
'''simple docstring'''


from pathlib import Path

import numpy as np
from PIL import Image



def  __snake_case     (    _UpperCAmelCase					:       np.ndarray):
   UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  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  __snake_case     (    _UpperCAmelCase					:       np.ndarray):
   return (gray > 127) & (gray <= 255)



def  __snake_case     (    _UpperCAmelCase					:       np.ndarray,      _UpperCAmelCase					:       np.ndarray):
   UpperCamelCase       =  np.zeros_like(_UpperCAmelCase)
   UpperCamelCase       =  np.zeros(
       (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1))

   # Copy image to padded image
   UpperCamelCase       =  image

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


if __name__ == "__main__":
   # read original image
   snake_case_ :  Union[str, Any]     =       Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
   snake_case_ :  Any     =       np.array(Image.open(lena_path))
   # kernel to be applied
   snake_case_ :  Union[str, Any]     =       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
   snake_case_ :  Union[str, Any]     =       dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
   # Save the output image
   snake_case_ :  Any     =       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


snake_case_ :  List[Any]     =					{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Union[str, Any]     =					[
       'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecAudioForAudioFrameClassification',
       'Data2VecAudioForCTC',
       'Data2VecAudioForSequenceClassification',
       'Data2VecAudioForXVector',
       'Data2VecAudioModel',
       'Data2VecAudioPreTrainedModel',
   ]
   snake_case_ :  str     =					[
       'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecTextForCausalLM',
       'Data2VecTextForMaskedLM',
       'Data2VecTextForMultipleChoice',
       'Data2VecTextForQuestionAnswering',
       'Data2VecTextForSequenceClassification',
       'Data2VecTextForTokenClassification',
       'Data2VecTextModel',
       'Data2VecTextPreTrainedModel',
   ]
   snake_case_ :  Optional[int]     =					[
       'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecVisionForImageClassification',
       'Data2VecVisionForMaskedImageModeling',
       'Data2VecVisionForSemanticSegmentation',
       'Data2VecVisionModel',
       'Data2VecVisionPreTrainedModel',
   ]

if is_tf_available():
   snake_case_ :  List[str]     =					[
       'TFData2VecVisionForImageClassification',
       'TFData2VecVisionForSemanticSegmentation',
       'TFData2VecVisionModel',
       'TFData2VecVisionPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
   from .configuration_dataavec_text import (
       DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecTextConfig,
       DataaVecTextOnnxConfig,
   )
   from .configuration_dataavec_vision import (
       DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecVisionConfig,
       DataaVecVisionOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dataavec_audio import (
          DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecAudioForAudioFrameClassification,
          DataaVecAudioForCTC,
          DataaVecAudioForSequenceClassification,
          DataaVecAudioForXVector,
          DataaVecAudioModel,
          DataaVecAudioPreTrainedModel,
      )
      from .modeling_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecTextForCausalLM,
          DataaVecTextForMaskedLM,
          DataaVecTextForMultipleChoice,
          DataaVecTextForQuestionAnswering,
          DataaVecTextForSequenceClassification,
          DataaVecTextForTokenClassification,
          DataaVecTextModel,
          DataaVecTextPreTrainedModel,
      )
      from .modeling_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecVisionForImageClassification,
          DataaVecVisionForMaskedImageModeling,
          DataaVecVisionForSemanticSegmentation,
          DataaVecVisionModel,
          DataaVecVisionPreTrainedModel,
      )
   if is_tf_available():
      from .modeling_tf_dataavec_vision import (
          TFDataaVecVisionForImageClassification,
          TFDataaVecVisionForSemanticSegmentation,
          TFDataaVecVisionModel,
          TFDataaVecVisionPreTrainedModel,
      )

else:
   import sys

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





