






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 				__UpperCAmelCase		(UpperCAmelCase_   ):







			def __init__(     self:  int				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  Optional[Any]				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  Tuple = None				,     ):







									'''simple docstring'''




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

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







			def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									if not isinstance(__lowercase				,     __lowercase					):
															_SCREAMING_SNAKE_CASE     				=  list(__lowercase					)

									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:  str				,     UpperCAmelCase_:  List[Any]				,     UpperCAmelCase_:  str = 4.0				,     UpperCAmelCase_:  List[Any] = None				,     UpperCAmelCase_:  Union[str, Any] = 50				,     UpperCAmelCase_:  int = "pil"				,     UpperCAmelCase_:  Any = True				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  len(__lowercase					)
									_SCREAMING_SNAKE_CASE     				=  self.transformer.config.sample_size
									_SCREAMING_SNAKE_CASE     				=  self.transformer.config.in_channels

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

									_SCREAMING_SNAKE_CASE     				=  torch.tensor(__lowercase				,     device=self.device					).reshape(-1					)
									_SCREAMING_SNAKE_CASE     				=  torch.tensor([1_000] * batch_size				,     device=self.device					)
									_SCREAMING_SNAKE_CASE     				=  torch.cat([class_labels, class_null]				,     0					) if guidance_scale > 1 else class_labels

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

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

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

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

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

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

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

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

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

									_SCREAMING_SNAKE_CASE     				=  1 / self.vae.config.scaling_factor * latents
									_SCREAMING_SNAKE_CASE     				=  self.vae.decode(__lowercase					).sample

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

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

									if output_type == "pil":
															_SCREAMING_SNAKE_CASE     				=  self.numpy_to_pil(__lowercase					)

									if not return_dict:
															return (samples,)

									return ImagePipelineOutput(images=__lowercase					)









UpperCamelCase			    =     {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

UpperCamelCase			    =     {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  list[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  True
						_SCREAMING_SNAKE_CASE     				=  []

						for neighbour in graph[vert]:
												if not visited[neighbour]:
																		order += topology_sort(snake_case__	,snake_case__	,snake_case__		)

						order.append(snake_case__		)

						return order

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  list[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  True
						_SCREAMING_SNAKE_CASE     				=  [vert]

						for neighbour in reversed_graph[vert]:
												if not visited[neighbour]:
																		component += find_components(snake_case__	,snake_case__	,snake_case__		)

						return component




def 	__lowerCamelCase      (		snake_case__		)							->  list[list[int]]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  len(snake_case__		) * [False]
						_SCREAMING_SNAKE_CASE     				=  {vert: [] for vert in range(len(snake_case__		)		)}

						for vert, neighbours in graph.items():
												for neighbour in neighbours:
																		reversed_graph[neighbour].append(snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  []
						for i, was_visited in enumerate(snake_case__		):
												if not was_visited:
																		order += topology_sort(snake_case__	,snake_case__	,snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  []
						_SCREAMING_SNAKE_CASE     				=  len(snake_case__		) * [False]

						for i in range(len(snake_case__		)		):
												_SCREAMING_SNAKE_CASE     				=  order[len(snake_case__		) - i - 1]
												if not visited[vert]:
																		_SCREAMING_SNAKE_CASE     				=  find_components(snake_case__	,snake_case__	,snake_case__		)
																		components_list.append(snake_case__		)

						return components_list

