code: 
  
  
  
  
  
  import math
  from collections import defaultdict
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import randn_tensor
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_=0.9_99      ,		lowercase_="cosine"      ,		)			->						Dict:
         if alpha_transform_type == "cosine":
  
                def alpha_bar_fn(lowercase_  ):
                       return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2  ) ** 2
  
         elif alpha_transform_type == "exp":
  
                def alpha_bar_fn(lowercase_  ):
                       return math.exp(t * -12.0  )
  
         else:
                raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''  )
  
         _snake_case      :							Union[str, Any]     				=						[]
         for i in range(UpperCamelCase__  ):
                _snake_case      :							str     				=						i / num_diffusion_timesteps
                _snake_case      :							Optional[Any]     				=						(i + 1) / num_diffusion_timesteps
                betas.append(min(1 - alpha_bar_fn(UpperCamelCase__  ) / alpha_bar_fn(UpperCamelCase__  )      ,		UpperCamelCase__  )  )
         return torch.tensor(UpperCamelCase__      ,		dtype=torch.floataa  )
  
  class 				A    (__UpperCAmelCase       ,__UpperCAmelCase				):
        _SCREAMING_SNAKE_CASE					=							[e.name for e in KarrasDiffusionSchedulers]
        _SCREAMING_SNAKE_CASE					=							2
  
  
  
        @register_to_config
        def __init__(					self     ,  lowercase_ = 1000     ,  lowercase_ = 0.0_0085     ,  lowercase_ = 0.012     ,  lowercase_ = "linear"     ,  lowercase_ = None     ,  lowercase_ = "epsilon"     ,  lowercase_ = "linspace"     ,  lowercase_ = 0     ,  )      ->   int:
  
  
  
               '''simple docstring'''
  
               if trained_betas is not None:
                      _snake_case      :							str     				=						torch.tensor(__lowerCamelCase     ,  dtype=torch.floataa					)
               elif beta_schedule == "linear":
                      _snake_case      :							Optional[Any]     				=						torch.linspace(__lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase     ,  dtype=torch.floataa					)
               elif beta_schedule == "scaled_linear":
                      # this schedule is very specific to the latent diffusion model.
                      _snake_case      :							str     				=						(
                          torch.linspace(beta_start**0.5     ,  beta_end**0.5     ,  __lowerCamelCase     ,  dtype=torch.floataa					) ** 2
                      )
               elif beta_schedule == "squaredcos_cap_v2":
                      # Glide cosine schedule
                      _snake_case      :							int     				=						betas_for_alpha_bar(__lowerCamelCase					)
               else:
                      raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)
  
               _snake_case      :							str     				=						1.0 - self.betas
               _snake_case      :							Union[str, Any]     				=						torch.cumprod(self.alphas     ,  dim=0					)
  
               #  set all values
               self.set_timesteps(__lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_=None					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               if schedule_timesteps is None:
                      _snake_case      :							str     				=						self.timesteps
  
               _snake_case      :							Tuple     				=						(schedule_timesteps == timestep).nonzero()
  
               # The sigma index that is taken for the **very** first `step`
               # is always the second index (or the last index if there is only 1)
               # This way we can ensure we don't accidentally skip a sigma in
               # case we start in the middle of the denoising schedule (e.g. for image-to-image)
               if len(self._index_counter					) == 0:
                      _snake_case      :							Any     				=						1 if len(__lowerCamelCase					) > 1 else 0
               else:
                      _snake_case      :							int     				=						timestep.cpu().item() if torch.is_tensor(__lowerCamelCase					) else timestep
                      _snake_case      :							int     				=						self._index_counter[timestep_int]
  
               return indices[pos].item()
  
  
  
        @property
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               if self.config.timestep_spacing in ["linspace", "trailing"]:
                      return self.sigmas.max()
  
               return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  )      ->   torch.FloatTensor:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						self.index_for_timestep(__lowerCamelCase					)
  
               if self.state_in_first_order:
                      _snake_case      :							Union[str, Any]     				=						self.sigmas[step_index]
               else:
                      _snake_case      :							Union[str, Any]     				=						self.sigmas_interpol[step_index - 1]
  
               _snake_case      :							Dict     				=						sample / ((sigma**2 + 1) ** 0.5)
               return sample
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_ = None     ,  lowercase_ = None     ,  )      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						num_inference_steps
  
               _snake_case      :							List[str]     				=						num_train_timesteps or self.config.num_train_timesteps
  
               # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
               if self.config.timestep_spacing == "linspace":
                      _snake_case      :							Any     				=						np.linspace(0     ,  num_train_timesteps - 1     ,  __lowerCamelCase     ,  dtype=__lowerCamelCase					)[::-1].copy()
               elif self.config.timestep_spacing == "leading":
                      _snake_case      :							List[str]     				=						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
                      _snake_case      :							Optional[int]     				=						(np.arange(0     ,  __lowerCamelCase					) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase					)
                      timesteps += self.config.steps_offset
               elif self.config.timestep_spacing == "trailing":
                      _snake_case      :							Any     				=						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
                      _snake_case      :							Optional[int]     				=						(np.arange(__lowerCamelCase     ,  0     ,  -step_ratio					)).round().copy().astype(__lowerCamelCase					)
                      timesteps -= 1
               else:
                      raise ValueError(
                          F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)
  
               _snake_case      :							List[Any]     				=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
               _snake_case      :							Union[str, Any]     				=						torch.from_numpy(np.log(__lowerCamelCase					)					).to(__lowerCamelCase					)
  
               _snake_case      :							List[Any]     				=						np.interp(__lowerCamelCase     ,  np.arange(0     ,  len(__lowerCamelCase					)					)     ,  __lowerCamelCase					)
               _snake_case      :							Union[str, Any]     				=						np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
               _snake_case      :							List[Any]     				=						torch.from_numpy(__lowerCamelCase					).to(device=__lowerCamelCase					)
  
               # compute up and down sigmas
               _snake_case      :							Union[str, Any]     				=						sigmas.roll(-1					)
               _snake_case      :							List[str]     				=						0.0
               _snake_case      :							List[str]     				=						(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
               _snake_case      :							Optional[int]     				=						(sigmas_next**2 - sigmas_up**2) ** 0.5
               _snake_case      :							Tuple     				=						0.0
  
               # compute interpolated sigmas
               _snake_case      :							str     				=						sigmas.log().lerp(sigmas_down.log()     ,  0.5					).exp()
               _snake_case      :							List[Any]     				=						0.0
  
               # set sigmas
               _snake_case      :							str     				=						torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
               _snake_case      :							List[Any]     				=						torch.cat(
                   [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)
               _snake_case      :							Tuple     				=						torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
               _snake_case      :							Any     				=						torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)
  
               if str(__lowerCamelCase					).startswith('''mps'''					):
                      # mps does not support float64
                      _snake_case      :							Optional[Any]     				=						torch.from_numpy(__lowerCamelCase					).to(__lowerCamelCase     ,  dtype=torch.floataa					)
               else:
                      _snake_case      :							Dict     				=						torch.from_numpy(__lowerCamelCase					).to(__lowerCamelCase					)
  
               _snake_case      :							Union[str, Any]     				=						self.sigma_to_t(__lowerCamelCase					).to(__lowerCamelCase     ,  dtype=timesteps.dtype					)
               _snake_case      :							Optional[int]     				=						torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])     ,  dim=-1					).flatten()
  
               _snake_case      :							Optional[Any]     				=						torch.cat([timesteps[:1], interleaved_timesteps]					)
  
               _snake_case      :							Optional[Any]     				=						None
  
               # for exp beta schedules, such as the one for `pipeline_shap_e.py`
               # we need an index counter
               _snake_case      :							Optional[Any]     				=						defaultdict(__lowerCamelCase					)
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						sigma.log()
  
               # get distribution
               _snake_case      :							Optional[Any]     				=						log_sigma - self.log_sigmas[:, None]
  
               # get sigmas range
               _snake_case      :							Optional[Any]     				=						dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
               _snake_case      :							List[str]     				=						low_idx + 1
  
               _snake_case      :							int     				=						self.log_sigmas[low_idx]
               _snake_case      :							Any     				=						self.log_sigmas[high_idx]
  
               # interpolate sigmas
               _snake_case      :							Optional[int]     				=						(low - log_sigma) / (low - high)
               _snake_case      :							int     				=						w.clamp(0     ,  1					)
  
               # transform interpolation to time range
               _snake_case      :							List[Any]     				=						(1 - w) * low_idx + w * high_idx
               _snake_case      :							Optional[int]     				=						t.view(sigma.shape					)
               return t
  
  
  
        @property
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               return self.sample is None
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_ = None     ,  lowercase_ = True     ,  )      ->   Union[SchedulerOutput, Tuple]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						self.index_for_timestep(__lowerCamelCase					)
  
               # advance index counter by 1
               _snake_case      :							Optional[int]     				=						timestep.cpu().item() if torch.is_tensor(__lowerCamelCase					) else timestep
               self._index_counter[timestep_int] += 1
  
               if self.state_in_first_order:
                      _snake_case      :							int     				=						self.sigmas[step_index]
                      _snake_case      :							Optional[int]     				=						self.sigmas_interpol[step_index]
                      _snake_case      :							List[str]     				=						self.sigmas_up[step_index]
                      _snake_case      :							str     				=						self.sigmas_down[step_index - 1]
               else:
                      # 2nd order / KPDM2's method
                      _snake_case      :							Union[str, Any]     				=						self.sigmas[step_index - 1]
                      _snake_case      :							Dict     				=						self.sigmas_interpol[step_index - 1]
                      _snake_case      :							str     				=						self.sigmas_up[step_index - 1]
                      _snake_case      :							Optional[Any]     				=						self.sigmas_down[step_index - 1]
  
