


"""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""")




"""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)

