


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        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A		,      A		,      A = None		,      )  -> int:







					'''simple docstring'''







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

					# 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(A	)
								lowerCamelCase    		=  dict(sorted(self.labels.items()	)	)







		def 					__A   ( self		,      A	)  -> List[int]:







					'''simple docstring'''







					if not isinstance(A		,      A	):
								lowerCamelCase    		=  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 = 50		,      A = "pil"		,      A = True		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  len(A	)
					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=A		,      device=self.device		,      dtype=self.transformer.dtype		,      )
					lowerCamelCase    		=  torch.cat([latents] * 2	) if guidance_scale > 1 else latents

					lowerCamelCase    		=  torch.tensor(A		,      device=self.device	).reshape(-1	)
					lowerCamelCase    		=  torch.tensor([10_00] * 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(A	)

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

								lowerCamelCase    		=  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+)
											lowerCamelCase    		=  latent_model_input.device.type == """mps"""
											if isinstance(A		,      A	):
														lowerCamelCase    		=  torch.floataa if is_mps else torch.floataa
											else:
														lowerCamelCase    		=  torch.intaa if is_mps else torch.intaa
											lowerCamelCase    		=  torch.tensor([timesteps]		,      dtype=A		,      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(
								    A		,      timestep=A		,      class_labels=A	).sample

								# perform guidance
								if guidance_scale > 1:
											lowerCamelCase		,		lowerCamelCase    		=  noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
											lowerCamelCase		,		lowerCamelCase    		=  torch.split(A		,      len(A	) // 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(A		,      A		,      dim=1	)
								else:
											lowerCamelCase    		=  noise_pred

								# compute previous image: x_t -> x_t-1
								lowerCamelCase    		=  self.scheduler.step(A		,      A		,      A	).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(A	).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(A	)

					if not return_dict:
								return (samples,)

					return ImagePipelineOutput(images=A	)





UpperCAmelCase   :    Any     =		[0, 2, 4, 6, 8]
UpperCAmelCase   :    Optional[Any]     =		[1, 3, 5, 7, 9]






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						list[int]  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			if remaining_length == 0:
						if digits[0] == 0 or digits[-1] == 0:
									return 0

						for i in range(length // 2 - 1  ,							-1  ,							-1      ):
									remainder += digits[i] + digits[length - i - 1]

									if remainder % 2 == 0:
												return 0

									remainder //= 10

						return 1

			if remaining_length == 1:
						if remainder % 2 == 0:
									return 0

						lowerCamelCase    		=  0
						for digit in range(10      ):
									lowerCamelCase    		=  digit
									result += reversible_numbers(
									    0  ,							(remainder + 2 * digit) // 10  ,							lowerCamelCase__  ,							lowerCamelCase__      )
						return result

			lowerCamelCase    		=  0
			for digita in range(10      ):
						lowerCamelCase    		=  digita

						if (remainder + digita) % 2 == 0:
									lowerCamelCase    		=  ODD_DIGITS
						else:
									lowerCamelCase    		=  EVEN_DIGITS

						for digita in other_parity_digits:
									lowerCamelCase    		=  digita
									result += reversible_numbers(
									    remaining_length - 2  ,							(remainder + digita + digita) // 10  ,							lowerCamelCase__  ,							lowerCamelCase__  ,							)
			return result






def 	__lowerCamelCase						(							lowerCamelCase__		:						int = 9      ):





			'''simple docstring'''




			lowerCamelCase    		=  0
			for length in range(1  ,							max_power + 1      ):
						result += reversible_numbers(lowerCamelCase__  ,							0  ,							[0] * length  ,							lowerCamelCase__      )
			return result


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

