code: 
  
  
  """simple docstring"""
  import numpy as np
  from PIL import Image
  
  def 							_snake_case			(      __snake_case       :			Any      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				str      =		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.2989 * r + 0.5870 * g + 0.1140 * b
  
  def 							_snake_case			(      __snake_case       :			List[str]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return (gray > 127) & (gray <= 255)
  
  def 							_snake_case			(      __snake_case       :			List[Any]	,   __snake_case       :			Dict      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				List[str]      =		np.zeros_like(SCREAMING_SNAKE_CASE__      )
     _lowerCamelCase  :				str      =		np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )
  
     # Copy image to padded image
     _lowerCamelCase  :				List[Any]      =		image
  
     # Iterate over image & apply kernel
     for x in range(image.shape[1]      ):
        for y in range(image.shape[0]      ):
           _lowerCamelCase  :				Dict      =		(
               kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
           ).sum()
           _lowerCamelCase  :				List[str]      =		int(summation == 5      )
     return output
  
  
  # kernel to be applied
  UpperCAmelCase   =	np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
  
  if __name__ == "__main__":
   # read original image
   UpperCAmelCase   =	np.array(Image.open(r"""..\image_data\lena.jpg"""))
   # Apply erosion operation to a binary image
   UpperCAmelCase   =	erosion(grayabinary(rgbagray(image)), structuring_element)
   # Save the output image
   UpperCAmelCase   =	Image.fromarray(output).convert("""RGB""")
   pil_img.save("""result_erosion.png""")
  
style_context: 
  
  
  """simple docstring"""
  import itertools
  from dataclasses import dataclass
  from io import StringIO
  from typing import Optional
  
  import pyarrow as pa
  
  import datasets
  from datasets.features.features import require_storage_cast
  from datasets.table import table_cast
  
  
  UpperCAmelCase   =	datasets.utils.logging.get_logger(__name__)
  
  
  
  
  @dataclass
  class   lowercase__							(				datasets.BuilderConfig							):
     __UpperCAmelCase       					=				None
     __UpperCAmelCase       					=				"utf-8"
     __UpperCAmelCase       					=				None
     __UpperCAmelCase       					=				10 << 20  # 10MB
     __UpperCAmelCase       					=				False
     __UpperCAmelCase       					=				"line"
  
  
  
  
  
  
  
  class   lowercase__							(				datasets.ArrowBasedBuilder							):
     __UpperCAmelCase       					=				TextConfig
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        return datasets.DatasetInfo(features=self.config.features)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Optional[int]:
        if not self.config.data_files:
           raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}')
        _lowerCamelCase  :				Dict      =		dl_manager.download_and_extract(self.config.data_files)
        if isinstance(SCREAMING_SNAKE_CASE			,      (str, list, tuple)):
           _lowerCamelCase  :				Optional[Any]      =		data_files
           if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              _lowerCamelCase  :				List[Any]      =		[files]
           _lowerCamelCase  :				List[str]      =		[dl_manager.iter_files(SCREAMING_SNAKE_CASE) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,      gen_kwargs={"""files""": files})]
        _lowerCamelCase  :				int      =		[]
        for split_name, files in data_files.items():
           if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              _lowerCamelCase  :				Any      =		[files]
           _lowerCamelCase  :				Any      =		[dl_manager.iter_files(SCREAMING_SNAKE_CASE) for file in files]
           splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE			,      gen_kwargs={"""files""": files}))
        return splits
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     pa.Table:
        if self.config.features is not None:
           _lowerCamelCase  :				Union[str, Any]      =		self.config.features.arrow_schema
           if all(not require_storage_cast(SCREAMING_SNAKE_CASE) for feature in self.config.features.values()):
              # cheaper cast
              _lowerCamelCase  :				Any      =		pa_table.cast(SCREAMING_SNAKE_CASE)
           else:
              # more expensive cast; allows str <-> int/float or str to Audio for example
              _lowerCamelCase  :				Any      =		table_cast(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
           return pa_table
        else:
           return pa_table.cast(pa.schema({"""text""": pa.string()}))
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Any:
        _lowerCamelCase  :				Optional[Any]      =		list(self.config.features) if self.config.features is not None else ["""text"""]
        for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE)):
           # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
           with open(SCREAMING_SNAKE_CASE			,      encoding=self.config.encoding			,      errors=self.config.errors) as f:
              if self.config.sample_by == "line":
                 _lowerCamelCase  :				Union[str, Any]      =		0
                 while True:
                    _lowerCamelCase  :				int      =		f.read(self.config.chunksize)
                    if not batch:
                       break
                    batch += f.readline()  # finish current line
                    # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
                    _lowerCamelCase  :				List[str]      =		StringIO(SCREAMING_SNAKE_CASE).readlines()
                    if not self.config.keep_linebreaks:
                       _lowerCamelCase  :				Optional[Any]      =		[line.rstrip("""\n""") for line in batch]
                    _lowerCamelCase  :				Any      =		pa.Table.from_arrays([pa.array(SCREAMING_SNAKE_CASE)]			,      names=SCREAMING_SNAKE_CASE)
                    # Uncomment for debugging (will print the Arrow table size and elements)
                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                    yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE)
                    batch_idx += 1
              elif self.config.sample_by == "paragraph":
                 _lowerCamelCase  :				Optional[Any]      =		0
                 _lowerCamelCase  :				Dict      =		""""""
                 while True:
                    _lowerCamelCase  :				Union[str, Any]      =		f.read(self.config.chunksize)
                    if not new_batch:
                       break
                    batch += new_batch
                    batch += f.readline()  # finish current line
                    _lowerCamelCase  :				Any      =		batch.split("""\n\n""")
                    _lowerCamelCase  :				Dict      =		pa.Table.from_arrays(
                        [pa.array([example for example in batch[:-1] if example])]			,      names=SCREAMING_SNAKE_CASE)
                    # Uncomment for debugging (will print the Arrow table size and elements)
                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                    yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE)
                    batch_idx += 1
                    _lowerCamelCase  :				Union[str, Any]      =		batch[-1]
                 if batch:
                    _lowerCamelCase  :				Optional[int]      =		pa.Table.from_arrays([pa.array([batch])]			,      names=SCREAMING_SNAKE_CASE)
                    yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE)
              elif self.config.sample_by == "document":
                 _lowerCamelCase  :				Tuple      =		f.read()
                 _lowerCamelCase  :				List[Any]      =		pa.Table.from_arrays([pa.array([text])]			,      names=SCREAMING_SNAKE_CASE)
                 yield file_idx, self._cast_table(SCREAMING_SNAKE_CASE)
  
  