

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       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_       ,					snake_case_           =					image.size
							snake_case_       ,					snake_case_           =					(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
							snake_case_           =					image.resize((w, h)     ,							resample=PIL_INTERPOLATION['lanczos']   )
							snake_case_           =					np.array(UpperCamelCase__   ).astype(np.floataa   ) / 2_55.0
							snake_case_           =					image[None].transpose(0     ,							3     ,							1     ,							2   )
							snake_case_           =					torch.from_numpy(UpperCamelCase__   )
							return 2.0 * image - 1.0







class 				lowercase       ( lowercase_    ):




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     ):
											super().__init__()
											self.register_modules(vqvae=snake_case   ,     unet=snake_case   ,     scheduler=snake_case )




				@torch.no_grad()
				def __call__(      self   ,     snake_case = None   ,     snake_case = 1   ,     snake_case = 100   ,     snake_case = 0.0   ,     snake_case = None   ,     snake_case = "pil"   ,     snake_case = True   ,     ):
											if isinstance(snake_case   ,     PIL.Image.Image ):
																		snake_case_           =					1
											elif isinstance(snake_case   ,     torch.Tensor ):
																		snake_case_           =					image.shape[0]
											else:
																		raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case )}''' )

											if isinstance(snake_case   ,     PIL.Image.Image ):
																		snake_case_           =					preprocess(snake_case )

											snake_case_       ,					snake_case_           =					image.shape[-2:]

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

											snake_case_           =					randn_tensor(snake_case   ,     generator=snake_case   ,     device=self.device   ,     dtype=snake_case )

											snake_case_           =					image.to(device=self.device   ,     dtype=snake_case )

											# set timesteps and move to the correct device
											self.scheduler.set_timesteps(snake_case   ,     device=self.device )
											snake_case_           =					self.scheduler.timesteps

											# scale the initial noise by the standard deviation required by the scheduler
											snake_case_           =					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]
											snake_case_           =					'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
											snake_case_           =					{}
											if accepts_eta:
																		snake_case_           =					eta

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

											# decode the image latents with the VQVAE
											snake_case_           =					self.vqvae.decode(snake_case ).sample
											snake_case_           =					torch.clamp(snake_case   ,     -1.0   ,     1.0 )
											snake_case_           =					image / 2 + 0.5
											snake_case_           =					image.cpu().permute(0   ,     2   ,     3   ,     1 ).numpy()

											if output_type == "pil":
																		snake_case_           =					self.numpy_to_pil(snake_case )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=snake_case )







def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					(
							    first_str_length if first_str_length > second_str_length else second_str_length
							)
							snake_case_           =					[]
							for char_count in range(UpperCamelCase__   ):
														if char_count < first_str_length:
																					output_list.append(first_str[char_count]   )
														if char_count < second_str_length:
																					output_list.append(second_str[char_count]   )
							return "".join(UpperCamelCase__   )


if __name__ == "__main__":
					print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)




