import numpy as np
from PIL import Image



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			np.array      )				->   np.array:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.2989 * r + 0.5870 * g + 0.1140 * b



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			np.array      )				->   np.array:







  """simple docstring"""





  return (gray > 1_27) & (gray <= 2_55)



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			np.array					,	__UpperCamelCase       :			np.array      )				->   np.array:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= np.zeros_like(__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__     				= np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

  # Copy image to padded image
  SCREAMING_SNAKE_CASE__     				= image

  # Iterate over image & apply kernel
  for x in range(image.shape[1]      ):
    for y in range(image.shape[0]      ):
      SCREAMING_SNAKE_CASE__     				= (
          kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
      ).sum()
      SCREAMING_SNAKE_CASE__     				= int(summation == 5      )
  return output


# kernel to be applied
__lowerCamelCase     :					Union[str, Any]				  =						np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
   # read original image
   __lowerCamelCase     :					Optional[Any]				  =						np.array(Image.open(r'''..\image_data\lena.jpg'''))
   # Apply erosion operation to a binary image
   __lowerCamelCase     :					List[Any]				  =						erosion(grayabinary(rgbagray(image)), structuring_element)
   # Save the output image
   __lowerCamelCase     :					Tuple				  =						Image.fromarray(output).convert('''RGB''')
   pil_img.save('''result_erosion.png''')

from typing import TYPE_CHECKING

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


__lowerCamelCase     :					Optional[int]				  =						{'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase     :					Dict				  =						['''ViltFeatureExtractor''']
   __lowerCamelCase     :					Dict				  =						['''ViltImageProcessor''']
   __lowerCamelCase     :					str				  =						['''ViltProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase     :					Any				  =						[
       '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ViltForImageAndTextRetrieval''',
       '''ViltForImagesAndTextClassification''',
       '''ViltForTokenClassification''',
       '''ViltForMaskedLM''',
       '''ViltForQuestionAnswering''',
       '''ViltLayer''',
       '''ViltModel''',
       '''ViltPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vilt import ViltFeatureExtractor
      from .image_processing_vilt import ViltImageProcessor
      from .processing_vilt import ViltProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vilt import (
          VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViltForImageAndTextRetrieval,
          ViltForImagesAndTextClassification,
          ViltForMaskedLM,
          ViltForQuestionAnswering,
          ViltForTokenClassification,
          ViltLayer,
          ViltModel,
          ViltPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase     :					List[str]				  =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)
