






'''simple docstring'''





import cva
import numpy as np
def __UpperCAmelCase (			A			:	np.ndarray	,   A			:	int	,   A			:	int	,   A			:	int						)  -> int:

			try:
						return int(image[x_coordinate][y_coordinate] >= center						)
			except (IndexError, TypeError):
						return 0
def __UpperCAmelCase (			A			:	np.ndarray	,   A			:	int	,   A			:	int						)  -> int:
			UpperCAmelCase_     :   Optional[Any]       =   image[x_coordinate][y_coordinate]
			UpperCAmelCase_     :   int       =   [1, 2, 4, 8, 1_6, 3_2, 6_4, 1_2_8]

			# skip get_neighbors_pixel if center is null
			if center is None:
						return 0

			# Starting from the top right, assigning value to pixels clockwise
			UpperCAmelCase_     :   Union[str, Any]       =   [
			    get_neighbors_pixel(A	,   x_coordinate - 1	,   y_coordinate + 1	,   A						),
			    get_neighbors_pixel(A	,   A	,   y_coordinate + 1	,   A						),
			    get_neighbors_pixel(A	,   x_coordinate - 1	,   A	,   A						),
			    get_neighbors_pixel(A	,   x_coordinate + 1	,   y_coordinate + 1	,   A						),
			    get_neighbors_pixel(A	,   x_coordinate + 1	,   A	,   A						),
			    get_neighbors_pixel(A	,   x_coordinate + 1	,   y_coordinate - 1	,   A						),
			    get_neighbors_pixel(A	,   A	,   y_coordinate - 1	,   A						),
			    get_neighbors_pixel(A	,   x_coordinate - 1	,   y_coordinate - 1	,   A						),
			]

			# Converting the binary value to decimal.
			return sum(
			    binary_value * power for binary_value, power in zip(A	,   A						)						)


if __name__ == "__main__":
						# Reading the image and converting it to grayscale.
						_UpperCamelCase		:		str								=			cva.imread(
						    'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
						)

						# Create a numpy array as the same height and width of read image
						_UpperCamelCase		:		Union[str, Any]								=			np.zeros((image.shape[0], image.shape[1]))

						# Iterating through the image and calculating the
						# local binary pattern value for each pixel.
						for i in range(0, image.shape[0]):
												for j in range(0, image.shape[1]):
																		_UpperCamelCase		:		Tuple								=			local_binary_value(image, i, j)

						cva.imshow('local binary pattern', lbp_image)
						cva.waitKey(0)
						cva.destroyAllWindows()










'''simple docstring'''





from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class 				snake_case__     (			UpperCamelCase):




					def __init__(       self       :						Optional[int]							,				_A       :						AutoencoderKL							,				_A       :						CLIPTextModel							,				_A       :						CLIPTokenizer							,				_A       :						UNetaDConditionModel							,				_A       :						DDIMScheduler							,				_A       :						float = 0.93							,				_A       :						bool = True							,				)  ->    Dict:
								UpperCAmelCase_     :   Tuple       =   mixing_coeff
								UpperCAmelCase_     :   Union[str, Any]       =   leapfrog_steps

								super().__init__()
								self.register_modules(
								    vae=_A							,				text_encoder=_A							,				tokenizer=_A							,				unet=_A							,				scheduler=_A							,				)

								UpperCAmelCase_     :   Optional[Any]       =   2 ** (len(self.vae.config.block_out_channels     ) - 1)
								UpperCAmelCase_     :   Optional[Any]       =   VaeImageProcessor(vae_scale_factor=self.vae_scale_factor     )




					def 						A     (       self       :						Optional[int]							,				_A       :						str							,				_A       :						Optional[str] = None							,				_A       :						bool = False     )  ->    Dict:
								UpperCAmelCase_     :   int       =   self.tokenizer(
								    _A							,				padding='''max_length'''							,				max_length=self.tokenizer.model_max_length							,				truncation=_A							,				return_tensors='''pt'''							,				)

								UpperCAmelCase_     :   Tuple       =   self.text_encoder(text_inputs.input_ids.to(self.device     )     ).last_hidden_state

