from the paper
							UpperCAmelCase   		=				self.scheduler.schedule[t]
							UpperCAmelCase   		=				self.scheduler.schedule[t - 1] if t > 0 else 0

							# 1. Select temporarily increased noise level sigma_hat
							# 2. Add new noise to move from sample_i to sample_hat
							UpperCAmelCase		,			UpperCAmelCase   		=				self.scheduler.add_noise_to_input(_A							,					_A							,					generator=_A		)

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							UpperCAmelCase   		=				(sigma_hat / 2) * model((sample_hat + 1) / 2							,					sigma_hat / 2		).sample

							# 4. Evaluate dx/dt at sigma_hat
							# 5. Take Euler step from sigma to sigma_prev
							UpperCAmelCase   		=				self.scheduler.step(_A							,					_A							,					_A							,					_A		)

							if sigma_prev != 0:
										# 6. Apply 2nd order correction
										# The model inputs and output are adjusted by following eq. (213) in [1].
										UpperCAmelCase   		=				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,					sigma_prev / 2		).sample
										UpperCAmelCase   		=				self.scheduler.step_correct(
										    _A							,					_A							,					_A							,					_A							,					step_output.prev_sample							,					step_output['''derivative''']							,					)
							UpperCAmelCase   		=				step_output.prev_sample

				UpperCAmelCase   		=				(sample / 2 + 0.5).clamp(0							,					1		)
				UpperCAmelCase   		=				sample.cpu().permute(0							,					2							,					3							,					1		).numpy()
				if output_type == "pil":
							UpperCAmelCase   		=				self.numpy_to_pil(_A		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_A		)



