
"""simple docstring"""



import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Features						):






							"""simple docstring"""



							_lowerCAmelCase  	=			np.inf

							def set_batch_size(SCREAMING_SNAKE_CASE:					FeatureType						) -> None:
														nonlocal batch_size
														if isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			min(SCREAMING_SNAKE_CASE     ,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS						)
														elif isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			min(SCREAMING_SNAKE_CASE     ,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS						)
														elif isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						) and feature.dtype == "binary":
																					_lowerCAmelCase  	=			min(SCREAMING_SNAKE_CASE     ,  config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS						)

							_visit(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

							return None if batch_size is np.inf else batch_size
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				def __init__(					self	:			str				,     UpperCAmelCase_	:			NestedDataStructureLike[PathLike]				,     UpperCAmelCase_	:			Optional[NamedSplit] = None				,     UpperCAmelCase_	:			Optional[Features] = None				,     UpperCAmelCase_	:			str = None				,     UpperCAmelCase_	:			bool = False				,     UpperCAmelCase_	:			bool = False				,     UpperCAmelCase_	:			Optional[int] = None				,     **UpperCAmelCase_	:			List[str]				,     )		->  int:







											"""simple docstring"""




											super().__init__(
											    UpperCAmelCase_				,     split=UpperCAmelCase_				,     features=UpperCAmelCase_				,     cache_dir=UpperCAmelCase_				,     keep_in_memory=UpperCAmelCase_				,     streaming=UpperCAmelCase_				,     num_proc=UpperCAmelCase_				,     **UpperCAmelCase_				,     )
											_lowerCAmelCase  	=			path_or_paths if isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ) else {self.split: path_or_paths}
											_lowerCAmelCase  	=			_PACKAGED_DATASETS_MODULES['parquet'][1]
											_lowerCAmelCase  	=			Parquet(
											    cache_dir=UpperCAmelCase_				,     data_files=UpperCAmelCase_				,     features=UpperCAmelCase_				,     hash=UpperCAmelCase_				,     **UpperCAmelCase_				,     )







				def 				__lowerCamelCase	(					self	:			Any  )		->  Union[str, Any]:







											"""simple docstring"""




											if self.streaming:
																		_lowerCAmelCase  	=			self.builder.as_streaming_dataset(split=self.split  )
											# Build regular (map-style) dataset
											else:
																		_lowerCAmelCase  	=			None
																		_lowerCAmelCase  	=			None
																		_lowerCAmelCase  	=			None
																		_lowerCAmelCase  	=			None

																		self.builder.download_and_prepare(
																		    download_config=UpperCAmelCase_				,     download_mode=UpperCAmelCase_				,     verification_mode=UpperCAmelCase_				,     base_path=UpperCAmelCase_				,     num_proc=self.num_proc				,     )
																		_lowerCAmelCase  	=			self.builder.as_dataset(
																		    split=self.split				,     verification_mode=UpperCAmelCase_				,     in_memory=self.keep_in_memory  )
											return dataset






class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				def __init__(					self	:			List[str]				,     UpperCAmelCase_	:			Dataset				,     UpperCAmelCase_	:			Union[PathLike, BinaryIO]				,     UpperCAmelCase_	:			Optional[int] = None				,     **UpperCAmelCase_	:			List[str]				,     )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			dataset
											_lowerCAmelCase  	=			path_or_buf
											_lowerCAmelCase  	=			batch_size or get_writer_batch_size(dataset.features  )
											_lowerCAmelCase  	=			parquet_writer_kwargs


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

											if isinstance(self.path_or_buf				,     (str, bytes, os.PathLike)  ):
																		with open(self.path_or_buf				,     'wb+'  ) as buffer:
																									_lowerCAmelCase  	=			self._write(file_obj=UpperCAmelCase_				,     batch_size=UpperCAmelCase_				,     **self.parquet_writer_kwargs  )
											else:
																		_lowerCAmelCase  	=			self._write(file_obj=self.path_or_buf				,     batch_size=UpperCAmelCase_				,     **self.parquet_writer_kwargs  )
											return written







				def 				__lowerCamelCase	(					self	:			Tuple				,     UpperCAmelCase_	:			BinaryIO				,     UpperCAmelCase_	:			int				,     **UpperCAmelCase_	:			int  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			0
											_lowerCAmelCase  	=			parquet_writer_kwargs.pop('path_or_buf'				,     UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.dataset.features.arrow_schema

											_lowerCAmelCase  	=			pq.ParquetWriter(UpperCAmelCase_				,     schema=UpperCAmelCase_				,     **UpperCAmelCase_  )

											for offset in logging.tqdm(
											    range(0				,     len(self.dataset  )				,     UpperCAmelCase_  )				,     unit='ba'				,     disable=not logging.is_progress_bar_enabled()				,     desc='Creating parquet from Arrow format'				,     ):
																		_lowerCAmelCase  	=			query_table(
																		    table=self.dataset._data				,     key=slice(UpperCAmelCase_				,     offset + batch_size  )				,     indices=self.dataset._indices if self.dataset._indices is not None else None				,     )
																		writer.write_table(UpperCAmelCase_  )
																		written += batch.nbytes
											writer.close()
											return written








"""simple docstring"""



from pathlib import Path

import numpy as np
from PIL import Image






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							return (gray > 127) & (gray <= 255)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray     ,  SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase  	=			np.zeros_like(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

							# Copy image to padded image
							_lowerCAmelCase  	=			image

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


if __name__ == "__main__":
	# read original image
	_snake_case          =						Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
	_snake_case          =						np.array(Image.open(lena_path))
	# kernel to be applied
	_snake_case          =						np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
	_snake_case          =						dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
	# Save the output image
	_snake_case          =						Image.fromarray(output).convert('''RGB''')
	pil_img.save('''result_dilation.png''')






