
import numpy as np
from PIL import Image



def      __UpperCAmelCase    (  snake_case_							:		np.array				):



	'''simple docstring'''



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



def      __UpperCAmelCase    (  snake_case_							:		np.array				):



	'''simple docstring'''



	return (gray > 1_2_7) & (gray <= 2_5_5)



def      __UpperCAmelCase    (  snake_case_							:		np.array				,					snake_case_							:		np.array				):



	'''simple docstring'''



	UpperCAmelCase:					List[Any] 						=     np.zeros_like(snake_case_				)
	UpperCAmelCase:					Dict 						=     np.zeros(
	    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)				)

	# Copy image to padded image
	UpperCAmelCase:					Union[str, Any] 						=     image

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


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

if __name__ == "__main__":
	# read original image
	snake_case_      :					List[str]				      =     np.array(Image.open(R'..\image_data\lena.jpg'))
	# Apply erosion operation to a binary image
	snake_case_      :					Optional[Any]				      =     erosion(grayabinary(rgbagray(image)), structuring_element)
	# Save the output image
	snake_case_      :					Optional[int]				      =     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


snake_case_      :					Any				      =     {
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					str				      =     ['ImageGPTFeatureExtractor']
	snake_case_      :					Optional[int]				      =     ['ImageGPTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					List[str]				      =     [
	    'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ImageGPTForCausalImageModeling',
	    'ImageGPTForImageClassification',
	    'ImageGPTModel',
	    'ImageGPTPreTrainedModel',
	    'load_tf_weights_in_imagegpt',
	]


if TYPE_CHECKING:
	from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
		from .image_processing_imagegpt import ImageGPTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_imagegpt import (
		    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ImageGPTForCausalImageModeling,
		    ImageGPTForImageClassification,
		    ImageGPTModel,
		    ImageGPTPreTrainedModel,
		    load_tf_weights_in_imagegpt,
		)

else:
	import sys

	snake_case_      :					Dict				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
