



import numpy as np
from PIL import Image

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     np.array      ):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     np.array      ):
				"""simple docstring"""







				return (gray > 127) & (gray <= 255)

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     np.array			,       _SCREAMING_SNAKE_CASE    :     np.array      ):
				"""simple docstring"""







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

				# Copy image to padded image
				__a					       =						image

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


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

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





from typing import TYPE_CHECKING

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


lowerCamelCase__     =	{"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	[
						    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """HubertForCTC""",
						    """HubertForSequenceClassification""",
						    """HubertModel""",
						    """HubertPreTrainedModel""",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	[
						    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFHubertForCTC""",
						    """TFHubertModel""",
						    """TFHubertPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_hubert import (
												    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    HubertForCTC,
												    HubertForSequenceClassification,
												    HubertModel,
												    HubertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_hubert import (
												    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFHubertForCTC,
												    TFHubertModel,
												    TFHubertPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
