'''simple docstring'''





import cva
import numpy as np




class 	__A				:



    def __init__(self   :   Dict       ,      __a   :   float       ,      __a   :   int			):

         if k in (0.04, 0.06):
              UpperCAmelCase_	   =       k
              UpperCAmelCase_	   =       window_size
         else:
              raise ValueError("invalid k value"			)



    def __str__(self   :   Tuple			):
         return str(self.k			)






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

                   UpperCAmelCase_	   =       (wxx * wyy) - (wxy**2)
                   UpperCAmelCase_	   =       wxx + wyy
                   UpperCAmelCase_	   =       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__":
    SCREAMING_SNAKE_CASE_:					int  =HarrisCorner(0.04, 3)
    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_:					Dict  =edge_detect.detect('path_to_image')
    cva.imwrite('detect.png', color_img)





'''simple docstring'''





import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
    import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin




@require_torch
class 	__A				(			unittest.TestCase		,     UpperCamelCase__     ):



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       load_tool("text-to-speech"			)
         self.tool.setup()



    def  _lowercase						(self   :   Union[str, Any]			):
         # SpeechT5 isn't deterministic
         torch.manual_seed(0			)
         UpperCAmelCase_	   =       self.tool("hey"			)
         UpperCAmelCase_	   =       result.to_raw()
         self.assertTrue(
             torch.allclose(
                 resulting_tensor[:3]       ,      torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]			)       ,      )			)






    def  _lowercase						(self   :   List[str]			):
         # SpeechT5 isn't deterministic
         torch.manual_seed(0			)
         UpperCAmelCase_	   =       self.tool("hey"			)
         UpperCAmelCase_	   =       result.to_raw()
         self.assertTrue(
             torch.allclose(
                 resulting_tensor[:3]       ,      torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]			)       ,      )			)




