




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




class 						__lowercase		(lowerCamelCase__							):




      """simple docstring"""





      def __init__(				self    ,				A    ,				A    ,				A    ,				)		->    Dict:
         super().__init__()
         self.register_modules(vqvae=__lowerCamelCase    ,				unet=__lowerCamelCase    ,				scheduler=__lowerCamelCase     )

      @torch.no_grad()
      def __call__(				self    ,				A = None    ,				A = 1    ,				A = 1_0_0    ,				A = 0.0    ,				A = None    ,				A = "pil"    ,				A = True    ,				)		->    Union[Tuple, ImagePipelineOutput]:






         if isinstance(__lowerCamelCase    ,				PIL.Image.Image     ):
            snake_case				:    Optional[int]           =	1
         elif isinstance(__lowerCamelCase    ,				torch.Tensor     ):
            snake_case				:    List[str]           =	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     ):
            snake_case				:    Tuple           =	preprocess(__lowerCamelCase     )

         snake_case				:    Dict           =	image.shape[-2:]

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

         snake_case				:    Union[str, Any]           =	randn_tensor(__lowerCamelCase    ,				generator=__lowerCamelCase    ,				device=self.device    ,				dtype=__lowerCamelCase     )

         snake_case				:    Any           =	image.to(device=self.device    ,				dtype=__lowerCamelCase     )

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

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

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

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

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

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=__lowerCamelCase     )







def 						SCREAMING_SNAKE_CASE__ (   )  ->       Dict:
   snake_case				:    Optional[int]           =	[]
   snake_case				:    Tuple           =	1

   while len(lowercase					) < 1E6:
      constant.append(str(lowercase					)					)
      i += 1

   snake_case				:    int           =	"""""".join(lowercase					)

   return (
       int(constant[0]					)
       * int(constant[9]					)
       * int(constant[99]					)
       * int(constant[999]					)
       * int(constant[9999]					)
       * int(constant[99999]					)
       * int(constant[999999]					)
   )


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





