




"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch

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

class     lowercase (      __UpperCAmelCase):
				__lowerCAmelCase      :      UNetaDModel
				__lowerCAmelCase      :      PNDMScheduler







				def __init__(							self				:   str ,       _lowerCamelCase				:   UNetaDModel ,       _lowerCamelCase				:   PNDMScheduler    ):



					"""simple docstring"""

					super().__init__()

					A_					:			Optional[int]						=						PNDMScheduler.from_config(scheduler.config    )

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







				@torch.no_grad()
				def __call__(							self				:   List[str] ,       _lowerCamelCase				:   int = 1 ,       _lowerCamelCase				:   int = 50 ,       _lowerCamelCase				:   Optional[Union[torch.Generator, List[torch.Generator]]] = None ,       _lowerCamelCase				:   Optional[str] = "pil" ,       _lowerCamelCase				:   bool = True ,       **_lowerCamelCase				:   List[Any] ,       ):



					"""simple docstring"""

					A_					:			Dict						=						randn_tensor(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,       generator=_lowerCamelCase ,       device=self.device ,       )

					self.scheduler.set_timesteps(_lowerCamelCase    )
					for t in self.progress_bar(self.scheduler.timesteps    ):
						A_					:			Tuple						=						self.unet(_lowerCamelCase ,       _lowerCamelCase    ).sample

						A_					:			List[Any]						=						self.scheduler.step(_lowerCamelCase ,       _lowerCamelCase ,       _lowerCamelCase    ).prev_sample

					A_					:			int						=						(image / 2 + 0.5).clamp(0 ,       1    )
					A_					:			int						=						image.cpu().permute(0 ,       2 ,       3 ,       1    ).numpy()
					if output_type == "pil":
						A_					:			int						=						self.numpy_to_pil(_lowerCamelCase    )

					if not return_dict:
						return (image,)

					return ImagePipelineOutput(images=_lowerCamelCase    )











"""simple docstring"""

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						9

	while num > 0:
		if num % 10 != digit:
			return False
		num //= 100
		digit -= 1

	return True

def 						lowercase_ (						):




	"""simple docstring"""






	A_					:			Dict						=						1_3890_2663

	while not is_square_form(num * num		):
		if num % 10 == 3:
			num -= 6  # (3 - 6) % 10 = 7
		else:
			num -= 4  # (7 - 4) % 10 = 3

	return num * 10


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





