



'''simple docstring'''





from pathlib import Path

import numpy as np
from PIL import Image
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Any											= rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    return (gray > 127) & (gray <= 255)
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[Any]											= np.zeros_like(UpperCamelCase      )
    lowerCAmelCase__			:  str											= np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

    # Copy image to padded image
    lowerCAmelCase__			:  Any											= image

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


if __name__ == "__main__":
 # read original image
 _lowerCAmelCase										=     Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
 _lowerCAmelCase										=     np.array(Image.open(lena_path))
 # kernel to be applied
 _lowerCAmelCase										=     np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
 _lowerCAmelCase										=     dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
 # Save the output image
 _lowerCAmelCase										=     Image.fromarray(output).convert('''RGB''')
 pil_img.save('''result_dilation.png''')







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase										=     {
    '''configuration_blip_2''': [
        '''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Blip2Config''',
        '''Blip2QFormerConfig''',
        '''Blip2VisionConfig''',
    ],
    '''processing_blip_2''': ['''Blip2Processor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase										=     [
     '''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Blip2Model''',
     '''Blip2QFormerModel''',
     '''Blip2PreTrainedModel''',
     '''Blip2ForConditionalGeneration''',
     '''Blip2VisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_blip_a import (
     BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlipaConfig,
     BlipaQFormerConfig,
     BlipaVisionConfig,
 )
 from .processing_blip_a import BlipaProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blip_a import (
      BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlipaForConditionalGeneration,
      BlipaModel,
      BlipaPreTrainedModel,
      BlipaQFormerModel,
      BlipaVisionModel,
  )

else:
 import sys

 _lowerCAmelCase										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


