
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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	lowercase    ,   lowercase	    =       image.size
	lowercase    ,   lowercase	    =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
	lowercase	    =       image.resize((w, h)					,				resample=PIL_INTERPOLATION['lanczos']				)
	lowercase	    =       np.array(__SCREAMING_SNAKE_CASE				).astype(np.floataa				) / 2_55.0
	lowercase	    =       image[None].transpose(0					,				3					,				1					,				2				)
	lowercase	    =       torch.from_numpy(__SCREAMING_SNAKE_CASE				)
	return 2.0 * image - 1.0







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	def __init__(			self						,       snake_case						,       snake_case						,       snake_case						,       ):
		super().__init__()
		self.register_modules(vqvae=snake_case						,       unet=snake_case						,       scheduler=snake_case			)





	@torch.no_grad()
	def __call__(			self						,       snake_case = None						,       snake_case = 1						,       snake_case = 100						,       snake_case = 0.0						,       snake_case = None						,       snake_case = "pil"						,       snake_case = True						,       ):
		if isinstance(snake_case						,       PIL.Image.Image			):
			lowercase	    =       1
		elif isinstance(snake_case						,       torch.Tensor			):
			lowercase	    =       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			):
			lowercase	    =       preprocess(snake_case			)

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

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

		lowercase	    =       randn_tensor(snake_case						,       generator=snake_case						,       device=self.device						,       dtype=snake_case			)

		lowercase	    =       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			)
		lowercase	    =       self.scheduler.timesteps

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

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

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

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

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=snake_case			)







from collections import defaultdict





def 				A__ (snake_case   :		int				)     ->						int:
					__UpperCamelCase					:	Optional[int]             =      1
					__UpperCamelCase					:	Tuple             =      True
					for v in tree[start]:
										if v not in visited:
															ret += dfs(snake_case				)
					if ret % 2 == 0:
										cuts.append(snake_case				)
					return ret





def 				A__ ()     ->						str:
					dfs(1				)


if __name__ == "__main__":
	a__     ,							a__		     =     10, 9
	a__		     =     defaultdict(list)
	a__		     =     {}
	a__		     =     []
	a__		     =     0
	a__		     =     [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
	for u, v in edges:
		tree[u].append(v)
		tree[v].append(u)
	even_tree()
	print(len(cuts) - 1)






