
"""simple docstring"""
import cva
import numpy as np
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       if k in (0.04, 0.06):
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = k
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = window_size
       else:
         raise ValueError("invalid k value"				)
     def __str__(     self				):




       """simple docstring"""


       return str(self.k				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


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

           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = (wxx * wyy) - (wxy**2)
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = wxx + wyy
           _SCREAMING_SNAKE_CASE   :				Any							    = 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)  ,     255				)
       return color_img, corner_list


if __name__ == "__main__":
      lowercase_       :						Optional[int]     					=	HarrisCorner(0.0_4, 3)
      lowercase_					,	lowercase_       :						Union[str, Any]     					=	edge_detect.detect('''path_to_image''')
      cva.imwrite('''detect.png''', color_img)





"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			42
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE  ):
     @register_to_config
     def __init__(     self  ,     snake_case__ = 3  ,     snake_case__ = 3  ,     snake_case__ = ("DownEncoderBlock2D",)  ,     snake_case__ = ("UpDecoderBlock2D",)  ,     snake_case__ = (64,)  ,     snake_case__ = 1  ,     snake_case__ = "silu"  ,     snake_case__ = 3  ,     snake_case__ = 32  ,     snake_case__ = 256  ,     snake_case__ = 32  ,     snake_case__ = None  ,     snake_case__ = 0.18_215  ,     snake_case__ = "group"  ,     ):




       """simple docstring"""


       super().__init__()

       # pass init params to Encoder
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = Encoder(
           in_channels=snake_case__  ,     out_channels=snake_case__  ,     down_block_types=snake_case__  ,     block_out_channels=snake_case__  ,     layers_per_block=snake_case__  ,     act_fn=snake_case__  ,     norm_num_groups=snake_case__  ,     double_z=snake_case__  ,     )

       _SCREAMING_SNAKE_CASE   :				Dict							    = vq_embed_dim if vq_embed_dim is not None else latent_channels

       _SCREAMING_SNAKE_CASE   :				Tuple							    = nn.Convad(snake_case__  ,     snake_case__  ,     1				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = VectorQuantizer(snake_case__  ,     snake_case__  ,     beta=0.25  ,     remap=snake_case__  ,     sane_index_shape=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = nn.Convad(snake_case__  ,     snake_case__  ,     1				)

       # pass init params to Decoder
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = Decoder(
           in_channels=snake_case__  ,     out_channels=snake_case__  ,     up_block_types=snake_case__  ,     block_out_channels=snake_case__  ,     layers_per_block=snake_case__  ,     act_fn=snake_case__  ,     norm_num_groups=snake_case__  ,     norm_type=snake_case__  ,     )
     @apply_forward_hook
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = True				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.encoder(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.quant_conv(snake_case__				)

       if not return_dict:
         return (h,)

       return VQEncoderOutput(latents=snake_case__				)
     @apply_forward_hook
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = False  ,     snake_case__ = True				):




       """simple docstring"""


       if not force_not_quantize:
         _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = self.quantize(snake_case__				)
       else:
         _SCREAMING_SNAKE_CASE   :				int							    = h
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.post_quant_conv(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				int							    = self.decoder(snake_case__  ,     quant if self.config.norm_type == "spatial" else None				)

       if not return_dict:
         return (dec,)

       return DecoderOutput(sample=snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = True				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = sample
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.encode(snake_case__				).latents
       _SCREAMING_SNAKE_CASE   :				Any							    = self.decode(snake_case__				).sample

       if not return_dict:
         return (dec,)

       return DecoderOutput(sample=snake_case__				)



