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





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	):
          super().__init__()
          self.register_modules(vqvae=lowerCamelCase		,	unet=lowerCamelCase		,	scheduler=lowerCamelCase )



      @torch.no_grad()
      def __call__(			self		,	lowerCamelCase = None		,	lowerCamelCase = 1		,	lowerCamelCase = 1_00		,	lowerCamelCase = 0.0		,	lowerCamelCase = None		,	lowerCamelCase = "pil"		,	lowerCamelCase = True		,	):
          if isinstance(lowerCamelCase		,	PIL.Image.Image ):
              snake_case__						=				1
          elif isinstance(lowerCamelCase		,	torch.Tensor ):
              snake_case__						=				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__						=				preprocess(lowerCamelCase )

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

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

          snake_case__						=				randn_tensor(lowerCamelCase		,	generator=lowerCamelCase		,	device=self.device		,	dtype=lowerCamelCase )

          snake_case__						=				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__						=				self.scheduler.timesteps

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

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

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

          if output_type == "pil":
              snake_case__						=				self.numpy_to_pil(lowerCamelCase )

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=lowerCamelCase )


from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						,					snake_case__						=				set(__lowerCAmelCase							), [start]

    while stack:
        snake_case__						=				stack.pop()
        explored.add(__lowerCAmelCase							)
        # Differences from BFS:
        # 1) pop last element instead of first one
        # 2) add adjacent elements to stack without exploring them
        for adj in reversed(graph[v]							):
            if adj not in explored:
                stack.append(__lowerCAmelCase							)
    return explored


__magic_name__            =  {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, '''A'''))



