
"""simple docstring"""




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



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a,							__a    ):

		# prepare kernel
		# the kernel size have to be odd
		if (ksize % 2) == 0:
				SCREAMING_SNAKE_CASE_						 = ksize + 1
		SCREAMING_SNAKE_CASE_						 = np.zeros((ksize, ksize),							dtype=np.floataa    )

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

						# degree to radiant
						SCREAMING_SNAKE_CASE_						 = theta / 180 * np.pi
						SCREAMING_SNAKE_CASE_						 = np.cos(_theta    )
						SCREAMING_SNAKE_CASE_						 = np.sin(_theta    )

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

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

						# fill kernel
						SCREAMING_SNAKE_CASE_						 = 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
						lowerCAmelCase__							=				imread('../image_data/lena.jpg')
						# turn image in gray scale value
						lowerCAmelCase__							=				cvtColor(img, COLOR_BGR2GRAY)

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

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

						waitKey(0)

"""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        _lowerCamelCase ( __a,							__a=0.9_9_9,							__a="cosine",							):
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(__a    ):
						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(__a    ):
						return math.exp(t * -1_2.0    )

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

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





class  snake_case     (							__lowercase       ,			__lowercase   ):
							UpperCAmelCase__						     =      [e.name for e in KarrasDiffusionSchedulers]
							UpperCAmelCase__						     =      2

							@register_to_config
							def __init__(self       ,       SCREAMING_SNAKE_CASE_ = 10_00       ,       SCREAMING_SNAKE_CASE_ = 0.0_00_85       ,       SCREAMING_SNAKE_CASE_ = 0.0_12       ,       SCREAMING_SNAKE_CASE_ = "linear"       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = "epsilon"       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = "linspace"       ,       SCREAMING_SNAKE_CASE_ = 0       ,       ):

									"""simple docstring"""







									if trained_betas is not None:
											SCREAMING_SNAKE_CASE_						 = torch.tensor(SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    )
									elif beta_schedule == "linear":
											SCREAMING_SNAKE_CASE_						 = torch.linspace(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    )
									elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											SCREAMING_SNAKE_CASE_						 = (
											    torch.linspace(beta_start**0.5       ,       beta_end**0.5       ,       SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    ) ** 2
											)
									elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											SCREAMING_SNAKE_CASE_						 = betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       ,       alpha_transform_type='''cosine'''    )
									elif beta_schedule == "exp":
											SCREAMING_SNAKE_CASE_						 = betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       ,       alpha_transform_type='''exp'''    )
									else:
											raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'    )

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

									#  set all values
									self.set_timesteps(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = use_karras_sigmas

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=None    ):

									"""simple docstring"""







									if schedule_timesteps is None:
											SCREAMING_SNAKE_CASE_						 = self.timesteps

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

									return indices[pos].item()

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.index_for_timestep(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index]
									SCREAMING_SNAKE_CASE_						 = sample / ((sigma**2 + 1) ** 0.5)
									return sample

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = num_inference_steps

									SCREAMING_SNAKE_CASE_						 = 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":
											SCREAMING_SNAKE_CASE_						 = np.linspace(0       ,       num_train_timesteps - 1       ,       SCREAMING_SNAKE_CASE_       ,       dtype=SCREAMING_SNAKE_CASE_    )[::-1].copy()
									elif self.config.timestep_spacing == "leading":
											SCREAMING_SNAKE_CASE_						 = 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
											SCREAMING_SNAKE_CASE_						 = (np.arange(0       ,       SCREAMING_SNAKE_CASE_    ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_    )
											timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
											SCREAMING_SNAKE_CASE_						 = 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
											SCREAMING_SNAKE_CASE_						 = (np.arange(SCREAMING_SNAKE_CASE_       ,       0       ,       -step_ratio    )).round().copy().astype(SCREAMING_SNAKE_CASE_    )
											timesteps -= 1
									else:
											raise ValueError(
											    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'    )

									SCREAMING_SNAKE_CASE_						 = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
									SCREAMING_SNAKE_CASE_						 = np.log(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.interp(SCREAMING_SNAKE_CASE_       ,       np.arange(0       ,       len(SCREAMING_SNAKE_CASE_    )    )       ,       SCREAMING_SNAKE_CASE_    )

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

									SCREAMING_SNAKE_CASE_						 = np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
									SCREAMING_SNAKE_CASE_						 = torch.from_numpy(SCREAMING_SNAKE_CASE_    ).to(device=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2    ), sigmas[-1:]]    )

