
'''simple docstring'''






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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[Any]):
				lowerCamelCase					:			List[str]									= image.size
				lowerCamelCase					:			Optional[int]									= (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				lowerCamelCase					:			Tuple									= image.resize((w, h)       ,							resample=PIL_INTERPOLATION['lanczos'])
				lowerCamelCase					:			List[Any]									= np.array(UpperCAmelCase__).astype(np.floataa) / 2_55.0
				lowerCamelCase					:			int									= image[None].transpose(0       ,							3       ,							1       ,							2)
				lowerCamelCase					:			List[str]									= torch.from_numpy(UpperCAmelCase__)
				return 2.0 * image - 1.0




class     __snake_case		(   a__):
					def __init__(     self,    A,    A,    A,    ):

									"""simple docstring"""


									super().__init__()
									self.register_modules(vqvae=A,    unet=A,    scheduler=A      )




					@torch.no_grad()
					def __call__(     self,    A = None,    A = 1,    A = 100,    A = 0.0,    A = None,    A = "pil",    A = True,    ):

									"""simple docstring"""


									if isinstance(A,    PIL.Image.Image      ):
													lowerCamelCase					:			List[str]									= 1
									elif isinstance(A,    torch.Tensor      ):
													lowerCamelCase					:			Any									= image.shape[0]
									else:
													raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(A      )}'''      )

									if isinstance(A,    PIL.Image.Image      ):
													lowerCamelCase					:			Any									= preprocess(A      )

									lowerCamelCase					:			List[Any]									= image.shape[-2:]

									# in_channels should be 6: 3 for latents, 3 for low resolution image
									lowerCamelCase					:			Union[str, Any]									= (batch_size, self.unet.config.in_channels // 2, height, width)
									lowerCamelCase					:			Union[str, Any]									= next(self.unet.parameters()      ).dtype

									lowerCamelCase					:			Dict									= randn_tensor(A,    generator=A,    device=self.device,    dtype=A      )

									lowerCamelCase					:			Tuple									= image.to(device=self.device,    dtype=A      )

									# set timesteps and move to the correct device
									self.scheduler.set_timesteps(A,    device=self.device      )
									lowerCamelCase					:			int									= self.scheduler.timesteps

									# scale the initial noise by the standard deviation required by the scheduler
									lowerCamelCase					:			str									= latents * self.scheduler.init_noise_sigma

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
									# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
									# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
									# and should be between [0, 1]
									lowerCamelCase					:			Union[str, Any]									= 'eta' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
									lowerCamelCase					:			Optional[Any]									= {}
									if accepts_eta:
													lowerCamelCase					:			Optional[int]									= eta

									for t in self.progress_bar(A      ):
													# concat latents and low resolution image in the channel dimension.
													lowerCamelCase					:			Union[str, Any]									= torch.cat([latents, image],    dim=1      )
													lowerCamelCase					:			Tuple									= self.scheduler.scale_model_input(A,    A      )
													# predict the noise residual
													lowerCamelCase					:			str									= self.unet(A,    A      ).sample
													# compute the previous noisy sample x_t -> x_t-1
													lowerCamelCase					:			List[str]									= self.scheduler.step(A,    A,    A,    **A      ).prev_sample

									# decode the image latents with the VQVAE
									lowerCamelCase					:			int									= self.vqvae.decode(A      ).sample
									lowerCamelCase					:			str									= torch.clamp(A,    -1.0,    1.0      )
									lowerCamelCase					:			Union[str, Any]									= image / 2 + 0.5
									lowerCamelCase					:			Dict									= image.cpu().permute(0,    2,    3,    1      ).numpy()

									if output_type == "pil":
													lowerCamelCase					:			Union[str, Any]									= self.numpy_to_pil(A      )

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=A      )


'''simple docstring'''






A			      =      {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A			      =      ['a', 'b', 'c', 'd', 'e']





def    UpperCAmelCase							(  UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							List[Any]):
				lowerCamelCase					:			List[Any]									= start
				# add current to visited
				visited.append(UpperCAmelCase__)
				lowerCamelCase					:			List[str]									= edges[current]
				for neighbor in neighbors:
								# if neighbor not in visited, visit
								if neighbor not in visited:
												lowerCamelCase					:			Any									= topological_sort(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
    # if all neighbors visited add current to sort
				sort.append(UpperCAmelCase__)
				# if all vertices haven't been visited select a new one to visit
				if len(UpperCAmelCase__) != len(UpperCAmelCase__):
								for vertice in vertices:
												if vertice not in visited:
																lowerCamelCase					:			List[str]									= topological_sort(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
    # return sort
				return sort


if __name__ == "__main__":
					A			      =      topological_sort('a', [], [])
					print(sort)
