





import cva
import numpy as np

class 		UpperCamelCase__			:



					'''simple docstring'''

					def __init__(							self						:   str						,lowerCamelCase__						:   str						,lowerCamelCase__						:   int	)					->		Optional[Any]:




											'''simple docstring'''


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






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




											'''simple docstring'''


											return str(self.k	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Optional[Any]	)					->		tuple[cva.Mat, list[list[int]]]:




											'''simple docstring'''


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

																							SCREAMING_SNAKE_CASE		=						(wxx * wyy) - (wxy**2)
																							SCREAMING_SNAKE_CASE		=						wxx + wyy
																							SCREAMING_SNAKE_CASE		=						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_						       =	HarrisCorner(0.04, 3)
						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_						       =	edge_detect.detect("""path_to_image""")
						cva.imwrite("""detect.png""", color_img)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						       =	{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ReformerAttention""",
          """ReformerForMaskedLM""",
          """ReformerForQuestionAnswering""",
          """ReformerForSequenceClassification""",
          """ReformerLayer""",
          """ReformerModel""",
          """ReformerModelWithLMHead""",
          """ReformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer import ReformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer_fast import ReformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_reformer import (
                REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ReformerAttention,
                ReformerForMaskedLM,
                ReformerForQuestionAnswering,
                ReformerForSequenceClassification,
                ReformerLayer,
                ReformerModel,
                ReformerModelWithLMHead,
                ReformerPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