									SCREAMING_SNAKE_CASE_						 = torch.from_numpy(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2    )]    )

									if str(SCREAMING_SNAKE_CASE_    ).startswith('''mps'''    ):
											# mps does not support float64
											SCREAMING_SNAKE_CASE_						 = timesteps.to(SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    )
									else:
											SCREAMING_SNAKE_CASE_						 = timesteps.to(device=SCREAMING_SNAKE_CASE_    )

									# empty dt and derivative
									SCREAMING_SNAKE_CASE_						 = None
									SCREAMING_SNAKE_CASE_						 = None

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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = np.log(SCREAMING_SNAKE_CASE_    )

									# get distribution
									SCREAMING_SNAKE_CASE_						 = log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									SCREAMING_SNAKE_CASE_						 = np.cumsum((dists >= 0)       ,       axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
									SCREAMING_SNAKE_CASE_						 = low_idx + 1

									SCREAMING_SNAKE_CASE_						 = log_sigmas[low_idx]
									SCREAMING_SNAKE_CASE_						 = log_sigmas[high_idx]

									# interpolate sigmas
									SCREAMING_SNAKE_CASE_						 = (low - log_sigma) / (low - high)
									SCREAMING_SNAKE_CASE_						 = np.clip(SCREAMING_SNAKE_CASE_       ,       0       ,       1    )

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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = in_sigmas[-1].item()
									SCREAMING_SNAKE_CASE_						 = in_sigmas[0].item()

									SCREAMING_SNAKE_CASE_						 = 7.0  # 7.0 is the value used in the paper
									SCREAMING_SNAKE_CASE_						 = np.linspace(0       ,       1       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = sigma_min ** (1 / rho)
									SCREAMING_SNAKE_CASE_						 = sigma_max ** (1 / rho)
									SCREAMING_SNAKE_CASE_						 = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self.dt is None

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = True       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.index_for_timestep(SCREAMING_SNAKE_CASE_    )

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

									if self.state_in_first_order:
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index]
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index + 1]
									else:
											# 2nd order / Heun's method
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index - 1]
											SCREAMING_SNAKE_CASE_						 = 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
									SCREAMING_SNAKE_CASE_						 = 0
									SCREAMING_SNAKE_CASE_						 = 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":
											SCREAMING_SNAKE_CASE_						 = sigma_hat if self.state_in_first_order else sigma_next
											SCREAMING_SNAKE_CASE_						 = sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
											SCREAMING_SNAKE_CASE_						 = sigma_hat if self.state_in_first_order else sigma_next
											SCREAMING_SNAKE_CASE_						 = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
											    sample / (sigma_input**2 + 1)
											)
									elif self.config.prediction_type == "sample":
											SCREAMING_SNAKE_CASE_						 = 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:
											SCREAMING_SNAKE_CASE_						 = 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
											SCREAMING_SNAKE_CASE_						 = (sample - pred_original_sample) / sigma_hat
											# 3. delta timestep
											SCREAMING_SNAKE_CASE_						 = sigma_next - sigma_hat

											# store for 2nd order step
											SCREAMING_SNAKE_CASE_						 = derivative
											SCREAMING_SNAKE_CASE_						 = dt
											SCREAMING_SNAKE_CASE_						 = sample
									else:
											# 2. 2nd order / Heun's method
											SCREAMING_SNAKE_CASE_						 = (sample - pred_original_sample) / sigma_next
											SCREAMING_SNAKE_CASE_						 = (self.prev_derivative + derivative) / 2

											# 3. take prev timestep & sample
											SCREAMING_SNAKE_CASE_						 = self.dt
											SCREAMING_SNAKE_CASE_						 = self.sample

											# free dt and derivative
											# Note, this puts the scheduler in "first order mode"
											SCREAMING_SNAKE_CASE_						 = None
											SCREAMING_SNAKE_CASE_						 = None
											SCREAMING_SNAKE_CASE_						 = None

									SCREAMING_SNAKE_CASE_						 = sample + derivative * dt

									if not return_dict:
											return (prev_sample,)

									return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.sigmas.to(device=original_samples.device       ,       dtype=original_samples.dtype    )
									if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_    ):
											# mps does not support float64
											SCREAMING_SNAKE_CASE_						 = self.timesteps.to(original_samples.device       ,       dtype=torch.floataa    )
											SCREAMING_SNAKE_CASE_						 = timesteps.to(original_samples.device       ,       dtype=torch.floataa    )
									else:
											SCREAMING_SNAKE_CASE_						 = self.timesteps.to(original_samples.device    )
											SCREAMING_SNAKE_CASE_						 = timesteps.to(original_samples.device    )

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

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

									SCREAMING_SNAKE_CASE_						 = original_samples + noise * sigma
									return noisy_samples







							def __len__(self    ):

									"""simple docstring"""







									return self.config.num_train_timesteps