






from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def        _a  (     UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				int   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						k_size // 2
						lowerCAmelCase__      ,							lowerCAmelCase__       =						mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						lowerCAmelCase__       =						1 / (2 * pi * sigma) * exp(-(square(UpperCamelCase_   ) + square(UpperCamelCase_   )) / (2 * square(UpperCamelCase_   ))   )
						return g


def        _a  (     UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Dict   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__      ,							lowerCAmelCase__       =						image.shape[0], image.shape[1]
						# dst image height and width
						lowerCAmelCase__       =						height - k_size + 1
						lowerCAmelCase__       =						width - k_size + 1

						# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
						lowerCAmelCase__       =						zeros((dst_height * dst_width, k_size * k_size)   )
						lowerCAmelCase__       =						0
						for i, j in product(range(UpperCamelCase_   )    ,   range(UpperCamelCase_   )   ):
												lowerCAmelCase__       =						ravel(image[i : i + k_size, j : j + k_size]   )
												lowerCAmelCase__       =						window
												row += 1

						#  turn the kernel into shape(k*k, 1)
						lowerCAmelCase__       =						gen_gaussian_kernel(UpperCamelCase_    ,   UpperCamelCase_   )
						lowerCAmelCase__       =						ravel(UpperCamelCase_   )

						# reshape and get the dst image
						lowerCAmelCase__       =						dot(UpperCamelCase_    ,   UpperCamelCase_   ).reshape(UpperCamelCase_    ,   UpperCamelCase_   ).astype(UpperCamelCase_   )

						return dst


if __name__ == "__main__":
	# read original image
	a_							=    imread(r'''../image_data/lena.jpg''')
	# turn image in gray scale value
	a_							=    cvtColor(img, COLOR_BGR2GRAY)

	# get values with two different mask size
	a_							=    gaussian_filter(gray, 3, sigma=1)
	a_							=    gaussian_filter(gray, 5, sigma=0.8)

	# show result images
	imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
	imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
	waitKey()














from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    )->					List[str]:







							'''simple docstring'''





							super().__init__()
							self.register_modules(transformer=__UpperCAmelCase		,    vae=__UpperCAmelCase		,    scheduler=__UpperCAmelCase       )

							# create a imagenet -> id dictionary for easier use
							lowerCAmelCase__       =						{}
							if idalabel is not None:
													for key, value in idalabel.items():
																			for label in value.split(","       ):
																									lowerCAmelCase__       =						int(__UpperCAmelCase       )
													lowerCAmelCase__       =						dict(sorted(self.labels.items()       )       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					List[int]:







							'''simple docstring'''





							if not isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						list(__UpperCAmelCase       )

							for l in label:
													if l not in self.labels:
																			raise ValueError(
																			    F"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."       )

							return [self.labels[l] for l in label]


	@torch.no_grad()
	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = 4.0		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 50		,    __UpperCAmelCase = "pil"		,    __UpperCAmelCase = True		,    )->					Union[ImagePipelineOutput, Tuple]:







							'''simple docstring'''





							lowerCAmelCase__       =						len(__UpperCAmelCase       )
							lowerCAmelCase__       =						self.transformer.config.sample_size
							lowerCAmelCase__       =						self.transformer.config.in_channels

							lowerCAmelCase__       =						randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)		,    generator=__UpperCAmelCase		,    device=self.device		,    dtype=self.transformer.dtype		,    )
							lowerCAmelCase__       =						torch.cat([latents] * 2       ) if guidance_scale > 1 else latents

							lowerCAmelCase__       =						torch.tensor(__UpperCAmelCase		,    device=self.device       ).reshape(-1       )
							lowerCAmelCase__       =						torch.tensor([1000] * batch_size		,    device=self.device       )
							lowerCAmelCase__       =						torch.cat([class_labels, class_null]		,    0       ) if guidance_scale > 1 else class_labels

							# set step values
							self.scheduler.set_timesteps(__UpperCAmelCase       )

							for t in self.progress_bar(self.scheduler.timesteps       ):
													if guidance_scale > 1:
																			lowerCAmelCase__       =						latent_model_input[: len(__UpperCAmelCase       ) // 2]
																			lowerCAmelCase__       =						torch.cat([half, half]		,    dim=0       )
													lowerCAmelCase__       =						self.scheduler.scale_model_input(__UpperCAmelCase		,    __UpperCAmelCase       )

													lowerCAmelCase__       =						t
													if not torch.is_tensor(__UpperCAmelCase       ):
																			# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																			# This would be a good case for the `match` statement (Python 3.10+)
																			lowerCAmelCase__       =						latent_model_input.device.type == "mps"
																			if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
																									lowerCAmelCase__       =						torch.floataa if is_mps else torch.floataa
																			else:
																									lowerCAmelCase__       =						torch.intaa if is_mps else torch.intaa
																			lowerCAmelCase__       =						torch.tensor([timesteps]		,    dtype=__UpperCAmelCase		,    device=latent_model_input.device       )
													elif len(timesteps.shape       ) == 0:
																			lowerCAmelCase__       =						timesteps[None].to(latent_model_input.device       )
													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													lowerCAmelCase__       =						timesteps.expand(latent_model_input.shape[0]       )
													# predict noise model_output
													lowerCAmelCase__       =						self.transformer(
													    __UpperCAmelCase		,    timestep=__UpperCAmelCase		,    class_labels=__UpperCAmelCase       ).sample

													# perform guidance
													if guidance_scale > 1:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						torch.split(__UpperCAmelCase		,    len(__UpperCAmelCase       ) // 2		,    dim=0       )

																			lowerCAmelCase__       =						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																			lowerCAmelCase__       =						torch.cat([half_eps, half_eps]		,    dim=0       )

																			lowerCAmelCase__       =						torch.cat([eps, rest]		,    dim=1       )

													# learned sigma
													if self.transformer.config.out_channels // 2 == latent_channels:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						torch.split(__UpperCAmelCase		,    __UpperCAmelCase		,    dim=1       )
													else:
																			lowerCAmelCase__       =						noise_pred

													# compute previous image: x_t -> x_t-1
													lowerCAmelCase__       =						self.scheduler.step(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       ).prev_sample

							if guidance_scale > 1:
													lowerCAmelCase__      ,							lowerCAmelCase__       =						latent_model_input.chunk(2		,    dim=0       )
							else:
													lowerCAmelCase__       =						latent_model_input

							lowerCAmelCase__       =						1 / self.vae.config.scaling_factor * latents
							lowerCAmelCase__       =						self.vae.decode(__UpperCAmelCase       ).sample

							lowerCAmelCase__       =						(samples / 2 + 0.5).clamp(0		,    1       )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							lowerCAmelCase__       =						samples.cpu().permute(0		,    2		,    3		,    1       ).float().numpy()

							if output_type == "pil":
													lowerCAmelCase__       =						self.numpy_to_pil(__UpperCAmelCase       )

							if not return_dict:
													return (samples,)

							return ImagePipelineOutput(images=__UpperCAmelCase       )






