'''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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      List[str]=0.9_9_9			,	_UpperCAmelCase  :      Tuple="cosine"			,	)    ->    int:


       """simple docstring"""







       if alpha_transform_type == "cosine":

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

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

       _UpperCAmelCase     :  str							      =    []
       for i in range(_UpperCAmelCase    ):
              _UpperCAmelCase     :  int							      =    i / num_diffusion_timesteps
              _UpperCAmelCase     :  Union[str, 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 	lowerCamelCase_	(snake_case__							, snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						[e.name for e in KarrasDiffusionSchedulers]
      __UpperCamelCase:  List[Any]						=						2




      @register_to_config
      def __init__(       self						:				str						,					A						:				int = 1000						,					A						:				float = 0.00_085						,					A						:				float = 0.012						,					A						:				str = "linear"						,					A						:				Optional[Union[np.ndarray, List[float]]] = None						,					A						:				str = "epsilon"						,					A						:				Optional[bool] = False						,					A						:				Optional[bool] = False						,					A						:				float = 1.0						,					A						:				str = "linspace"						,					A						:				int = 0						,					):
             if trained_betas is not None:
                    _UpperCAmelCase     :  Optional[Any]							      =    torch.tensor(A						,					dtype=torch.floataa       )
             elif beta_schedule == "linear":
                    _UpperCAmelCase     :  Dict							      =    torch.linspace(A						,					A						,					A						,					dtype=torch.floataa       )
             elif beta_schedule == "scaled_linear":
                    # this schedule is very specific to the latent diffusion model.
                    _UpperCAmelCase     :  int							      =    (
                        torch.linspace(beta_start**0.5						,					beta_end**0.5						,					A						,					dtype=torch.floataa       ) ** 2
                    )
             elif beta_schedule == "squaredcos_cap_v2":
                    # Glide cosine schedule
                    _UpperCAmelCase     :  Optional[Any]							      =    betas_for_alpha_bar(A						,					alpha_transform_type="cosine"       )
             elif beta_schedule == "exp":
                    _UpperCAmelCase     :  List[str]							      =    betas_for_alpha_bar(A						,					alpha_transform_type="exp"       )
             else:
                    raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""       )

             _UpperCAmelCase     :  Optional[int]							      =    1.0 - self.betas
             _UpperCAmelCase     :  Tuple							      =    torch.cumprod(self.alphas						,					dim=0       )

             #  set all values
             self.set_timesteps(A						,					A						,					A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    use_karras_sigmas




      def      _A	(       self						:				Tuple						,					A						:				List[Any]						,					A						:				str=None       ):
             if schedule_timesteps is None:
                    _UpperCAmelCase     :  Dict							      =    self.timesteps

             _UpperCAmelCase     :  Union[str, Any]							      =    (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:
                    _UpperCAmelCase     :  Optional[int]							      =    1 if len(A       ) > 1 else 0
             else:
                    _UpperCAmelCase     :  List[str]							      =    timestep.cpu().item() if torch.is_tensor(A       ) else timestep
                    _UpperCAmelCase     :  Any							      =    self._index_counter[timestep_int]

             return indices[pos].item()




      @property
      def      _A	(       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      _A	(       self						:				Optional[Any]						,					A						:				torch.FloatTensor						,					A						:				Union[float, torch.FloatTensor]						,					):
             _UpperCAmelCase     :  List[str]							      =    self.index_for_timestep(A       )

             _UpperCAmelCase     :  int							      =    self.sigmas[step_index]
             _UpperCAmelCase     :  Union[str, Any]							      =    sample / ((sigma**2 + 1) ** 0.5)
             return sample




      def      _A	(       self						:				Optional[Any]						,					A						:				int						,					A						:				Union[str, torch.device] = None						,					A						:				Optional[int] = None						,					):
             _UpperCAmelCase     :  str							      =    num_inference_steps

             _UpperCAmelCase     :  Union[str, Any]							      =    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":
                    _UpperCAmelCase     :  List[str]							      =    np.linspace(0						,					num_train_timesteps - 1						,					A						,					dtype=A       )[::-1].copy()
             elif self.config.timestep_spacing == "leading":
                    _UpperCAmelCase     :  Union[str, 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
                    _UpperCAmelCase     :  Tuple							      =    (np.arange(0						,					A       ) * step_ratio).round()[::-1].copy().astype(A       )
                    timesteps += self.config.steps_offset
             elif self.config.timestep_spacing == "trailing":
                    _UpperCAmelCase     :  Tuple							      =    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
                    _UpperCAmelCase     :  Optional[Any]							      =    (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'."""       )

