





"""simple docstring"""






from typing import TYPE_CHECKING

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


A	     =					{
    '''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					['''MobileViTFeatureExtractor''']
		A	     =					['''MobileViTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MobileViTForImageClassification''',
		    '''MobileViTForSemanticSegmentation''',
		    '''MobileViTModel''',
		    '''MobileViTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFMobileViTForImageClassification''',
		    '''TFMobileViTForSemanticSegmentation''',
		    '''TFMobileViTModel''',
		    '''TFMobileViTPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilevit import MobileViTFeatureExtractor
				from .image_processing_mobilevit import MobileViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevit import (
				    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTForImageClassification,
				    MobileViTForSemanticSegmentation,
				    MobileViTModel,
				    MobileViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilevit import (
				    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileViTForImageClassification,
				    TFMobileViTForSemanticSegmentation,
				    TFMobileViTModel,
				    TFMobileViTPreTrainedModel,
				)


else:
		import sys

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






"""simple docstring"""






from pathlib import Path

import numpy as np
from PIL import Image

def       __A (     a_  :np.ndarray)						->						np.ndarray:
				__a    ,  __a    ,  __a	:  Union[str, Any]         =					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
				return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b

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

def       __A (     a_  :np.ndarray				,    a_  :np.ndarray)						->						np.ndarray:
				__a	:  Optional[int]         =					np.zeros_like(a_)
				__a	:  Dict         =					np.zeros(
				    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1))

				# Copy image to padded image
				__a	:  int         =					image

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


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