

"""simple docstring"""
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 SchedulerMixin


A									=						logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class        _UpperCamelCase	(						snake_case_	):


  """simple docstring"""





  snake_case_        =    42

class        _UpperCamelCase	(						snake_case_   ,				snake_case_	):


  """simple docstring"""





  snake_case_        =    1



  @register_to_config
  def __init__( self					: str       ,       snake_case					: Dict = 40       ,       snake_case					: Union[str, Any] = 0.002       ,       snake_case					: Dict = 80.0       ,       snake_case					: List[str] = 0.5       ,       snake_case					: Optional[Any] = 1.0       ,       snake_case					: Dict = 7.0       ,       snake_case					: Union[str, Any] = True       ,       )	->			Any:


         '''simple docstring'''





         __magic_name__			:		List[Any]        =     sigma_max

         __magic_name__			:		Union[str, Any]        =     np.linspace(0       ,       1       ,       lowercase_      )
         __magic_name__			:		Tuple        =     self._convert_to_karras(lowercase_      )
         __magic_name__			:		List[Any]        =     self.sigma_to_t(lowercase_      )

         # setable values
         __magic_name__			:		Optional[int]        =     None
         __magic_name__			:		Dict        =     torch.from_numpy(lowercase_      )
         __magic_name__			:		Optional[Any]        =     torch.from_numpy(lowercase_      )
         __magic_name__			:		Optional[int]        =     False
         __magic_name__			:		List[str]        =     False



  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: List[str]       ,       snake_case					: List[Any]=None      )	->			Optional[Any]:


         '''simple docstring'''





         if schedule_timesteps is None:
                __magic_name__			:		Dict        =     self.timesteps

         __magic_name__			:		int        =     (schedule_timesteps == timestep).nonzero()
         return indices.item()



  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: Optional[Any]       ,       snake_case					: int      )	->			torch.FloatTensor:


         '''simple docstring'''





         if isinstance(lowercase_       ,       torch.Tensor      ):
                __magic_name__			:		Optional[Any]        =     timestep.to(self.timesteps.device      )
         __magic_name__			:		Dict        =     self.index_for_timestep(lowercase_      )
         __magic_name__			:		Any        =     self.sigmas[step_idx]

         __magic_name__			:		Tuple        =     sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

         __magic_name__			:		Dict        =     True
         return sample



  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: List[str]      )	->			Tuple:


         '''simple docstring'''





         if not isinstance(lowercase_       ,       np.ndarray      ):
                __magic_name__			:		Optional[int]        =     np.array(lowercase_       ,       dtype=np.floataa      )

         __magic_name__			:		Tuple        =     1000 * 0.25 * np.log(sigmas + 1e-44      )

         return timesteps



  def 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: Optional[Any] = None       ,       snake_case					: Optional[int] = None       ,       snake_case					: Optional[Any] = None       ,       )	->			str:


         '''simple docstring'''





         if num_inference_steps is None and timesteps is None:
                raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''      )

         if num_inference_steps is not None and timesteps is not None:
                raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''      )

         # Follow DDPMScheduler custom timesteps logic
         if timesteps is not None:
                for i in range(1       ,       len(lowercase_      )      ):
                       if timesteps[i] >= timesteps[i - 1]:
                              raise ValueError('''`timesteps` must be in descending order.'''      )

                if timesteps[0] >= self.config.num_train_timesteps:
                       raise ValueError(
                           f"""`timesteps` must start before `self.config.train_timesteps`:"""
                           f""" {self.config.num_train_timesteps}."""      )

                __magic_name__			:		str        =     np.array(lowercase_       ,       dtype=np.intaa      )
                __magic_name__			:		Optional[Any]        =     True
         else:
                if num_inference_steps > self.config.num_train_timesteps:
                       raise ValueError(
                           f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
                           f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
                           f""" maximal {self.config.num_train_timesteps} timesteps."""      )

                __magic_name__			:		Dict        =     num_inference_steps

                __magic_name__			:		Any        =     self.config.num_train_timesteps // self.num_inference_steps
                __magic_name__			:		List[str]        =     (np.arange(0       ,       lowercase_      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
                __magic_name__			:		List[Any]        =     False

         # Map timesteps to Karras sigmas directly for multistep sampling
         # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
         __magic_name__			:		str        =     self.config.num_train_timesteps
         __magic_name__			:		int        =     timesteps[::-1].copy()
         __magic_name__			:		Tuple        =     ramp / (num_train_timesteps - 1)
         __magic_name__			:		Any        =     self._convert_to_karras(lowercase_      )
         __magic_name__			:		str        =     self.sigma_to_t(lowercase_      )