								UpperCAmelCase_     :   Tuple       =   prompt_embeds.to(dtype=self.text_encoder.dtype							,				device=self.device     )

								if do_classifier_free_guidance:
											UpperCAmelCase_     :   Optional[int]       =   '''''' if negative_prompt is None else negative_prompt

											UpperCAmelCase_     :   Union[str, Any]       =   self.tokenizer(
											    _A							,				padding='''max_length'''							,				max_length=self.tokenizer.model_max_length							,				truncation=_A							,				return_tensors='''pt'''							,				)

											UpperCAmelCase_     :   List[str]       =   self.text_encoder(uncond_input.input_ids.to(self.device     )     ).last_hidden_state

											UpperCAmelCase_     :   Tuple       =   torch.cat([negative_prompt_embeds, prompt_embeds]     )

								return prompt_embeds




					def 						A     (       self       :						Optional[int]							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor     )  ->    Tuple:
								UpperCAmelCase_     :   Union[str, Any]       =   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
								UpperCAmelCase_     :   Any       =   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

								return [x, y]




					def 						A     (       self       :						Union[str, Any]							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor     )  ->    Any:
								UpperCAmelCase_     :   Optional[Any]       =   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								UpperCAmelCase_     :   int       =   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]




					def 						A     (       self       :						List[str]							,				_A       :						torch.Tensor     )  ->    List[Any]:
								# as self.alphas_cumprod is always in cpu
								UpperCAmelCase_     :   Tuple       =   int(_A     )

								UpperCAmelCase_     :   List[str]       =   self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

								return alpha_prod, 1 - alpha_prod




					def 						A     (       self       :						Dict							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				)  ->    Tuple:
								UpperCAmelCase_     :   str       =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								UpperCAmelCase_  ,     UpperCAmelCase_     :   int       =   self._get_alpha_and_beta(_A     )
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   self._get_alpha_and_beta(_A     )

								UpperCAmelCase_     :   Optional[Any]       =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
								UpperCAmelCase_     :   Tuple       =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

								UpperCAmelCase_     :   List[str]       =   (base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype     )




					def 						A     (       self       :						Any							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				)  ->    Union[str, Any]:
								UpperCAmelCase_     :   Tuple       =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   self._get_alpha_and_beta(_A     )
								UpperCAmelCase_  ,     UpperCAmelCase_     :   int       =   self._get_alpha_and_beta(_A     )

								UpperCAmelCase_     :   Any       =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
								UpperCAmelCase_     :   Optional[int]       =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
								UpperCAmelCase_     :   List[Any]       =   a_t * base + b_t * model_output

								return model_input, next_model_input.to(base.dtype     )




					@torch.no_grad()
					def 						A     (       self       :						int							,				_A       :						torch.Tensor     )  ->    List[Any]:
								UpperCAmelCase_     :   Union[str, Any]       =   1 / self.vae.config.scaling_factor * latents
								UpperCAmelCase_     :   str       =   self.vae.decode(_A     ).sample
								UpperCAmelCase_     :   Any       =   (image / 2 + 0.5).clamp(0							,				1     )
								return image




					@torch.no_grad()
					def 						A     (       self       :						int							,				_A       :						Image.Image							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						float							,				_A       :						Optional[torch.Generator] = None							,				)  ->    Any:
								UpperCAmelCase_     :   Optional[Any]       =   guidance_scale > 1.0

								UpperCAmelCase_     :   str       =   image.to(device=self.device							,				dtype=text_embeds.dtype     )
								UpperCAmelCase_     :   int       =   self.vae.encode(_A     ).latent_dist.sample(_A     )

								UpperCAmelCase_     :   List[str]       =   self.vae.config.scaling_factor * latent

								UpperCAmelCase_     :   Optional[int]       =   [latent.clone(), latent.clone()]

								for i, t in tqdm(enumerate(_A     )							,				total=len(_A     )     ):
											UpperCAmelCase_     :   List[str]       =   self.noise_mixing_layer(x=coupled_latents[0]							,				y=coupled_latents[1]     )

											# j - model_input index, k - base index
											for j in range(2     ):
														UpperCAmelCase_     :   List[Any]       =   j ^ 1

														if self.leapfrog_steps:
																	if i % 2 == 0:
																				UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   j, k

