


'''simple docstring'''






import cva
import numpy as np




class 	a       :
 def __init__(	self ,			__magic_name__ ,			__magic_name__  )   ->							int:

      if k in (0.0_4, 0.0_6):
           _a      =							k
           _a      =							window_size
      else:
           raise ValueError('invalid k value'  )
 def __str__(	self  )   ->							str:
      return str(self.k  )




 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							tuple[cva.Mat, list[list[int]]]:
      _a      =							cva.imread(__magic_name__ ,			0  )
      _a ,				_a      =							img.shape
      _a      =							[]
      _a      =							img.copy()
      _a      =							cva.cvtColor(__magic_name__ ,			cva.COLOR_GRAY2RGB  )
      _a ,				_a      =							np.gradient(__magic_name__  )
      _a      =							dx**2
      _a      =							dy**2
      _a      =							dx * dy
      _a      =							0.0_4
      _a      =							self.window_size // 2
      for y in range(__magic_name__ ,			h - offset  ):
           for x in range(__magic_name__ ,			w - offset  ):
                _a      =							ixx[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _a      =							iyy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _a      =							ixy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()

                _a      =							(wxx * wyy) - (wxy**2)
                _a      =							wxx + wyy
                _a      =							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__":
 a_   :      Any				    =      HarrisCorner(0.04, 3)
 a_    ,							a_   :      Optional[int]				    =      edge_detect.detect("path_to_image")
 cva.imwrite("detect.png", color_img)





'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      List[Any]				    =      {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      ["GPTNeoXTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[Any]				    =      [
     "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXForCausalLM",
     "GPTNeoXForQuestionAnswering",
     "GPTNeoXForSequenceClassification",
     "GPTNeoXForTokenClassification",
     "GPTNeoXLayer",
     "GPTNeoXModel",
     "GPTNeoXPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox import (
      GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXLayer,
      GPTNeoXModel,
      GPTNeoXPreTrainedModel,
  )


else:
 import sys

 a_   :      Optional[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