             _UpperCAmelCase     :  Dict							      =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
             _UpperCAmelCase     :  List[Any]							      =    np.log(A       )
             _UpperCAmelCase     :  int							      =    np.interp(A						,					np.arange(0						,					len(A       )       )						,					A       )

             if self.config.use_karras_sigmas:
                    _UpperCAmelCase     :  List[str]							      =    self._convert_to_karras(in_sigmas=A						,					num_inference_steps=self.num_inference_steps       )
                    _UpperCAmelCase     :  str							      =    np.array([self._sigma_to_t(A						,					A       ) for sigma in sigmas]       )

             _UpperCAmelCase     :  Optional[Any]							      =    np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
             _UpperCAmelCase     :  List[str]							      =    torch.from_numpy(A       ).to(device=A       )
             _UpperCAmelCase     :  Tuple							      =    torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2       ), sigmas[-1:]]       )

             _UpperCAmelCase     :  Optional[Any]							      =    torch.from_numpy(A       )
             _UpperCAmelCase     :  Optional[Any]							      =    torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2       )]       )

             if str(A       ).startswith("mps"       ):
                    # mps does not support float64
                    _UpperCAmelCase     :  Union[str, Any]							      =    timesteps.to(A						,					dtype=torch.floataa       )
             else:
                    _UpperCAmelCase     :  List[str]							      =    timesteps.to(device=A       )

             # empty dt and derivative
             _UpperCAmelCase     :  Optional[int]							      =    None
             _UpperCAmelCase     :  Optional[Any]							      =    None

             # for exp beta schedules, such as the one for `pipeline_shap_e.py`
             # we need an index counter
             _UpperCAmelCase     :  int							      =    defaultdict(A       )




      def      _A	(       self						:				Tuple						,					A						:				Optional[int]						,					A						:				Tuple       ):
             # get log sigma
             _UpperCAmelCase     :  Dict							      =    np.log(A       )

             # get distribution
             _UpperCAmelCase     :  Union[str, Any]							      =    log_sigma - log_sigmas[:, np.newaxis]

             # get sigmas range
             _UpperCAmelCase     :  Union[str, Any]							      =    np.cumsum((dists >= 0)						,					axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
             _UpperCAmelCase     :  List[Any]							      =    low_idx + 1

             _UpperCAmelCase     :  List[Any]							      =    log_sigmas[low_idx]
             _UpperCAmelCase     :  Optional[Any]							      =    log_sigmas[high_idx]

             # interpolate sigmas
             _UpperCAmelCase     :  Optional[Any]							      =    (low - log_sigma) / (low - high)
             _UpperCAmelCase     :  List[str]							      =    np.clip(A						,					0						,					1       )

             # transform interpolation to time range
             _UpperCAmelCase     :  Tuple							      =    (1 - w) * low_idx + w * high_idx
             _UpperCAmelCase     :  Optional[Any]							      =    t.reshape(sigma.shape       )
             return t




      def      _A	(       self						:				Tuple						,					A						:				torch.FloatTensor						,					A						:				List[str]       ):
             _UpperCAmelCase     :  float							      =    in_sigmas[-1].item()
             _UpperCAmelCase     :  float							      =    in_sigmas[0].item()

             _UpperCAmelCase     :  Union[str, Any]							      =    7.0  # 7.0 is the value used in the paper
             _UpperCAmelCase     :  List[Any]							      =    np.linspace(0						,					1						,					A       )
             _UpperCAmelCase     :  List[Any]							      =    sigma_min ** (1 / rho)
             _UpperCAmelCase     :  Tuple							      =    sigma_max ** (1 / rho)
             _UpperCAmelCase     :  List[Any]							      =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
             return sigmas




      @property
      def      _A	(       self						:				List[Any]       ):
             return self.dt is None




      def      _A	(       self						:				Tuple						,					A						:				Union[torch.FloatTensor, np.ndarray]						,					A						:				Union[float, torch.FloatTensor]						,					A						:				Union[torch.FloatTensor, np.ndarray]						,					A						:				bool = True						,					):
             _UpperCAmelCase     :  Dict							      =    self.index_for_timestep(A       )

             # advance index counter by 1
             _UpperCAmelCase     :  int							      =    timestep.cpu().item() if torch.is_tensor(A       ) else timestep
             self._index_counter[timestep_int] += 1

             if self.state_in_first_order:
                    _UpperCAmelCase     :  Any							      =    self.sigmas[step_index]
                    _UpperCAmelCase     :  Any							      =    self.sigmas[step_index + 1]
             else:
                    # 2nd order / Heun's method
                    _UpperCAmelCase     :  Optional[int]							      =    self.sigmas[step_index - 1]
                    _UpperCAmelCase     :  List[str]							      =    self.sigmas[step_index]

