



import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


lowerCAmelCase					:				Optional[int]   	=logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	_a			(      snake_case_  ):
			_UpperCamelCase:   torch.FloatTensor
			_UpperCamelCase:   Optional[torch.FloatTensor]	     =		None







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=0.999			,SCREAMING_SNAKE_CASE__="cosine"			,):


						'''simple docstring'''




						if alpha_transform_type == "cosine":

												def alpha_bar_fn(SCREAMING_SNAKE_CASE__  ):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(SCREAMING_SNAKE_CASE__  ):
																		return math.exp(t * -12.0  )

						else:
												raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""  )

						lowerCAmelCase	:   Optional[Any]					     =					[]
						for i in range(SCREAMING_SNAKE_CASE__  ):
												lowerCAmelCase	:   str					     =					i / num_diffusion_timesteps
												lowerCAmelCase	:   int					     =					(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__  ) / alpha_bar_fn(SCREAMING_SNAKE_CASE__  )			,SCREAMING_SNAKE_CASE__  )  )
						return torch.tensor(SCREAMING_SNAKE_CASE__			,dtype=torch.floataa  )






class 	_a			(      snake_case_     , snake_case_  ):
			_UpperCamelCase:   Optional[int]	     =		[e.name for e in KarrasDiffusionSchedulers]
			_UpperCamelCase:   str	     =		1






			@register_to_config
			def __init__(    self    ,  lowercase_ = 1000    ,  lowercase_ = 0.0_0_0_1    ,  lowercase_ = 0.0_2    ,  lowercase_ = "linear"    ,  lowercase_ = None    ,  lowercase_ = "epsilon"    ,  lowercase_ = "linspace"    ,  lowercase_ = 0    ,  )	->					List[str]:
									if trained_betas is not None:
															lowerCAmelCase	:   int					     =					torch.tensor(lowercase_    ,  dtype=torch.floataa )
									elif beta_schedule == "linear":
															lowerCAmelCase	:   List[Any]					     =					torch.linspace(lowercase_    ,  lowercase_    ,  lowercase_    ,  dtype=torch.floataa )
									elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															lowerCAmelCase	:   Any					     =					(
															    torch.linspace(beta_start**0.5    ,  beta_end**0.5    ,  lowercase_    ,  dtype=torch.floataa ) ** 2
															)
									elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															lowerCAmelCase	:   Tuple					     =					betas_for_alpha_bar(lowercase_ )
									else:
															raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )

									lowerCAmelCase	:   Optional[Any]					     =					1.0 - self.betas
									lowerCAmelCase	:   Any					     =					torch.cumprod(self.alphas    ,  dim=0 )

									lowerCAmelCase	:   int					     =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
									lowerCAmelCase	:   List[str]					     =					np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
									lowerCAmelCase	:   List[Any]					     =					torch.from_numpy(lowercase_ )

									# setable values
									lowerCAmelCase	:   Optional[int]					     =					None
									lowerCAmelCase	:   Tuple					     =					np.linspace(0    ,  num_train_timesteps - 1    ,  lowercase_    ,  dtype=lowercase_ )[::-1].copy()
									lowerCAmelCase	:   str					     =					torch.from_numpy(lowercase_ )
									lowerCAmelCase	:   List[str]					     =					False






			@property
			def 							_snake_case   (    self )	->					List[str]:
									# standard deviation of the initial noise distribution
									if self.config.timestep_spacing in ["linspace", "trailing"]:
															return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					torch.FloatTensor:
									if isinstance(lowercase_    ,  torch.Tensor ):
															lowerCAmelCase	:   Union[str, Any]					     =					timestep.to(self.timesteps.device )
									lowerCAmelCase	:   Dict					     =					(self.timesteps == timestep).nonzero().item()
									lowerCAmelCase	:   List[str]					     =					self.sigmas[step_index]
									lowerCAmelCase	:   Optional[Any]					     =					sample / ((sigma**2 + 1) ** 0.5)
									lowerCAmelCase	:   Any					     =					True
									return sample






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ = None )	->					List[Any]:
									lowerCAmelCase	:   str					     =					num_inference_steps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
															lowerCAmelCase	:   int					     =					np.linspace(0    ,  self.config.num_train_timesteps - 1    ,  lowercase_    ,  dtype=lowercase_ )[
															    ::-1
															].copy()
									elif self.config.timestep_spacing == "leading":
															lowerCAmelCase	:   Optional[int]					     =					self.config.num_train_timesteps // self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															lowerCAmelCase	:   Optional[int]					     =					(np.arange(0    ,  lowercase_ ) * step_ratio).round()[::-1].copy().astype(lowercase_ )
															timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
															lowerCAmelCase	:   Optional[Any]					     =					self.config.num_train_timesteps / self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															lowerCAmelCase	:   str					     =					(np.arange(self.config.num_train_timesteps    ,  0    ,  -step_ratio )).round().copy().astype(lowercase_ )
															timesteps -= 1
									else:
															raise ValueError(
															    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )

