
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class  UpperCAmelCase_					(						snake_case__       ):





 def __init__(     self			,							lowercase_			,							lowercase_ = None			,							lowercase_ = None			,							lowercase_ = None			,							lowercase_ = False			,							lowercase_ = False			,							lowercase_ = None			,							**lowercase_			,							):
     super().__init__(
         lowercase_			,							split=lowercase_			,							features=lowercase_			,							cache_dir=lowercase_			,							keep_in_memory=lowercase_			,							streaming=lowercase_			,							num_proc=lowercase_			,							**lowercase_			,							)
     snake_case_			:     Optional[int]        =    path_or_paths if isinstance(lowercase_			,							lowercase_) else {self.split: path_or_paths}
     snake_case_			:     Any        =    Csv(
         cache_dir=lowercase_			,							data_files=lowercase_			,							features=lowercase_			,							**lowercase_			,							)





 def 							snake_case__		(     self):
     # Build iterable dataset
     if self.streaming:
         snake_case_			:     List[str]        =    self.builder.as_streaming_dataset(split=self.split)
     # Build regular (map-style) dataset
     else:
         snake_case_			:     Any        =    None
         snake_case_			:     Optional[int]        =    None
         snake_case_			:     str        =    None
         snake_case_			:     Tuple        =    None

         self.builder.download_and_prepare(
             download_config=lowercase_			,							download_mode=lowercase_			,							verification_mode=lowercase_			,							base_path=lowercase_			,							num_proc=self.num_proc			,							)
         snake_case_			:     Optional[Any]        =    self.builder.as_dataset(
             split=self.split			,							verification_mode=lowercase_			,							in_memory=self.keep_in_memory)
     return dataset






class  UpperCAmelCase_					:





 def __init__(     self			,							lowercase_			,							lowercase_			,							lowercase_ = None			,							lowercase_ = None			,							**lowercase_			,							):
     if num_proc is not None and num_proc <= 0:
         raise ValueError(F'num_proc {num_proc} must be an integer > 0.')

     snake_case_			:     List[Any]        =    dataset
     snake_case_			:     List[str]        =    path_or_buf
     snake_case_			:     int        =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     snake_case_			:     List[Any]        =    num_proc
     snake_case_			:     Union[str, Any]        =    "utf-8"
     snake_case_			:     List[Any]        =    to_csv_kwargs





 def 							snake_case__		(     self):
     snake_case_			:     int        =    self.to_csv_kwargs.pop("path_or_buf"			,							lowercase_)
     snake_case_			:     Any        =    self.to_csv_kwargs.pop("header"			,							lowercase_)
     snake_case_			:     Any        =    self.to_csv_kwargs.pop("index"			,							lowercase_)

     if isinstance(self.path_or_buf			,							(str, bytes, os.PathLike)):
         with open(self.path_or_buf			,							"wb+") as buffer:
             snake_case_			:     Optional[Any]        =    self._write(file_obj=lowercase_			,							header=lowercase_			,							index=lowercase_			,							**self.to_csv_kwargs)
     else:
         snake_case_			:     str        =    self._write(file_obj=self.path_or_buf			,							header=lowercase_			,							index=lowercase_			,							**self.to_csv_kwargs)
     return written





 def 							snake_case__		(     self			,							lowercase_):
     snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_			:     List[Any]        =    args

     snake_case_			:     Union[str, Any]        =    query_table(
         table=self.dataset.data			,							key=slice(lowercase_			,							offset + self.batch_size)			,							indices=self.dataset._indices			,							)
     snake_case_			:     Dict        =    batch.to_pandas().to_csv(
         path_or_buf=lowercase_			,							header=header if (offset == 0) else False			,							index=lowercase_			,							**lowercase_)
     return csv_str.encode(self.encoding)





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_			,							**lowercase_):
     snake_case_			:     Optional[int]        =    0

     if self.num_proc is None or self.num_proc == 1:
         for offset in logging.tqdm(
             range(0			,							len(self.dataset)			,							self.batch_size)			,							unit="ba"			,							disable=not logging.is_progress_bar_enabled()			,							desc="Creating CSV from Arrow format"			,							):
             snake_case_			:     int        =    self._batch_csv((offset, header, index, to_csv_kwargs))
             written += file_obj.write(lowercase_)

     else:
         snake_case_      ,			snake_case_			:     Optional[int]        =    len(self.dataset), self.batch_size
         with multiprocessing.Pool(self.num_proc) as pool:
             for csv_str in logging.tqdm(
                 pool.imap(
                     self._batch_csv			,							[(offset, header, index, to_csv_kwargs) for offset in range(0			,							lowercase_			,							lowercase_)]			,							)			,							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,							unit="ba"			,							disable=not logging.is_progress_bar_enabled()			,							desc="Creating CSV from Arrow format"			,							):
                 written += file_obj.write(lowercase_)

     return written






'''simple docstring'''
import numpy as np
from PIL import Image

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_      ,			snake_case_      ,			snake_case_			:     List[str]        =    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    return (gray > 1_2_7) & (gray <= 2_5_5)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






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

    # Copy image to padded image
    snake_case_			:     Union[str, Any]        =    image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]							):
        for y in range(image.shape[0]							):
            snake_case_			:     List[str]        =    (
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            snake_case_			:     Dict        =    int(summation == 5							)
    return output


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

if __name__ == "__main__":
  # read original image
  a_           =    np.array(Image.open(R"..\image_data\lena.jpg"))
  # Apply erosion operation to a binary image
  a_           =    erosion(grayabinary(rgbagray(image)), structuring_element)
  # Save the output image
  a_           =    Image.fromarray(output).convert("RGB")
  pil_img.save("result_erosion.png")




