





"""simple docstring"""


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 SCREAMING_SNAKE_CASE_   (       snake_case   :					Any						,  snake_case   :					str=0.9_9_9						,  snake_case   :					Any="cosine"						,  )->		Union[str, Any]:
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(snake_case   :					str		):
                     return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(snake_case   :					Optional[Any]		):
                     return math.exp(t * -1_2.0		)

       else:
              raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'		)

       _lowerCamelCase    	=					[]
       for i in range(snake_case		):
              _lowerCamelCase    	=					i / num_diffusion_timesteps
              _lowerCamelCase    	=					(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(snake_case		) / alpha_bar_fn(snake_case		)						,  snake_case		)		)
       return torch.tensor(snake_case						,  dtype=torch.floataa		)







class 	__a   (  lowerCAmelCase__	,						lowerCAmelCase__		):
     SCREAMING_SNAKE_CASE__ :      List[Any]       		=			[e.name for e in KarrasDiffusionSchedulers]
     SCREAMING_SNAKE_CASE__ :      Tuple       		=			2



     @register_to_config
     def __init__(       self					,     a__ = 10_00					,     a__ = 0.00085					,     a__ = 0.012					,     a__ = "linear"					,     a__ = None					,     a__ = "epsilon"					,     a__ = "linspace"					,     a__ = 0					,     ):
            if trained_betas is not None:
                   _lowerCamelCase    	=					torch.tensor(a__					,     dtype=torch.floataa   )
            elif beta_schedule == "linear":
                   _lowerCamelCase    	=					torch.linspace(a__					,     a__					,     a__					,     dtype=torch.floataa   )
            elif beta_schedule == "scaled_linear":
                   # this schedule is very specific to the latent diffusion model.
                   _lowerCamelCase    	=					(
                       torch.linspace(beta_start**0.5					,     beta_end**0.5					,     a__					,     dtype=torch.floataa   ) ** 2
                   )
            elif beta_schedule == "squaredcos_cap_v2":
                   # Glide cosine schedule
                   _lowerCamelCase    	=					betas_for_alpha_bar(a__   )
            else:
                   raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'   )

            _lowerCamelCase    	=					1.0 - self.betas
            _lowerCamelCase    	=					torch.cumprod(self.alphas					,     dim=0   )

            #  set all values
            self.set_timesteps(a__					,     a__					,     a__   )



     def 						snake_case_			(       self					,     a__					,     a__=None   ):
            if schedule_timesteps is None:
                   _lowerCamelCase    	=					self.timesteps

            _lowerCamelCase    	=					(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:
                   _lowerCamelCase    	=					1 if len(a__   ) > 1 else 0
            else:
                   _lowerCamelCase    	=					timestep.cpu().item() if torch.is_tensor(a__   ) else timestep
                   _lowerCamelCase    	=					self._index_counter[timestep_int]

            return indices[pos].item()



     @property
     def 						snake_case_			(       self   ):
            # 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					,     a__					,     a__					,     ):
            _lowerCamelCase    	=					self.index_for_timestep(a__   )

            if self.state_in_first_order:
                   _lowerCamelCase    	=					self.sigmas[step_index]
            else:
                   _lowerCamelCase    	=					self.sigmas_interpol[step_index - 1]

            _lowerCamelCase    	=					sample / ((sigma**2 + 1) ** 0.5)
            return sample



     def 						snake_case_			(       self					,     a__					,     a__ = None					,     a__ = None					,     ):
            _lowerCamelCase    	=					num_inference_steps

            _lowerCamelCase    	=					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":
                   _lowerCamelCase    	=					np.linspace(0					,     num_train_timesteps - 1					,     a__					,     dtype=a__   )[::-1].copy()
            elif self.config.timestep_spacing == "leading":
                   _lowerCamelCase    	=					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    	=					(np.arange(0					,     a__   ) * step_ratio).round()[::-1].copy().astype(a__   )
                   timesteps += self.config.steps_offset
            elif self.config.timestep_spacing == "trailing":
                   _lowerCamelCase    	=					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    	=					(np.arange(a__					,     0					,     -step_ratio   )).round().copy().astype(a__   )
                   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    	=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
            _lowerCamelCase    	=					torch.from_numpy(np.log(a__   )   ).to(a__   )

            _lowerCamelCase    	=					np.interp(a__					,     np.arange(0					,     len(a__   )   )					,     a__   )
            _lowerCamelCase    	=					np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
            _lowerCamelCase    	=					torch.from_numpy(a__   ).to(device=a__   )

