





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				(	lowerCamelCase:   Optional[int] )	->						Dict:







      '''simple docstring'''






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







class A_					(			_lowerCamelCase   ):




   def __init__(self	:Optional[int]    ,   _UpperCamelCase	:VQModel    ,   _UpperCamelCase	:UNetaDModel    ,   _UpperCamelCase	:Union[
           DDIMScheduler,
           PNDMScheduler,
           LMSDiscreteScheduler,
           EulerDiscreteScheduler,
           EulerAncestralDiscreteScheduler,
           DPMSolverMultistepScheduler,
       ]    ,   )->					Dict:
         super().__init__()
         self.register_modules(vqvae=_UpperCamelCase    ,   unet=_UpperCamelCase    ,   scheduler=_UpperCamelCase )






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

         if isinstance(_UpperCamelCase    ,   PIL.Image.Image ):
               __A          =				preprocess(_UpperCamelCase )

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

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

         __A          =				randn_tensor(_UpperCamelCase    ,   generator=_UpperCamelCase    ,   device=self.device    ,   dtype=_UpperCamelCase )

         __A          =				image.to(device=self.device    ,   dtype=_UpperCamelCase )

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

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

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

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

         if output_type == "pil":
               __A          =				self.numpy_to_pil(_UpperCamelCase )

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=_UpperCamelCase )












def _a				(	lowerCamelCase:   int = 2_00 )	->						int:







      '''simple docstring'''






      __A          =				[1, 2, 5, 10, 20, 50, 1_00, 2_00]
      __A          =				[0] * (pence + 1)
      __A          =				1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(lowerCamelCase					,				pence + 1					,				1 ):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73682





