





'''simple docstring'''
import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


a__							:							int			=2 << 20  # 2mb
a__							:							Any			=get_logger(__name__)

class 					snake_case					(   __lowerCamelCase	):




 """simple docstring"""



 def 	_lowerCamelCase (	self	:							Tuple     ):
        __UpperCamelCase	=  self._options.get_all_options().get('runner'     )
        return runner in [None, "DirectRunner", "PortableRunner"]






def     lowercase__       (       __lowercase						:  List[Any]    ,  __lowercase						:  Optional[Any]    ,  __lowercase						:  Union[str, Any]=False					)			->				Any:



       """simple docstring"""






       __UpperCamelCase	=  FileSystems
       if fs.exists(__lowercase					):
              if force_upload:
                     logger.info(F'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''					)
              else:
                     logger.info(F'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'''					)
                     return
       with fs.create(__lowercase					) as remote_file:
              with open(__lowercase    ,  'rb'					) as local_file:
                     __UpperCamelCase	=  local_file.read(__lowercase					)
                     while chunk:
                            remote_file.write(__lowercase					)
                            __UpperCamelCase	=  local_file.read(__lowercase					)


def     lowercase__       (       __lowercase						:  List[Any]    ,  __lowercase						:  Dict    ,  __lowercase						:  Tuple=False					)			->				Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase	=  FileSystems
       if os.path.exists(__lowercase					):
              if force_download:
                     logger.info(F'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''					)
              else:
                     logger.info(F'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'''					)
                     return
       with fs.open(__lowercase					) as remote_file:
              with open(__lowercase    ,  'wb'					) as local_file:
                     __UpperCamelCase	=  remote_file.read(__lowercase					)
                     while chunk:
                            local_file.write(__lowercase					)
                            __UpperCamelCase	=  remote_file.read(__lowercase					)







'''simple docstring'''
import numpy as np
from PIL import Image
def     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __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     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       return (gray > 127) & (gray <= 255)
def     lowercase__       (       __lowercase						:  np.array    ,  __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	=  np.zeros_like(__lowercase					)
       __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 == 5					)
       return output


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

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