



'''simple docstring'''

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_     (    __UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case							,  snake_case:    Tuple      							=image.size
			snake_case							,  snake_case:    Tuple      							=(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:    Tuple      							=np.array(__UpperCAmelCase					).astype(np.floataa					) / 255.0
			snake_case:    str      							=image[None].transpose(0				,							3				,							1				,							2					)
			snake_case:    Optional[int]      							=torch.from_numpy(__UpperCAmelCase					)
			return 2.0 * image - 1.0







class 						a_		(     snake_case    ):


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




							@torch.no_grad()
							def __call__(				self   :						str      ,						a_   :						Union[torch.Tensor, PIL.Image.Image] = None      ,						a_   :						Optional[int] = 1      ,						a_   :						Optional[int] = 1_0_0      ,						a_   :						Optional[float] = 0.0      ,						a_   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,						a_   :						Optional[str] = "pil"      ,						a_   :						bool = True      ,						)			->					Union[Tuple, ImagePipelineOutput]:
										if isinstance(a_      ,						PIL.Image.Image			):
													snake_case:    Dict      							=1
										elif isinstance(a_      ,						torch.Tensor			):
													snake_case:    str      							=image.shape[0]
										else:
													raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(a_			)}'''			)

										if isinstance(a_      ,						PIL.Image.Image			):
													snake_case:    Optional[Any]      							=preprocess(a_			)

										snake_case							,  snake_case:    Optional[int]      							=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:    Dict      							=next(self.unet.parameters()			).dtype

										snake_case:    List[str]      							=randn_tensor(a_      ,						generator=a_      ,						device=self.device      ,						dtype=a_			)

										snake_case:    Dict      							=image.to(device=self.device      ,						dtype=a_			)

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

										# scale the initial noise by the standard deviation required by the scheduler
										snake_case:    Optional[int]      							=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:    Optional[int]      							=eta

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

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

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

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=a_			)









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    int      							=0
			if start < end:
						snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=a[end]
						snake_case:    List[Any]      							=a[pivot]
						snake_case:    int      							=temp

						snake_case							,  snake_case:    Optional[int]      							=_in_place_partition(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							__UpperCAmelCase				,							p - 1					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							p + 1				,							__UpperCAmelCase					)
			return count







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=0
			snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[Any]      							=a[end]
			snake_case:    List[str]      							=a[pivot]
			snake_case:    Dict      							=temp
			snake_case:    Union[str, Any]      							=start - 1
			for index in range(__UpperCAmelCase				,							__UpperCAmelCase					):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									snake_case:    Optional[Any]      							=new_pivot_index + 1
									snake_case:    Dict      							=a[new_pivot_index]
									snake_case:    Union[str, Any]      							=a[index]
									snake_case:    List[Any]      							=temp

			snake_case:    Union[str, Any]      							=a[new_pivot_index + 1]
			snake_case:    Tuple      							=a[end]
			snake_case:    Union[str, Any]      							=temp
			return new_pivot_index + 1, count


a							   =				TemporaryFile()
a							   =				100  # 1000 elements are to be sorted


a   ,      a							   =				0, 1  # mean and standard deviation
a							   =				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
a							   =				np.load(outfile)
a							   =				len(M) - 1
a							   =				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)







print(z)




