




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						(__A   :    List[str]       )   ->      Any:







     """simple docstring"""


     UpperCAmelCase_     ,       UpperCAmelCase_          =							image.size
     UpperCAmelCase_     ,       UpperCAmelCase_          =							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     UpperCAmelCase_          =							image.resize((w, h)   ,				resample=PIL_INTERPOLATION['''lanczos''']       )
     UpperCAmelCase_          =							np.array(__A       ).astype(np.floataa       ) / 255.0
     UpperCAmelCase_          =							image[None].transpose(0   ,				3   ,				1   ,				2       )
     UpperCAmelCase_          =							torch.from_numpy(__A       )
     return 2.0 * image - 1.0







class __snake_case			(			a		):







   def __init__(      self		:						int					,						_snake_case		:						VQModel					,						_snake_case		:						UNetaDModel					,						_snake_case		:						Union[
           DDIMScheduler,
           PNDMScheduler,
           LMSDiscreteScheduler,
           EulerDiscreteScheduler,
           EulerAncestralDiscreteScheduler,
           DPMSolverMultistepScheduler,
       ]					,						):

        """simple docstring"""


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







   @torch.no_grad()
   def __call__(      self		:						List[Any]					,						_snake_case		:						Union[torch.Tensor, PIL.Image.Image] = None					,						_snake_case		:						Optional[int] = 1					,						_snake_case		:						Optional[int] = 100					,						_snake_case		:						Optional[float] = 0.0					,						_snake_case		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None					,						_snake_case		:						Optional[str] = "pil"					,						_snake_case		:						bool = True					,						):

        """simple docstring"""


        if isinstance(_snake_case					,						PIL.Image.Image):
             UpperCAmelCase_          =							1
        elif isinstance(_snake_case					,						torch.Tensor):
             UpperCAmelCase_          =							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):
             UpperCAmelCase_          =							preprocess(_snake_case)

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

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

        UpperCAmelCase_          =							randn_tensor(_snake_case					,						generator=_snake_case					,						device=self.device					,						dtype=_snake_case)

        UpperCAmelCase_          =							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)
        UpperCAmelCase_          =							self.scheduler.timesteps

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

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

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

        if output_type == "pil":
             UpperCAmelCase_          =							self.numpy_to_pil(_snake_case)

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=_snake_case)












def 	_a      (						SCREAMING_SNAKE_CASE_							:				int = 1_00_00_00     ):
					__lowerCAmelCase				   =       [i - 1 for i in range(limit + 1     )]

					for i in range(2      ,      limit + 1     ):
										if phi[i] == i - 1:
															for j in range(2 * i      ,      limit + 1      ,      SCREAMING_SNAKE_CASE_     ):
																				phi[j] -= phi[j] // i

					return sum(phi[2 : limit + 1]     )


if __name__ == "__main__":
					print(solution())




