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

lowercase__    :       Union[str, Any]						=			1_8_0

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			str:
							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__     ,						snake_case__     ,						snake_case__							)  ->			Union[str, 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 1_5 * 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 < 1_1 * 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 1_1 * PI / 8 <= direction < 1_3 * 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 1_3 * PI / 8 <= direction < 1_5 * 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__     ,						snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)  ->			Optional[int]:
							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__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)  ->			Union[str, Any]:
							for row in range(1     ,						image_shape[0]							):
														for col in range(1     ,						image_shape[1]							):
																					if destination[row, col] == weak:
																												if 2_5_5 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__     ,						snake_case__=1_5     ,						snake_case__=3_0     ,						snake_case__=1_2_8     ,						snake_case__=2_5_5							)  ->			str:
							# 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
	lowercase__    :       str						=			cva.imread(R'''../image_data/lena.jpg''', 0)
	# canny edge detection
	lowercase__    :       int						=			canny(lena)
	cva.imshow('''canny''', canny_destination)
	cva.waitKey(0)




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


lowercase__    :       List[Any]						=			logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      torch.FloatTensor




class      lowercase_						(						UpperCamelCase_   ,  UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      Tuple     =       1


	@register_to_config
	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 40	,    __SCREAMING_SNAKE_CASE = 0.0_0_2	,    __SCREAMING_SNAKE_CASE = 8_0.0	,    __SCREAMING_SNAKE_CASE = 0.5	,    __SCREAMING_SNAKE_CASE = 1.0	,    __SCREAMING_SNAKE_CASE = 7.0	,    __SCREAMING_SNAKE_CASE = True	,    )		->Tuple:
								# standard deviation of the initial noise distribution
								lowerCAmelCase				=	sigma_max

								lowerCAmelCase				=	np.linspace(0	,    1	,    __SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self._convert_to_karras(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigma_to_t(__SCREAMING_SNAKE_CASE       )

								# setable values
								lowerCAmelCase				=	None
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	False
								lowerCAmelCase				=	False


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
								if schedule_timesteps is None:
															lowerCAmelCase				=	self.timesteps

								lowerCAmelCase				=	(schedule_timesteps == timestep).nonzero()
								return indices.item()


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->torch.FloatTensor:
								# Get sigma corresponding to timestep
								if isinstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       ):
															lowerCAmelCase				=	timestep.to(self.timesteps.device       )
								lowerCAmelCase				=	self.index_for_timestep(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigmas[step_idx]

								lowerCAmelCase				=	sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

								lowerCAmelCase				=	True
								return sample


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								if not isinstance(__SCREAMING_SNAKE_CASE	,    np.ndarray       ):
															lowerCAmelCase				=	np.array(__SCREAMING_SNAKE_CASE	,    dtype=np.floataa       )

								lowerCAmelCase				=	1000 * 0.2_5 * np.log(sigmas + 1e-44       )

								return timesteps


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    )		->str:
								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(__SCREAMING_SNAKE_CASE       )       ):
																						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}."       )

															lowerCAmelCase				=	np.array(__SCREAMING_SNAKE_CASE	,    dtype=np.intaa       )
															lowerCAmelCase				=	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."       )

															lowerCAmelCase				=	num_inference_steps

															lowerCAmelCase				=	self.config.num_train_timesteps // self.num_inference_steps
															lowerCAmelCase				=	(np.arange(0	,    __SCREAMING_SNAKE_CASE       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
															lowerCAmelCase				=	False

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

								lowerCAmelCase				=	np.concatenate([sigmas, [self.sigma_min]]       ).astype(np.floataa       )
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE       )

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


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_max

								lowerCAmelCase				=	self.config.rho
								lowerCAmelCase				=	sigma_min ** (1 / rho)
								lowerCAmelCase				=	sigma_max ** (1 / rho)
								lowerCAmelCase				=	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								lowerCAmelCase				=	self.config.sigma_data

								lowerCAmelCase				=	sigma_data**2 / (sigma**2 + sigma_data**2)
								lowerCAmelCase				=	sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->List[str]:
								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_data

								lowerCAmelCase				=	sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
								lowerCAmelCase				=	(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = True	,    )		->Union[CMStochasticIterativeSchedulerOutput, Tuple]:

								if (
								    isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
								    or isinstance(__SCREAMING_SNAKE_CASE	,    torch.IntTensor       )
								    or isinstance(__SCREAMING_SNAKE_CASE	,    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(__SCREAMING_SNAKE_CASE	,    torch.Tensor       ):
															lowerCAmelCase				=	timestep.to(self.timesteps.device       )

								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_max

								lowerCAmelCase				=	self.index_for_timestep(__SCREAMING_SNAKE_CASE       )

								# sigma_next corresponds to next_t in original implementation
								lowerCAmelCase				=	self.sigmas[step_index]
								if step_index + 1 < self.config.num_train_timesteps:
															lowerCAmelCase				=	self.sigmas[step_index + 1]
								else:
															# Set sigma_next to sigma_min
															lowerCAmelCase				=	self.sigmas[-1]

								# Get scalings for boundary conditions
								lowerCAmelCase	,   lowerCAmelCase				=	self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE       )

								# 1. Denoise model output using boundary conditions
								lowerCAmelCase				=	c_out * model_output + c_skip * sample
								if self.config.clip_denoised:
															lowerCAmelCase				=	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:
															lowerCAmelCase				=	randn_tensor(
															    model_output.shape	,    dtype=model_output.dtype	,    device=model_output.device	,    generator=__SCREAMING_SNAKE_CASE       )
								else:
															lowerCAmelCase				=	torch.zeros_like(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	noise * self.config.s_noise

								lowerCAmelCase				=	sigma_next.clamp(min=__SCREAMING_SNAKE_CASE	,    max=__SCREAMING_SNAKE_CASE       )

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

								if not return_dict:
															return (prev_sample,)

								return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    )		->torch.FloatTensor:
								# 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(__SCREAMING_SNAKE_CASE       ):
															# 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				=	[(schedule_timesteps == t).nonzero().item() 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       )		->int:
								return self.config.num_train_timesteps



