






'''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_    (    a__							):







			def __init__(self					,   __a					,   __a					,   __a					,   __a = None					,   )			->      Optional[int]:
						"""simple docstring"""






						super().__init__()
						self.register_modules(transformer=_A					,   vae=_A					,   scheduler=_A)

						# create a imagenet -> id dictionary for easier use
						__snake_case				:      Union[str, Any]     							= {}
						if idalabel is not None:
									for key, value in idalabel.items():
												for label in value.split(','):
															__snake_case				:      Tuple     							= int(_A)
									__snake_case				:      Dict     							= dict(sorted(self.labels.items()))







			def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Optional[Any]:
						"""simple docstring"""






						if not isinstance(_A					,   _A):
									__snake_case				:      Tuple     							= list(_A)

						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					,   __a					,   __a = 4.0					,   __a = None					,   __a = 5_0					,   __a = "pil"					,   __a = True					,   )			->      List[Any]:
						"""simple docstring"""






						__snake_case				:      str     							= len(_A)
						__snake_case				:      List[Any]     							= self.transformer.config.sample_size
						__snake_case				:      Dict     							= self.transformer.config.in_channels

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

						__snake_case				:      Optional[Any]     							= torch.tensor(_A					,   device=self.device).reshape(-1)
						__snake_case				:      str     							= torch.tensor([1_0_0_0] * batch_size					,   device=self.device)
						__snake_case				:      Dict     							= torch.cat([class_labels, class_null]					,   0) if guidance_scale > 1 else class_labels

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

						for t in self.progress_bar(self.scheduler.timesteps):
									if guidance_scale > 1:
												__snake_case				:      int     							= latent_model_input[: len(_A) // 2]
												__snake_case				:      int     							= torch.cat([half, half]					,   dim=0)
									__snake_case				:      Union[str, Any]     							= self.scheduler.scale_model_input(_A					,   _A)

									__snake_case				:      Union[str, Any]     							= t
									if not torch.is_tensor(_A):
												# 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+)
												__snake_case				:      Union[str, Any]     							= latent_model_input.device.type == 'mps'
												if isinstance(_A					,   _A):
															__snake_case				:      List[str]     							= torch.floataa if is_mps else torch.floataa
												else:
															__snake_case				:      Union[str, Any]     							= torch.intaa if is_mps else torch.intaa
												__snake_case				:      Optional[Any]     							= torch.tensor([timesteps]					,   dtype=_A					,   device=latent_model_input.device)
									elif len(timesteps.shape) == 0:
												__snake_case				:      Any     							= timesteps[None].to(latent_model_input.device)
									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									__snake_case				:      Dict     							= timesteps.expand(latent_model_input.shape[0])
									# predict noise model_output
									__snake_case				:      Optional[int]     							= self.transformer(
									    _A					,   timestep=_A					,   class_labels=_A).sample

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

												__snake_case				:      str     							= uncond_eps + guidance_scale * (cond_eps - uncond_eps)
												__snake_case				:      int     							= torch.cat([half_eps, half_eps]					,   dim=0)

												__snake_case				:      List[Any]     							= torch.cat([eps, rest]					,   dim=1)

									# learned sigma
									if self.transformer.config.out_channels // 2 == latent_channels:
												__snake_case				:      Any     							= torch.split(_A					,   _A					,   dim=1)
									else:
												__snake_case				:      Optional[int]     							= noise_pred

									# compute previous image: x_t -> x_t-1
									__snake_case				:      Tuple     							= self.scheduler.step(_A					,   _A					,   _A).prev_sample

						if guidance_scale > 1:
									__snake_case				:      Union[str, Any]     							= latent_model_input.chunk(2					,   dim=0)
						else:
									__snake_case				:      int     							= latent_model_input

						__snake_case				:      List[str]     							= 1 / self.vae.config.scaling_factor * latents
						__snake_case				:      Dict     							= self.vae.decode(_A).sample

						__snake_case				:      int     							= (samples / 2 + 0.5).clamp(0					,   1)

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

						if output_type == "pil":
									__snake_case				:      List[str]     							= self.numpy_to_pil(_A)

						if not return_dict:
									return (samples,)

						return ImagePipelineOutput(images=_A)







'''simple docstring'''






__A    	=   {str(digit): digit**5 for digit in range(1_0)}




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A  )  )




def        _SCREAMING_SNAKE_CASE  (				)    -> int:







   """simple docstring"""
   return sum(
       number
       for number in range(10_00  ,		1_00_00_00  )
       if number == digits_fifth_powers_sum(A  )  )


if __name__ == "__main__":
      print(solution())