														UpperCAmelCase_     :   str       =   coupled_latents[j]
														UpperCAmelCase_     :   List[Any]       =   coupled_latents[k]

														UpperCAmelCase_     :   Optional[int]       =   torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

														UpperCAmelCase_     :   Dict       =   self.unet(_A							,				_A							,				encoder_hidden_states=_A     ).sample

														if do_classifier_free_guidance:
																	UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   noise_pred.chunk(2     )
																	UpperCAmelCase_     :   List[str]       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

														UpperCAmelCase_  ,     UpperCAmelCase_     :   Union[str, Any]       =   self.noise_step(
														    base=_A							,				model_input=_A							,				model_output=_A							,				timestep=_A							,				)

														UpperCAmelCase_     :   str       =   model_input

								return coupled_latents





					@torch.no_grad()
					def __call__(       self       :						Any							,				_A       :						str							,				_A       :						str							,				_A       :						Image.Image							,				_A       :						float = 3.0							,				_A       :						int = 50							,				_A       :						float = 0.8							,				_A       :						Optional[str] = None							,				_A       :						Optional[torch.Generator] = None							,				_A       :						Optional[str] = "pil"							,				)  ->    Union[str, Any]:
								UpperCAmelCase_     :   List[Any]       =   guidance_scale > 1.0

								UpperCAmelCase_     :   Optional[Any]       =   self.image_processor.preprocess(_A     )

								UpperCAmelCase_     :   str       =   self._encode_prompt(_A							,				_A							,				_A     )
								UpperCAmelCase_     :   int       =   self._encode_prompt(_A							,				_A							,				_A     )

								self.scheduler.set_timesteps(_A							,				self.device     )

								UpperCAmelCase_     :   Any       =   num_inference_steps - int(num_inference_steps * strength     )
								UpperCAmelCase_     :   Tuple       =   self.scheduler.timesteps[t_limit:]
								UpperCAmelCase_     :   Any       =   fwd_timesteps.flip(0     )

								UpperCAmelCase_     :   Optional[int]       =   self.prepare_latents(_A							,				_A							,				_A							,				_A							,				_A     )

								for i, t in tqdm(enumerate(_A     )							,				total=len(_A     )     ):
											# j - model_input index, k - base index
											for k in range(2     ):
														UpperCAmelCase_     :   Union[str, Any]       =   k ^ 1

														if self.leapfrog_steps:
																	if i % 2 == 1:
																				UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   j, k

														UpperCAmelCase_     :   Tuple       =   coupled_latents[j]
														UpperCAmelCase_     :   Optional[int]       =   coupled_latents[k]

														UpperCAmelCase_     :   Union[str, Any]       =   torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

														UpperCAmelCase_     :   int       =   self.unet(_A							,				_A							,				encoder_hidden_states=_A     ).sample

														if do_classifier_free_guidance:
																	UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   noise_pred.chunk(2     )
																	UpperCAmelCase_     :   Any       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

														UpperCAmelCase_  ,     UpperCAmelCase_     :   Any       =   self.denoise_step(
														    base=_A							,				model_input=_A							,				model_output=_A							,				timestep=_A							,				)

														UpperCAmelCase_     :   str       =   model_input

											UpperCAmelCase_     :   str       =   self.denoise_mixing_layer(x=coupled_latents[0]							,				y=coupled_latents[1]     )

								# either one is fine
								UpperCAmelCase_     :   Union[str, Any]       =   coupled_latents[0]

								if output_type not in ["latent", "pt", "np", "pil"]:
											UpperCAmelCase_     :   str       =   (
											    F"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: "
											    '''`pil`, `np`, `pt`, `latent`'''
											)
											deprecate('''Unsupported output_type'''							,				'''1.0.0'''							,				_A							,				standard_warn=_A     )
											UpperCAmelCase_     :   int       =   '''np'''

								if output_type == "latent":
											UpperCAmelCase_     :   Tuple       =   final_latent
								else:
											UpperCAmelCase_     :   Optional[int]       =   self.decode_latents(_A     )
											UpperCAmelCase_     :   str       =   self.image_processor.postprocess(_A							,				output_type=_A     )

								return image


