





"""simple docstring"""



from pathlib import Path

import numpy as np
from PIL import Image







def 		__lowercase					(       snake_case_  :  Dict						)       ->np.ndarray:



    '''simple docstring'''







    __A   :						List[Any]									=					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b







def 		__lowercase					(       snake_case_  :  Optional[Any]						)       ->np.ndarray:



    '''simple docstring'''







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







def 		__lowercase					(       snake_case_  :  Tuple    ,snake_case_  :  Union[str, Any]						)       ->np.ndarray:



    '''simple docstring'''







    __A   :						Tuple									=					np.zeros_like(__lowerCAmelCase						)
    __A   :						List[Any]									=					np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

    # Copy image to padded image
    __A   :						List[str]									=					image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]						):
        for y in range(image.shape[0]						):
            __A   :						Dict									=					(
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            __A   :						Dict									=					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""")










"""simple docstring"""



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_			=      {
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""VivitImageProcessor"""]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """VivitModel""",
           """VivitPreTrainedModel""",
           """VivitForVideoClassification""",
       ]


if TYPE_CHECKING:
       from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vivit import VivitImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vivit import (
                  VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VivitForVideoClassification,
                  VivitModel,
                  VivitPreTrainedModel,
              )


else:
       import sys

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




