
"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A     :			List[Any]  						=			{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			List[Any]  						=			[
       """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """Data2VecAudioForAudioFrameClassification""",
       """Data2VecAudioForCTC""",
       """Data2VecAudioForSequenceClassification""",
       """Data2VecAudioForXVector""",
       """Data2VecAudioModel""",
       """Data2VecAudioPreTrainedModel""",
   ]
   _A     :			str  						=			[
       """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """Data2VecTextForCausalLM""",
       """Data2VecTextForMaskedLM""",
       """Data2VecTextForMultipleChoice""",
       """Data2VecTextForQuestionAnswering""",
       """Data2VecTextForSequenceClassification""",
       """Data2VecTextForTokenClassification""",
       """Data2VecTextModel""",
       """Data2VecTextPreTrainedModel""",
   ]
   _A     :			Dict  						=			[
       """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """Data2VecVisionForImageClassification""",
       """Data2VecVisionForMaskedImageModeling""",
       """Data2VecVisionForSemanticSegmentation""",
       """Data2VecVisionModel""",
       """Data2VecVisionPreTrainedModel""",
   ]

if is_tf_available():
   _A     :			Optional[int]  						=			[
       """TFData2VecVisionForImageClassification""",
       """TFData2VecVisionForSemanticSegmentation""",
       """TFData2VecVisionModel""",
       """TFData2VecVisionPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
   from .configuration_dataavec_text import (
       DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecTextConfig,
       DataaVecTextOnnxConfig,
   )
   from .configuration_dataavec_vision import (
       DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecVisionConfig,
       DataaVecVisionOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dataavec_audio import (
          DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecAudioForAudioFrameClassification,
          DataaVecAudioForCTC,
          DataaVecAudioForSequenceClassification,
          DataaVecAudioForXVector,
          DataaVecAudioModel,
          DataaVecAudioPreTrainedModel,
      )
      from .modeling_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecTextForCausalLM,
          DataaVecTextForMaskedLM,
          DataaVecTextForMultipleChoice,
          DataaVecTextForQuestionAnswering,
          DataaVecTextForSequenceClassification,
          DataaVecTextForTokenClassification,
          DataaVecTextModel,
          DataaVecTextPreTrainedModel,
      )
      from .modeling_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecVisionForImageClassification,
          DataaVecVisionForMaskedImageModeling,
          DataaVecVisionForSemanticSegmentation,
          DataaVecVisionModel,
          DataaVecVisionPreTrainedModel,
      )
   if is_tf_available():
      from .modeling_tf_dataavec_vision import (
          TFDataaVecVisionForImageClassification,
          TFDataaVecVisionForSemanticSegmentation,
          TFDataaVecVisionModel,
          TFDataaVecVisionPreTrainedModel,
      )

else:
   import sys

   _A     :			Tuple  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









'''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)


