


"""simple docstring"""




from pathlib import Path

import numpy as np
from PIL import Image


def     lowercase			(SCREAMING_SNAKE_CASE_		:				np.ndarray  )   ->						np.ndarray:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
						return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b


def     lowercase			(SCREAMING_SNAKE_CASE_		:				np.ndarray  )   ->						np.ndarray:
						return (gray > 1_27) & (gray <= 2_55)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				np.ndarray ,			SCREAMING_SNAKE_CASE_		:				np.ndarray  )   ->						np.ndarray:
						SCREAMING_SNAKE_CASE													=					np.zeros_like(SCREAMING_SNAKE_CASE_  )
						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 > 0  )
						return output


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






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase						=      {
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      ['''BlipImageProcessor''']


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlipModel''',
	    '''BlipPreTrainedModel''',
	    '''BlipForConditionalGeneration''',
	    '''BlipForQuestionAnswering''',
	    '''BlipVisionModel''',
	    '''BlipTextModel''',
	    '''BlipForImageTextRetrieval''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFBlipModel''',
	    '''TFBlipPreTrainedModel''',
	    '''TFBlipForConditionalGeneration''',
	    '''TFBlipForQuestionAnswering''',
	    '''TFBlipVisionModel''',
	    '''TFBlipTextModel''',
	    '''TFBlipForImageTextRetrieval''',
	]

if TYPE_CHECKING:
	from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
	from .processing_blip import BlipProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_blip import BlipImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blip import (
		    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlipForConditionalGeneration,
		    BlipForImageTextRetrieval,
		    BlipForQuestionAnswering,
		    BlipModel,
		    BlipPreTrainedModel,
		    BlipTextModel,
		    BlipVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blip import (
		    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFBlipForConditionalGeneration,
		    TFBlipForImageTextRetrieval,
		    TFBlipForQuestionAnswering,
		    TFBlipModel,
		    TFBlipPreTrainedModel,
		    TFBlipTextModel,
		    TFBlipVisionModel,
		)

else:
	import sys

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