               # currently only gamma=0 is supported. This usually works best anyways.
               # We can support gamma in the future but then need to scale the timestep before
               # passing it to the model which requires a change in API
               _snake_case      :							Dict     				=						0
               _snake_case      :							int     				=						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now
  
               _snake_case      :							List[str]     				=						model_output.device
               _snake_case      :							int     				=						randn_tensor(model_output.shape     ,  dtype=model_output.dtype     ,  device=__lowerCamelCase     ,  generator=__lowerCamelCase					)
  
               # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
               if self.config.prediction_type == "epsilon":
                      _snake_case      :							List[str]     				=						sigma_hat if self.state_in_first_order else sigma_interpol
                      _snake_case      :							Optional[Any]     				=						sample - sigma_input * model_output
               elif self.config.prediction_type == "v_prediction":
                      _snake_case      :							Any     				=						sigma_hat if self.state_in_first_order else sigma_interpol
                      _snake_case      :							List[Any]     				=						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                          sample / (sigma_input**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`'''					)
  
               if self.state_in_first_order:
                      # 2. Convert to an ODE derivative for 1st order
                      _snake_case      :							Union[str, Any]     				=						(sample - pred_original_sample) / sigma_hat
                      # 3. delta timestep
                      _snake_case      :							List[Any]     				=						sigma_interpol - sigma_hat
  
                      # store for 2nd order step
                      _snake_case      :							str     				=						sample
                      _snake_case      :							Optional[int]     				=						dt
                      _snake_case      :							Optional[int]     				=						sample + derivative * dt
               else:
                      # DPM-Solver-2
                      # 2. Convert to an ODE derivative for 2nd order
                      _snake_case      :							Union[str, Any]     				=						(sample - pred_original_sample) / sigma_interpol
                      # 3. delta timestep
                      _snake_case      :							str     				=						sigma_down - sigma_hat
  
                      _snake_case      :							Optional[Any]     				=						self.sample
                      _snake_case      :							Tuple     				=						None
  
                      _snake_case      :							Optional[Any]     				=						sample + derivative * dt
                      _snake_case      :							Optional[int]     				=						prev_sample + noise * sigma_up
  
               if not return_dict:
                      return (prev_sample,)
  
               return SchedulerOutput(prev_sample=__lowerCamelCase					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   torch.FloatTensor:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.sigmas.to(device=original_samples.device     ,  dtype=original_samples.dtype					)
               if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase					):
                      # mps does not support float64
                      _snake_case      :							Optional[Any]     				=						self.timesteps.to(original_samples.device     ,  dtype=torch.floataa					)
                      _snake_case      :							Tuple     				=						timesteps.to(original_samples.device     ,  dtype=torch.floataa					)
               else:
                      _snake_case      :							Optional[Any]     				=						self.timesteps.to(original_samples.device					)
                      _snake_case      :							List[Any]     				=						timesteps.to(original_samples.device					)
  
               _snake_case      :							List[Any]     				=						[self.index_for_timestep(__lowerCamelCase     ,  __lowerCamelCase					) for t in timesteps]
  
               _snake_case      :							Any     				=						sigmas[step_indices].flatten()
               while len(sigma.shape					) < len(original_samples.shape					):
                      _snake_case      :							Optional[int]     				=						sigma.unsqueeze(-1					)
  
               _snake_case      :							List[str]     				=						original_samples + noise * sigma
               return noisy_samples
  
  
  
        def __len__(					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               return self.config.num_train_timesteps
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import cva
  import numpy as np
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
  
         try:
                return int(image[x_coordinate][y_coordinate] >= center  )
         except (IndexError, TypeError):
                return 0
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
         _snake_case      :							List[str]     				=						image[x_coordinate][y_coordinate]
         _snake_case      :							List[str]     				=						[1, 2, 4, 8, 16, 32, 64, 128]
  
         # skip get_neighbors_pixel if center is null
         if center is None:
                return 0
  
         # Starting from the top right, assigning value to pixels clockwise
         _snake_case      :							Any     				=						[
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate - 1      ,		lowercase_  ),
         ]
  
         # Converting the binary value to decimal.
         return sum(
             binary_value * power for binary_value, power in zip(lowercase_      ,		lowercase_  )  )
  
  
  if __name__ == "__main__":
     # Reading the image and converting it to grayscale.
     lowerCAmelCase_   =   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_   =   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_   =   local_binary_value(image, i, j)
  
     cva.imshow("local binary pattern", lbp_image)
     cva.waitKey(0)
     cva.destroyAllWindows()
  
  
  
  
  
  
  