


"""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  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ):


		'''simple docstring'''







		lowerCAmelCase					:					Optional[int]								=							image.size
		lowerCAmelCase					:					Union[str, Any]								=							(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
		lowerCAmelCase					:					List[Any]								=							image.resize((w, h)    ,				resample=PIL_INTERPOLATION["lanczos"]    )
		lowerCAmelCase					:					int								=							np.array(_lowerCamelCase    ).astype(np.floataa    ) / 2_5_5.0
		lowerCAmelCase					:					Tuple								=							image[None].transpose(0    ,				3    ,				1    ,				2    )
		lowerCAmelCase					:					Optional[Any]								=							torch.from_numpy(_lowerCamelCase    )
		return 2.0 * image - 1.0





class 				SCREAMING_SNAKE_CASE__    (		__lowercase    ):
							"""simple docstring"""


							def __init__(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					):






									"""simple docstring"""





									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		,					):






									"""simple docstring"""





									if isinstance(snake_case__		,					PIL.Image.Image						):
											lowerCAmelCase					:					Union[str, Any]								=							1
									elif isinstance(snake_case__		,					torch.Tensor						):
											lowerCAmelCase					:					List[Any]								=							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						):
											lowerCAmelCase					:					List[str]								=							preprocess(snake_case__						)

									lowerCAmelCase					:					int								=							image.shape[-2:]

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

									lowerCAmelCase					:					Optional[Any]								=							randn_tensor(snake_case__		,					generator=snake_case__		,					device=self.device		,					dtype=snake_case__						)

									lowerCAmelCase					:					Tuple								=							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						)
									lowerCAmelCase					:					List[Any]								=							self.scheduler.timesteps

									# scale the initial noise by the standard deviation required by the scheduler
									lowerCAmelCase					:					int								=							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					:					str								=							"""eta""" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
									lowerCAmelCase					:					Optional[int]								=							{}
									if accepts_eta:
											lowerCAmelCase					:					Tuple								=							eta

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

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

									if output_type == "pil":
											lowerCAmelCase					:					Optional[int]								=							self.numpy_to_pil(snake_case__						)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=snake_case__						)









"""simple docstring"""







import baseaa
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  return baseaa.baaencode(string.encode("utf-8"    )    )
def  a__      (    SCREAMING_SNAKE_CASE   :   bytes    ):


  '''simple docstring'''







  return baseaa.baadecode(SCREAMING_SNAKE_CASE    ).decode("utf-8"    )


if __name__ == "__main__":
     lowerCAmelCase__				    =       '''Hello World!'''
     lowerCAmelCase__				    =       baseaa_encode(test)
     print(encoded)

     lowerCAmelCase__				    =       baseaa_decode(encoded)
     print(decoded)