									lowerCAmelCase	:   List[Any]					     =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
									lowerCAmelCase	:   Optional[Any]					     =					np.interp(lowercase_    ,  np.arange(0    ,  len(lowercase_ ) )    ,  lowercase_ )
									lowerCAmelCase	:   Any					     =					np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
									lowerCAmelCase	:   Any					     =					torch.from_numpy(lowercase_ ).to(device=lowercase_ )
									if str(lowercase_ ).startswith("""mps""" ):
															# mps does not support float64
															lowerCAmelCase	:   int					     =					torch.from_numpy(lowercase_ ).to(lowercase_    ,  dtype=torch.floataa )
									else:
															lowerCAmelCase	:   Union[str, Any]					     =					torch.from_numpy(lowercase_ ).to(device=lowercase_ )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ = None    ,  lowercase_ = True    ,  )	->					Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

									if (
									    isinstance(lowercase_    ,  lowercase_ )
									    or isinstance(lowercase_    ,  torch.IntTensor )
									    or isinstance(lowercase_    ,  torch.LongTensor )
									):
															raise ValueError(
															    (
															        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
															        """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
															        """ one of the `scheduler.timesteps` as a timestep."""
															    )    ,  )

									if not self.is_scale_input_called:
															logger.warning(
															    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
															    """See `StableDiffusionPipeline` for a usage example.""" )

									if isinstance(lowercase_    ,  torch.Tensor ):
															lowerCAmelCase	:   Any					     =					timestep.to(self.timesteps.device )

									lowerCAmelCase	:   Tuple					     =					(self.timesteps == timestep).nonzero().item()
									lowerCAmelCase	:   Dict					     =					self.sigmas[step_index]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
															lowerCAmelCase	:   Any					     =					sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															lowerCAmelCase	:   Optional[int]					     =					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									elif self.config.prediction_type == "sample":
															raise NotImplementedError("""prediction_type not implemented yet: sample""" )
									else:
															raise ValueError(
															    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )

									lowerCAmelCase	:   Any					     =					self.sigmas[step_index]
									lowerCAmelCase	:   Tuple					     =					self.sigmas[step_index + 1]
									lowerCAmelCase	:   Tuple					     =					(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
									lowerCAmelCase	:   Union[str, Any]					     =					(sigma_to**2 - sigma_up**2) ** 0.5

									# 2. Convert to an ODE derivative
									lowerCAmelCase	:   Dict					     =					(sample - pred_original_sample) / sigma

									lowerCAmelCase	:   Dict					     =					sigma_down - sigma

									lowerCAmelCase	:   List[str]					     =					sample + derivative * dt

									lowerCAmelCase	:   str					     =					model_output.device
									lowerCAmelCase	:   str					     =					randn_tensor(model_output.shape    ,  dtype=model_output.dtype    ,  device=lowercase_    ,  generator=lowercase_ )

									lowerCAmelCase	:   Dict					     =					prev_sample + noise * sigma_up

									if not return_dict:
															return (prev_sample,)

									return EulerAncestralDiscreteSchedulerOutput(
									    prev_sample=lowercase_    ,  pred_original_sample=lowercase_ )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  )	->					torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									lowerCAmelCase	:   List[Any]					     =					self.sigmas.to(device=original_samples.device    ,  dtype=original_samples.dtype )
									if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_ ):
															# mps does not support float64
															lowerCAmelCase	:   int					     =					self.timesteps.to(original_samples.device    ,  dtype=torch.floataa )
															lowerCAmelCase	:   Dict					     =					timesteps.to(original_samples.device    ,  dtype=torch.floataa )
									else:
															lowerCAmelCase	:   int					     =					self.timesteps.to(original_samples.device )
															lowerCAmelCase	:   str					     =					timesteps.to(original_samples.device )

									lowerCAmelCase	:   List[str]					     =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

									lowerCAmelCase	:   Tuple					     =					sigmas[step_indices].flatten()
									while len(sigma.shape ) < len(original_samples.shape ):
															lowerCAmelCase	:   Any					     =					sigma.unsqueeze(-1 )

									lowerCAmelCase	:   Optional[Any]					     =					original_samples + noise * sigma
									return noisy_samples






			def __len__(    self )	->					str:
									return self.config.num_train_timesteps






import cva
import numpy as np







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						try:
												return int(image[x_coordinate][y_coordinate] >= center  )
						except (IndexError, TypeError):
												return 0







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   Any					     =					image[x_coordinate][y_coordinate]
						lowerCAmelCase	:   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
						lowerCAmelCase	:   Union[str, Any]					     =					[
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate - 1			,y_coordinate + 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,y_coordinate + 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate - 1			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate + 1			,y_coordinate + 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate + 1			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate + 1			,y_coordinate - 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,y_coordinate - 1			,SCREAMING_SNAKE_CASE__  ),
						    get_neighbors_pixel(SCREAMING_SNAKE_CASE__			,x_coordinate - 1			,y_coordinate - 1			,SCREAMING_SNAKE_CASE__  ),
						]

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


if __name__ == "__main__":
		# Reading the image and converting it to grayscale.
		lowerCAmelCase					:				Dict   	=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
		lowerCAmelCase					:				Dict   	=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]):
						lowerCAmelCase					:				Optional[int]   	=local_binary_value(image, i, j)

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

