
"""simple docstring"""




import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class 						a__		( __lowercase		):
   __lowerCAmelCase				=							42
   __lowerCAmelCase				=							42


class 						a__		( __lowercase,     __lowercase		):
   __lowerCAmelCase				=							1
   @register_to_config
   def __init__(     self      ,       _a = 2_000      ,       _a = 0.1_5      ,       _a = 0.0_1      ,       _a = 1_3_4_8.0      ,       _a = 1E-5      ,       _a = 1      ,       ):
          lowercase       :							Dict									=				sigma_max

          # setable values
          lowercase       :							Optional[Any]									=				None

          self.set_sigmas(_a      ,       _a      ,       _a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          return sample
   def      __magic_name__     (     self      ,       _a      ,       _a = None      ,       _a = None							):
          lowercase       :							Union[str, Any]									=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

          lowercase       :							List[str]									=				torch.linspace(1      ,       _a      ,       _a      ,       device=_a							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None      ,       _a = None      ,       _a = None							):
          lowercase       :							Any									=				sigma_min if sigma_min is not None else self.config.sigma_min
          lowercase       :							Optional[Any]									=				sigma_max if sigma_max is not None else self.config.sigma_max
          lowercase       :							int									=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if self.timesteps is None:
                 self.set_timesteps(_a      ,       _a							)

          lowercase       :							Union[str, Any]									=				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
          lowercase       :							Any									=				torch.exp(torch.linspace(math.log(_a							)      ,       math.log(_a							)      ,       _a							)							)
          lowercase       :							List[Any]									=				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          return torch.where(
              timesteps == 0      ,       torch.zeros_like(t.to(timesteps.device							)							)      ,       self.discrete_sigmas[timesteps - 1].to(timesteps.device							)      ,       )
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a = None      ,       _a = True      ,       ):

          if self.timesteps is None:
                 raise ValueError(
                     "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"							)

          lowercase       :							List[str]									=				timestep * torch.ones(
              sample.shape[0]      ,       device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
          lowercase       :							Dict									=				(timestep * (len(self.timesteps							) - 1)).long()

          # mps requires indices to be in the same device, so we use cpu as is the default with cuda
          lowercase       :							int									=				timesteps.to(self.discrete_sigmas.device							)

          lowercase       :							Dict									=				self.discrete_sigmas[timesteps].to(sample.device							)
          lowercase       :							Union[str, Any]									=				self.get_adjacent_sigma(_a      ,       _a							).to(sample.device							)
          lowercase       :							List[str]									=				torch.zeros_like(_a							)
          lowercase       :							Union[str, Any]									=				(sigma**2 - adjacent_sigma**2) ** 0.5

          # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
          # also equation 47 shows the analog from SDE models to ancestral sampling methods
          lowercase       :							int									=				diffusion.flatten()
          while len(diffusion.shape							) < len(sample.shape							):
                 lowercase       :							List[str]									=				diffusion.unsqueeze(-1							)
          lowercase       :							Optional[int]									=				drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          lowercase       :							Any									=				randn_tensor(
              sample.shape      ,       layout=sample.layout      ,       generator=_a      ,       device=sample.device      ,       dtype=sample.dtype							)
          lowercase       :							Union[str, Any]									=				sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          lowercase       :							Tuple									=				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

          if not return_dict:
                 return (prev_sample, prev_sample_mean)

          return SdeVeOutput(prev_sample=_a      ,       prev_sample_mean=_a							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a = None      ,       _a = True      ,       ):

          if self.timesteps is None:
                 raise ValueError(
                     "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"							)

          # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
          # sample noise for correction
          lowercase       :							str									=				randn_tensor(sample.shape      ,       layout=sample.layout      ,       generator=_a							).to(sample.device							)

          # compute step size from the model_output, the noise, and the snr
          lowercase       :							List[str]									=				torch.norm(model_output.reshape(model_output.shape[0]      ,       -1							)      ,       dim=-1							).mean()
          lowercase       :							Optional[Any]									=				torch.norm(noise.reshape(noise.shape[0]      ,       -1							)      ,       dim=-1							).mean()
          lowercase       :							Optional[Any]									=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
          lowercase       :							Union[str, Any]									=				step_size * torch.ones(sample.shape[0]							).to(sample.device							)
          # self.repeat_scalar(step_size, sample.shape[0])

          # compute corrected sample: model_output term and noise term
          lowercase       :							int									=				step_size.flatten()
          while len(step_size.shape							) < len(sample.shape							):
                 lowercase       :							int									=				step_size.unsqueeze(-1							)
          lowercase       :							Any									=				sample + step_size * model_output
          lowercase       :							List[Any]									=				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
                 return (prev_sample,)

          return SchedulerOutput(prev_sample=_a							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       ):
          lowercase       :							Union[str, Any]									=				timesteps.to(original_samples.device							)
          lowercase       :							Union[str, Any]									=				self.discrete_sigmas.to(original_samples.device							)[timesteps]
          lowercase       :							Union[str, Any]									=				(
              noise * sigmas[:, None, None, None]
              if noise is not None
              else torch.randn_like(_a							) * sigmas[:, None, None, None]
          )
          lowercase       :							Union[str, Any]									=				noise + original_samples
          return noisy_samples

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









'''simple docstring'''






import cva
import numpy as np




class  _A :

		def __init__(   self					:   Any  ,      __magic_name__					:   float  ,      __magic_name__					:   int			)					->    Optional[int]:


					"""simple docstring"""

					if k in (0.04, 0.06):
								__snake_case					:      List[str]							      =     k
								__snake_case					:      int							      =     window_size
					else:
								raise ValueError("""invalid k value"""			)

		def __str__(   self					:   Union[str, Any]			)					->    str:


					"""simple docstring"""

					return str(self.k			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   str			)					->    tuple[cva.Mat, list[list[int]]]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     cva.imread(__magic_name__  ,      0			)
					__snake_case      ,     __snake_case					:      List[str]							      =     img.shape
					__snake_case					:      list[list[int]]							      =     []
					__snake_case					:      str							      =     img.copy()
					__snake_case					:      Tuple							      =     cva.cvtColor(__magic_name__  ,      cva.COLOR_GRAY2RGB			)
					__snake_case      ,     __snake_case					:      List[Any]							      =     np.gradient(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     dx**2
					__snake_case					:      Tuple							      =     dy**2
					__snake_case					:      List[Any]							      =     dx * dy
					__snake_case					:      List[Any]							      =     0.04
					__snake_case					:      Tuple							      =     self.window_size // 2
					for y in range(__magic_name__  ,      h - offset			):
								for x in range(__magic_name__  ,      w - offset			):
											__snake_case					:      Dict							      =     ixx[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      Optional[int]							      =     iyy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      str							      =     ixy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()

											__snake_case					:      List[str]							      =     (wxx * wyy) - (wxy**2)
											__snake_case					:      Dict							      =     wxx + wyy
											__snake_case					:      List[str]							      =     det - k * (trace**2)
											# Can change the value
											if r > 0.5:
														corner_list.append([x, y, r]			)
														color_img.itemset((y, x, 0)  ,      0			)
														color_img.itemset((y, x, 1)  ,      0			)
														color_img.itemset((y, x, 2)  ,      2_55			)
					return color_img, corner_list


if __name__ == "__main__":
	__UpperCamelCase					    =	HarrisCorner(0.04, 3)
	__UpperCamelCase    ,		__UpperCamelCase					    =	edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)


