"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  List[str]	,    snake_case__    :  TransformeraDModel	,    snake_case__    :  AutoencoderKL	,    snake_case__    :  KarrasDiffusionSchedulers	,    snake_case__    :  Optional[Dict[int, str]] = None	,    ):
							super().__init__()
							self.register_modules(transformer=snake_case__	,    vae=snake_case__	,    scheduler=snake_case__  )

							# create a imagenet -> id dictionary for easier use
							lowerCAmelCase__   = {}
							if idalabel is not None:
									for key, value in idalabel.items():
											for label in value.split(""","""  ):
													lowerCAmelCase__   = int(snake_case__  )
									lowerCAmelCase__   = dict(sorted(self.labels.items()  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Union[str, List[str]]  ):

							if not isinstance(snake_case__	,    snake_case__  ):
									lowerCAmelCase__   = list(snake_case__  )

							for l in label:
									if l not in self.labels:
											raise ValueError(
											    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""  )

							return [self.labels[l] for l in label]




					@torch.no_grad()
					def __call__(						self    :  List[str]	,    snake_case__    :  List[int]	,    snake_case__    :  float = 4.0	,    snake_case__    :  Optional[Union[torch.Generator, List[torch.Generator]]] = None	,    snake_case__    :  int = 50	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    ):
							lowerCAmelCase__   = len(snake_case__  )
							lowerCAmelCase__   = self.transformer.config.sample_size
							lowerCAmelCase__   = self.transformer.config.in_channels

							lowerCAmelCase__   = randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)	,    generator=snake_case__	,    device=self.device	,    dtype=self.transformer.dtype	,    )
							lowerCAmelCase__   = torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

							lowerCAmelCase__   = torch.tensor(snake_case__	,    device=self.device  ).reshape(-1  )
							lowerCAmelCase__   = torch.tensor([1000] * batch_size	,    device=self.device  )
							lowerCAmelCase__   = torch.cat([class_labels, class_null]	,    0  ) if guidance_scale > 1 else class_labels

							# set step values
							self.scheduler.set_timesteps(snake_case__  )

							for t in self.progress_bar(self.scheduler.timesteps  ):
									if guidance_scale > 1:
											lowerCAmelCase__   = latent_model_input[: len(snake_case__  ) // 2]
											lowerCAmelCase__   = torch.cat([half, half]	,    dim=0  )
									lowerCAmelCase__   = self.scheduler.scale_model_input(snake_case__	,    snake_case__  )

									lowerCAmelCase__   = t
									if not torch.is_tensor(snake_case__  ):
											# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
											# This would be a good case for the `match` statement (Python 3.10+)
											lowerCAmelCase__   = latent_model_input.device.type == """mps"""
											if isinstance(snake_case__	,    snake_case__  ):
													lowerCAmelCase__   = torch.floataa if is_mps else torch.floataa
											else:
													lowerCAmelCase__   = torch.intaa if is_mps else torch.intaa
											lowerCAmelCase__   = torch.tensor([timesteps]	,    dtype=snake_case__	,    device=latent_model_input.device  )
									elif len(timesteps.shape  ) == 0:
											lowerCAmelCase__   = timesteps[None].to(latent_model_input.device  )
									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									lowerCAmelCase__   = timesteps.expand(latent_model_input.shape[0]  )
									# predict noise model_output
									lowerCAmelCase__   = self.transformer(
									    snake_case__	,    timestep=snake_case__	,    class_labels=snake_case__  ).sample

									# perform guidance
									if guidance_scale > 1:
											lowerCAmelCase__ ,       lowerCAmelCase__   = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
											lowerCAmelCase__ ,       lowerCAmelCase__   = torch.split(snake_case__	,    len(snake_case__  ) // 2	,    dim=0  )

											lowerCAmelCase__   = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
											lowerCAmelCase__   = torch.cat([half_eps, half_eps]	,    dim=0  )

											lowerCAmelCase__   = torch.cat([eps, rest]	,    dim=1  )

									# learned sigma
									if self.transformer.config.out_channels // 2 == latent_channels:
											lowerCAmelCase__ ,       lowerCAmelCase__   = torch.split(snake_case__	,    snake_case__	,    dim=1  )
									else:
											lowerCAmelCase__   = noise_pred

									# compute previous image: x_t -> x_t-1
									lowerCAmelCase__   = self.scheduler.step(snake_case__	,    snake_case__	,    snake_case__  ).prev_sample

							if guidance_scale > 1:
									lowerCAmelCase__ ,       lowerCAmelCase__   = latent_model_input.chunk(2	,    dim=0  )
							else:
									lowerCAmelCase__   = latent_model_input

							lowerCAmelCase__   = 1 / self.vae.config.scaling_factor * latents
							lowerCAmelCase__   = self.vae.decode(snake_case__  ).sample

							lowerCAmelCase__   = (samples / 2 + 0.5).clamp(0	,    1  )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							lowerCAmelCase__   = samples.cpu().permute(0	,    2	,    3	,    1  ).float().numpy()

							if output_type == "pil":
									lowerCAmelCase__   = self.numpy_to_pil(snake_case__  )

							if not return_dict:
									return (samples,)

							return ImagePipelineOutput(images=snake_case__  )





"""simple docstring"""




from itertools import permutations







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		if num[3] % 2 != 0:
				return False

		if (num[2] + num[3] + num[4]) % 3 != 0:
				return False

		if num[5] % 5 != 0:
				return False

		lowerCAmelCase__   = [7, 11, 13, 17]
		for i, test in enumerate(lowerCamelCase__  ):
				if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
						return False
		return True







def 				_UpperCAmelCase		(							lowerCamelCase__ = 10  ):





		"""simple docstring"""




		return sum(
		    int("""""".join(map(lowerCamelCase__      ,	lowerCamelCase__  )  )  )
		    for num in permutations(range(lowerCamelCase__  )  )
		    if is_substring_divisible(lowerCamelCase__  )  )


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




