





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       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]			)   ->							int:




		'''simple docstring'''
		lowercase__   ,				lowercase__					:      Union[str, Any]      =		image.size
		lowercase__   ,				lowercase__					:      List[Any]      =		(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		lowercase__					:      List[str]      =		image.resize((w, h)   ,    resample=PIL_INTERPOLATION['lanczos']			)
		lowercase__					:      int      =		np.array(_lowercase			).astype(np.floataa			) / 255.0
		lowercase__					:      Tuple      =		image[None].transpose(0   ,    3   ,    1   ,    2			)
		lowercase__					:      Tuple      =		torch.from_numpy(_lowercase			)
		return 2.0 * image - 1.0





class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	def __init__(						self:						List[Any],					lowerCamelCase_:						VQModel,					lowerCamelCase_:						UNetaDModel,					lowerCamelCase_:						Union[
	        DDIMScheduler,
	        PNDMScheduler,
	        LMSDiscreteScheduler,
	        EulerDiscreteScheduler,
	        EulerAncestralDiscreteScheduler,
	        DPMSolverMultistepScheduler,
	    ],					):
			super().__init__()
			self.register_modules(vqvae=lowerCamelCase_,					unet=lowerCamelCase_,					scheduler=lowerCamelCase_			)





	@torch.no_grad()
	def __call__(						self:						List[str],					lowerCamelCase_:						Union[torch.Tensor, PIL.Image.Image] = None,					lowerCamelCase_:						Optional[int] = 1,					lowerCamelCase_:						Optional[int] = 100,					lowerCamelCase_:						Optional[float] = 0.0,					lowerCamelCase_:						Optional[Union[torch.Generator, List[torch.Generator]]] = None,					lowerCamelCase_:						Optional[str] = "pil",					lowerCamelCase_:						bool = True,					):
			if isinstance(lowerCamelCase_,					PIL.Image.Image			):
					lowercase__					:      Union[str, Any]      =		1
			elif isinstance(lowerCamelCase_,					torch.Tensor			):
					lowercase__					:      Dict      =		image.shape[0]
			else:
					raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCamelCase_			)}"""			)

			if isinstance(lowerCamelCase_,					PIL.Image.Image			):
					lowercase__					:      Union[str, Any]      =		preprocess(lowerCamelCase_			)

			lowercase__   ,				lowercase__					:      Optional[Any]      =		image.shape[-2:]

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

			lowercase__					:      Any      =		randn_tensor(lowerCamelCase_,					generator=lowerCamelCase_,					device=self.device,					dtype=lowerCamelCase_			)

			lowercase__					:      Dict      =		image.to(device=self.device,					dtype=lowerCamelCase_			)

			# set timesteps and move to the correct device
			self.scheduler.set_timesteps(lowerCamelCase_,					device=self.device			)
			lowercase__					:      List[Any]      =		self.scheduler.timesteps

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

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

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

			if output_type == "pil":
					lowercase__					:      Any      =		self.numpy_to_pil(lowerCamelCase_			)

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=lowerCamelCase_			)









def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str			)   ->							str:




		'''simple docstring'''
		if not all(char in '01' for char in bin_string			):
				raise ValueError('Non-binary value was passed to the function'			)
		if not bin_string:
				raise ValueError('Empty string was passed to the function'			)
		lowercase__					:      Tuple      =		''
		while len(_lowercase			) % 3 != 0:
				lowercase__					:      Optional[int]      =		'0' + bin_string
		lowercase__					:      List[Any]      =		[
		    bin_string[index : index + 3]
		    for index in range(len(_lowercase			)			)
		    if index % 3 == 0
		]
		for bin_group in bin_string_in_3_list:
				lowercase__					:      int      =		0
				for index, val in enumerate(_lowercase			):
						oct_val += int(2 ** (2 - index) * int(_lowercase			)			)
				oct_string += str(_lowercase			)
		return oct_string


if __name__ == "__main__":
			from doctest import testmod

			testmod()