         __magic_name__			:		int        =     np.concatenate([sigmas, [self.sigma_min]]      ).astype(np.floataa      )
         __magic_name__			:		List[Any]        =     torch.from_numpy(lowercase_      ).to(device=lowercase_      )

         if str(lowercase_      ).startswith('''mps'''      ):
                # mps does not support float64
                __magic_name__			:		List[Any]        =     torch.from_numpy(lowercase_      ).to(lowercase_       ,       dtype=torch.floataa      )
         else:
                __magic_name__			:		str        =     torch.from_numpy(lowercase_      ).to(device=lowercase_      )



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: Optional[Any]      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		float        =     self.config.sigma_min
         __magic_name__			:		float        =     self.config.sigma_max

         __magic_name__			:		int        =     self.config.rho
         __magic_name__			:		List[Any]        =     sigma_min ** (1 / rho)
         __magic_name__			:		Any        =     sigma_max ** (1 / rho)
         __magic_name__			:		Optional[Any]        =     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas



  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: Tuple      )	->			int:


         '''simple docstring'''





         __magic_name__			:		List[str]        =     self.config.sigma_data

         __magic_name__			:		Union[str, Any]        =     sigma_data**2 / (sigma**2 + sigma_data**2)
         __magic_name__			:		Dict        =     sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out



  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: List[str]      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		List[str]        =     self.config.sigma_min
         __magic_name__			:		Any        =     self.config.sigma_data

         __magic_name__			:		str        =     sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
         __magic_name__			:		Tuple        =     (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out



  def 							_UpperCAmelCase      ( self					: Any       ,       snake_case					: int       ,       snake_case					: Tuple       ,       snake_case					: str       ,       snake_case					: List[str] = None       ,       snake_case					: Any = True       ,       )	->			Union[CMStochasticIterativeSchedulerOutput, Tuple]:


         '''simple docstring'''





         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'''
                        f""" `{self.__class__}.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      ):
                __magic_name__			:		Dict        =     timestep.to(self.timesteps.device      )

         __magic_name__			:		Union[str, Any]        =     self.config.sigma_min
         __magic_name__			:		Any        =     self.config.sigma_max

         __magic_name__			:		int        =     self.index_for_timestep(lowercase_      )

         # sigma_next corresponds to next_t in original implementation
         __magic_name__			:		Union[str, Any]        =     self.sigmas[step_index]
         if step_index + 1 < self.config.num_train_timesteps:
                __magic_name__			:		Union[str, Any]        =     self.sigmas[step_index + 1]
         else:
                # Set sigma_next to sigma_min
                __magic_name__			:		Optional[int]        =     self.sigmas[-1]

         # Get scalings for boundary conditions
         __magic_name__			:		List[Any]        =     self.get_scalings_for_boundary_condition(lowercase_      )

         # 1. Denoise model output using boundary conditions
         __magic_name__			:		int        =     c_out * model_output + c_skip * sample
         if self.config.clip_denoised:
                __magic_name__			:		Optional[int]        =     denoised.clamp(-1       ,       1      )

         # 2. Sample z ~ N(0, s_noise^2 * I)
         # Noise is not used for onestep sampling.
         if len(self.timesteps      ) > 1:
                __magic_name__			:		List[str]        =     randn_tensor(
                    model_output.shape       ,       dtype=model_output.dtype       ,       device=model_output.device       ,       generator=lowercase_      )
         else:
                __magic_name__			:		List[str]        =     torch.zeros_like(lowercase_      )
         __magic_name__			:		Union[str, Any]        =     noise * self.config.s_noise

         __magic_name__			:		List[Any]        =     sigma_next.clamp(min=lowercase_       ,       max=lowercase_      )

         # 3. Return noisy sample
         # tau = sigma_hat, eps = sigma_min
         __magic_name__			:		int        =     denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

         if not return_dict:
                return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=lowercase_      )



  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: Any       ,       snake_case					: List[Any]       ,       snake_case					: str       ,       )	->			torch.FloatTensor:


         '''simple docstring'''





         __magic_name__			:		List[str]        =     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
                __magic_name__			:		List[Any]        =     self.timesteps.to(original_samples.device       ,       dtype=torch.floataa      )
                __magic_name__			:		Tuple        =     timesteps.to(original_samples.device       ,       dtype=torch.floataa      )
         else:
                __magic_name__			:		Any        =     self.timesteps.to(original_samples.device      )
                __magic_name__			:		Dict        =     timesteps.to(original_samples.device      )

         __magic_name__			:		Any        =     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

         __magic_name__			:		int        =     original_samples + noise * sigma
         return noisy_samples




  def __len__( self					: str      )	->			Union[str, Any]:


         '''simple docstring'''





         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()

