
"""simple docstring"""




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 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   42
							UpperCAmelCase__      =   42

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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







							@torch.no_grad()
							def __call__(self							,							SCREAMING_SNAKE_CASE_ = 1							,							SCREAMING_SNAKE_CASE_ = 50							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = "pil"							,							SCREAMING_SNAKE_CASE_ = True							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_    			=			self.unet

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

									self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_     )

									for t in self.progress_bar(self.scheduler.timesteps     ):
											# here sigma_t == t_i from the paper
											SCREAMING_SNAKE_CASE_    			=			self.scheduler.schedule[t]
											SCREAMING_SNAKE_CASE_    			=			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
											SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.scheduler.add_noise_to_input(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     )

											# 3. Predict the noise residual given the noise magnitude `sigma_hat`
											# The model inputs and output are adjusted by following eq. (213) in [1].
											SCREAMING_SNAKE_CASE_    			=			(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
											SCREAMING_SNAKE_CASE_    			=			self.scheduler.step(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

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

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

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_     )

"""simple docstring"""




def 							_lowerCamelCase			(			__a     ):
		if divisor % 5 == 0 or divisor % 2 == 0:
				return 0
		SCREAMING_SNAKE_CASE_    			=			1
		SCREAMING_SNAKE_CASE_    			=			1
		while repunit:
				SCREAMING_SNAKE_CASE_    			=			(10 * repunit + 1) % divisor
				repunit_index += 1
		return repunit_index



def 							_lowerCamelCase			(			__a = 1_000_000     ):
		SCREAMING_SNAKE_CASE_    			=			limit - 1
		if divisor % 2 == 0:
				divisor += 1
		while least_divisible_repunit(__a     ) <= limit:
				divisor += 2
		return divisor


if __name__ == "__main__":
						print(f'''{solution() = }''')