




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


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



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






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

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

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

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

											lowerCAmelCase   =		image.to(device=self.device       , dtype=UpperCAmelCase_						)

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

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

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

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

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

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=UpperCAmelCase_						)









def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations(_snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														return sum(count_of_possible_combinations(target - item							) for item in array							)

							return count_of_possible_combinations(_snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations_with_dp_array(
							    _snake_case						, _snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														lowerCAmelCase   =		sum(
														    count_of_possible_combinations_with_dp_array(target - item						, _snake_case							)
														    for item in array							)
														lowerCAmelCase   =		answer
														return answer

							lowerCAmelCase   =		[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		[0] * (target + 1)
							lowerCAmelCase   =		1

							for i in range(1						, target + 1							):
														for j in range(_snake_case							):
																					if i - array[j] >= 0:
																												dp_array[i] += dp_array[i - array[j]]

							return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCAmelCase_						=3
		UpperCAmelCase_						=5
		UpperCAmelCase_						=[1, 2, 5]
		print(combination_sum_iv(n, array, target))