            # compute up and down sigmas
            _lowerCamelCase    	=					sigmas.roll(-1   )
            _lowerCamelCase    	=					0.0
            _lowerCamelCase    	=					(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
            _lowerCamelCase    	=					(sigmas_next**2 - sigmas_up**2) ** 0.5
            _lowerCamelCase    	=					0.0

            # compute interpolated sigmas
            _lowerCamelCase    	=					sigmas.log().lerp(sigmas_down.log()					,     0.5   ).exp()
            _lowerCamelCase    	=					0.0

            # set sigmas
            _lowerCamelCase    	=					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
            _lowerCamelCase    	=					torch.cat(
                [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )
            _lowerCamelCase    	=					torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2   ), sigmas_up[-1:]]   )
            _lowerCamelCase    	=					torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2   ), sigmas_down[-1:]]   )

            if str(a__   ).startswith('mps'   ):
                   # mps does not support float64
                   _lowerCamelCase    	=					torch.from_numpy(a__   ).to(a__					,     dtype=torch.floataa   )
            else:
                   _lowerCamelCase    	=					torch.from_numpy(a__   ).to(a__   )

            _lowerCamelCase    	=					self.sigma_to_t(a__   ).to(a__					,     dtype=timesteps.dtype   )
            _lowerCamelCase    	=					torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])					,     dim=-1   ).flatten()

            _lowerCamelCase    	=					torch.cat([timesteps[:1], interleaved_timesteps]   )

            _lowerCamelCase    	=					None

            # for exp beta schedules, such as the one for `pipeline_shap_e.py`
            # we need an index counter
            _lowerCamelCase    	=					defaultdict(a__   )



     def 						snake_case_			(       self					,     a__   ):
            # get log sigma
            _lowerCamelCase    	=					sigma.log()

            # get distribution
            _lowerCamelCase    	=					log_sigma - self.log_sigmas[:, None]

            # get sigmas range
            _lowerCamelCase    	=					dists.ge(0   ).cumsum(dim=0   ).argmax(dim=0   ).clamp(max=self.log_sigmas.shape[0] - 2   )
            _lowerCamelCase    	=					low_idx + 1

            _lowerCamelCase    	=					self.log_sigmas[low_idx]
            _lowerCamelCase    	=					self.log_sigmas[high_idx]

            # interpolate sigmas
            _lowerCamelCase    	=					(low - log_sigma) / (low - high)
            _lowerCamelCase    	=					w.clamp(0					,     1   )

            # transform interpolation to time range
            _lowerCamelCase    	=					(1 - w) * low_idx + w * high_idx
            _lowerCamelCase    	=					t.view(sigma.shape   )
            return t



     @property
     def 						snake_case_			(       self   ):
            return self.sample is None



     def 						snake_case_			(       self					,     a__					,     a__					,     a__					,     a__ = None					,     a__ = True					,     ):
            _lowerCamelCase    	=					self.index_for_timestep(a__   )

            # advance index counter by 1
            _lowerCamelCase    	=					timestep.cpu().item() if torch.is_tensor(a__   ) else timestep
            self._index_counter[timestep_int] += 1

            if self.state_in_first_order:
                   _lowerCamelCase    	=					self.sigmas[step_index]
                   _lowerCamelCase    	=					self.sigmas_interpol[step_index]
                   _lowerCamelCase    	=					self.sigmas_up[step_index]
                   _lowerCamelCase    	=					self.sigmas_down[step_index - 1]
            else:
                   # 2nd order / KPDM2's method
                   _lowerCamelCase    	=					self.sigmas[step_index - 1]
                   _lowerCamelCase    	=					self.sigmas_interpol[step_index - 1]
                   _lowerCamelCase    	=					self.sigmas_up[step_index - 1]
                   _lowerCamelCase    	=					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
            _lowerCamelCase    	=					0
            _lowerCamelCase    	=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

            _lowerCamelCase    	=					model_output.device
            _lowerCamelCase    	=					randn_tensor(model_output.shape					,     dtype=model_output.dtype					,     device=a__					,     generator=a__   )

            # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
            if self.config.prediction_type == "epsilon":
                   _lowerCamelCase    	=					sigma_hat if self.state_in_first_order else sigma_interpol
                   _lowerCamelCase    	=					sample - sigma_input * model_output
            elif self.config.prediction_type == "v_prediction":
                   _lowerCamelCase    	=					sigma_hat if self.state_in_first_order else sigma_interpol
                   _lowerCamelCase    	=					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
                   _lowerCamelCase    	=					(sample - pred_original_sample) / sigma_hat
                   # 3. delta timestep
                   _lowerCamelCase    	=					sigma_interpol - sigma_hat