             # 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
             _UpperCAmelCase     :  Union[str, Any]							      =    0
             _UpperCAmelCase     :  str							      =    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                    _UpperCAmelCase     :  str							      =    sigma_hat if self.state_in_first_order else sigma_next
                    _UpperCAmelCase     :  Tuple							      =    sample - sigma_input * model_output
             elif self.config.prediction_type == "v_prediction":
                    _UpperCAmelCase     :  int							      =    sigma_hat if self.state_in_first_order else sigma_next
                    _UpperCAmelCase     :  str							      =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                        sample / (sigma_input**2 + 1)
                    )
             elif self.config.prediction_type == "sample":
                    _UpperCAmelCase     :  Tuple							      =    model_output
             else:
                    raise ValueError(
                        F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""       )

             if self.config.clip_sample:
                    _UpperCAmelCase     :  Optional[int]							      =    pred_original_sample.clamp(
                        -self.config.clip_sample_range						,					self.config.clip_sample_range       )

             if self.state_in_first_order:
                    # 2. Convert to an ODE derivative for 1st order
                    _UpperCAmelCase     :  Optional[Any]							      =    (sample - pred_original_sample) / sigma_hat
                    # 3. delta timestep
                    _UpperCAmelCase     :  List[str]							      =    sigma_next - sigma_hat

                    # store for 2nd order step
                    _UpperCAmelCase     :  Optional[int]							      =    derivative
                    _UpperCAmelCase     :  List[str]							      =    dt
                    _UpperCAmelCase     :  int							      =    sample
             else:
                    # 2. 2nd order / Heun's method
                    _UpperCAmelCase     :  str							      =    (sample - pred_original_sample) / sigma_next
                    _UpperCAmelCase     :  Optional[int]							      =    (self.prev_derivative + derivative) / 2

                    # 3. take prev timestep & sample
                    _UpperCAmelCase     :  Union[str, Any]							      =    self.dt
                    _UpperCAmelCase     :  Optional[Any]							      =    self.sample

                    # free dt and derivative
                    # Note, this puts the scheduler in "first order mode"
                    _UpperCAmelCase     :  List[str]							      =    None
                    _UpperCAmelCase     :  str							      =    None
                    _UpperCAmelCase     :  List[str]							      =    None

             _UpperCAmelCase     :  Any							      =    sample + derivative * dt

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=A       )




      def      _A	(       self						:				Optional[Any]						,					A						:				torch.FloatTensor						,					A						:				torch.FloatTensor						,					A						:				torch.FloatTensor						,					):
             # Make sure sigmas and timesteps have the same device and dtype as original_samples
             _UpperCAmelCase     :  int							      =    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
                    _UpperCAmelCase     :  Tuple							      =    self.timesteps.to(original_samples.device						,					dtype=torch.floataa       )
                    _UpperCAmelCase     :  int							      =    timesteps.to(original_samples.device						,					dtype=torch.floataa       )
             else:
                    _UpperCAmelCase     :  Tuple							      =    self.timesteps.to(original_samples.device       )
                    _UpperCAmelCase     :  int							      =    timesteps.to(original_samples.device       )

             _UpperCAmelCase     :  Optional[Any]							      =    [self.index_for_timestep(A						,					A       ) for t in timesteps]

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

             _UpperCAmelCase     :  List[Any]							      =    original_samples + noise * sigma
             return noisy_samples





      def __len__(       self						:				List[str]       ):
             return self.config.num_train_timesteps



"""simple docstring"""





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		np.ndarray:


					"""simple docstring"""





					if (ksize % 2) == 0:
										_UpperCamelCase							 =  ksize + 1
					_UpperCamelCase							 =  np.zeros((ksize, ksize),     dtype=np.floataa	)

					# each value
					for y in range(__snake_case	):
										for x in range(__snake_case	):
															# distance from center
															_UpperCamelCase							 =  x - ksize // 2
															_UpperCamelCase							 =  y - ksize // 2

															# degree to radiant
															_UpperCamelCase							 =  theta / 1_80 * np.pi
															_UpperCamelCase							 =  np.cos(_theta	)
															_UpperCamelCase							 =  np.sin(_theta	)

															# get kernel x
															_UpperCamelCase							 =  cos_theta * px + sin_theta * py

															# get kernel y
															_UpperCamelCase							 =  -sin_theta * px + cos_theta * py

															# fill kernel
															_UpperCamelCase							 =  np.exp(
															    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)	) * np.cos(2 * np.pi * _x / lambd + psi	)

					return gabor


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# read original image
				_a														=		imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a														=		cvtColor(img, COLOR_BGR2GRAY)

				# Apply multiple Kernel to detect edges
				_a														=		np.zeros(gray.shape[:2])
				for theta in [0, 30, 60, 90, 120, 150]:
								_a														=		gabor_filter_kernel(10, 8, theta, 10, 0, 0)
								out += filteraD(gray, CV_8UC3, kernel_aa)
				_a														=		out / out.max() * 255
				_a														=		out.astype(np.uinta)

				imshow("""Original""", gray)
				imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

				waitKey(0)