                   # store for 2nd order step
                   _lowerCamelCase    	=					sample
                   _lowerCamelCase    	=					dt
                   _lowerCamelCase    	=					sample + derivative * dt
            else:
                   # DPM-Solver-2
                   # 2. Convert to an ODE derivative for 2nd order
                   _lowerCamelCase    	=					(sample - pred_original_sample) / sigma_interpol
                   # 3. delta timestep
                   _lowerCamelCase    	=					sigma_down - sigma_hat

                   _lowerCamelCase    	=					self.sample
                   _lowerCamelCase    	=					None

                   _lowerCamelCase    	=					sample + derivative * dt
                   _lowerCamelCase    	=					prev_sample + noise * sigma_up

            if not return_dict:
                   return (prev_sample,)

            return SchedulerOutput(prev_sample=a__   )



     def 						snake_case_			(       self					,     a__					,     a__					,     a__					,     ):
            # Make sure sigmas and timesteps have the same device and dtype as original_samples
            _lowerCamelCase    	=					self.sigmas.to(device=original_samples.device					,     dtype=original_samples.dtype   )
            if original_samples.device.type == "mps" and torch.is_floating_point(a__   ):
                   # mps does not support float64
                   _lowerCamelCase    	=					self.timesteps.to(original_samples.device					,     dtype=torch.floataa   )
                   _lowerCamelCase    	=					timesteps.to(original_samples.device					,     dtype=torch.floataa   )
            else:
                   _lowerCamelCase    	=					self.timesteps.to(original_samples.device   )
                   _lowerCamelCase    	=					timesteps.to(original_samples.device   )

            _lowerCamelCase    	=					[self.index_for_timestep(a__					,     a__   ) for t in timesteps]

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

            _lowerCamelCase    	=					original_samples + noise * sigma
            return noisy_samples



     def __len__(       self   ):
            return self.config.num_train_timesteps













"""simple docstring"""


import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

A_     :      Any								=1_8_0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Any		)->		Tuple:
       _lowerCamelCase    	=					k_size // 2
       _lowerCamelCase	,	_lowerCamelCase    	=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _lowerCamelCase    	=					(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(snake_case		) + np.square(snake_case		)) / (2 * np.square(snake_case		))		)
       )
       return g





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[int]						,  snake_case   :					int						,  snake_case   :					Dict		)->		Optional[Any]:
       _lowerCamelCase    	=					np.zeros(snake_case		)

       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     _lowerCamelCase    	=					gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row + 1, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row - 1, col]
                            _lowerCamelCase    	=					sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row - 1, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

       return destination





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Any]						,  snake_case   :					List[Any]						,  snake_case   :					int						,  snake_case   :					int						,  snake_case   :					List[str]						,  snake_case   :					str		)->		Tuple:
       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     if destination[row, col] >= threshold_high:
                            _lowerCamelCase    	=					strong
                     elif destination[row, col] <= threshold_low:
                            _lowerCamelCase    	=					0
                     else:
                            _lowerCamelCase    	=					weak





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Union[str, Any]						,  snake_case   :					Optional[int]						,  snake_case   :					Dict						,  snake_case   :					Tuple		)->		Optional[int]:
       for row in range(1						,  image_shape[0]		):
              for col in range(1						,  image_shape[1]		):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   _lowerCamelCase    	=					strong
                            else:
                                   _lowerCamelCase    	=					0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Union[str, Any]=15						,  snake_case   :					Optional[int]=30						,  snake_case   :					int=128						,  snake_case   :					str=255		)->		List[Any]:
       # gaussian_filter
       _lowerCamelCase    	=					img_convolve(snake_case						,  gen_gaussian_kernel(9						,  sigma=1.4		)		)
       # get the gradient and degree by sobel_filter
       _lowerCamelCase	,	_lowerCamelCase    	=					sobel_filter(snake_case		)
       _lowerCamelCase    	=					PI + np.radadeg(snake_case		)

       _lowerCamelCase    	=					suppress_non_maximum(image.shape						,  snake_case						,  snake_case		)

       detect_high_low_threshold(
           image.shape						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case		)

       track_edge(image.shape						,  snake_case						,  snake_case						,  snake_case		)

       return destination


if __name__ == "__main__":
      # read original image in gray mode
      A_     :      Optional[Any]								=cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      A_     :      Optional[Any]								=canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)






