






"""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	(			_A							):




					def __init__(    self					:						int   ,						A					:						NestedDataStructureLike[PathLike]   ,						A					:						Optional[NamedSplit] = None   ,						A					:						Optional[Features] = None   ,						A					:						str = None   ,						A					:						bool = False   ,						A					:						bool = False   ,						A					:						Optional[int] = None   ,						**A					:						List[str]   ,						)  ->  Optional[Any]:
							super().__init__(
							    A   ,						split=A   ,						features=A   ,						cache_dir=A   ,						keep_in_memory=A   ,						streaming=A   ,						num_proc=A   ,						**A   ,						)
							lowercase_			:			Optional[Any]								=       path_or_paths if isinstance(A   ,						A       ) else {self.split: path_or_paths}
							lowercase_			:			Tuple								=       Csv(
							    cache_dir=A   ,						data_files=A   ,						features=A   ,						**A   ,						)






					def   A  (    self					:						Tuple       )  ->  List[str]:
							# Build iterable dataset
							if self.streaming:
									lowercase_			:			str								=       self.builder.as_streaming_dataset(split=self.split       )
							# Build regular (map-style) dataset
							else:
									lowercase_			:			str								=       None
									lowercase_			:			Dict								=       None
									lowercase_			:			int								=       None
									lowercase_			:			Tuple								=       None

									self.builder.download_and_prepare(
									    download_config=A   ,						download_mode=A   ,						verification_mode=A   ,						base_path=A   ,						num_proc=self.num_proc   ,						)
									lowercase_			:			str								=       self.builder.as_dataset(
									    split=self.split   ,						verification_mode=A   ,						in_memory=self.keep_in_memory       )
							return dataset





class  _UpperCAmelCase	:




					def __init__(    self					:						Union[str, Any]   ,						A					:						Dataset   ,						A					:						Union[PathLike, BinaryIO]   ,						A					:						Optional[int] = None   ,						A					:						Optional[int] = None   ,						**A					:						Optional[int]   ,						)  ->  Tuple:
							if num_proc is not None and num_proc <= 0:
									raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''       )

							lowercase_			:			int								=       dataset
							lowercase_			:			List[Any]								=       path_or_buf
							lowercase_			:			str								=       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							lowercase_			:			Optional[int]								=       num_proc
							lowercase_			:			str								=       '''utf-8'''
							lowercase_			:			int								=       to_csv_kwargs




					def   A  (    self					:						List[Any]       )  ->  int:
							lowercase_			:			Tuple								=       self.to_csv_kwargs.pop('''path_or_buf'''   ,						A       )
							lowercase_			:			List[Any]								=       self.to_csv_kwargs.pop('''header'''   ,						A       )
							lowercase_			:			List[str]								=       self.to_csv_kwargs.pop('''index'''   ,						A       )

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




					def   A  (    self					:						List[Any]   ,						A					:						Optional[int]       )  ->  Any:
							lowercase_	, lowercase_	, lowercase_	, lowercase_			:			List[Any]								=       args

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






					def   A  (    self					:						Optional[int]   ,						A					:						BinaryIO   ,						A					:						Optional[int]   ,						A					:						str   ,						**A					:						Union[str, Any]       )  ->  int:
							lowercase_			:			List[Any]								=       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'''   ,						):
											lowercase_			:			Any								=       self._batch_csv((offset, header, index, to_csv_kwargs)       )
											written += file_obj.write(A       )

							else:
									lowercase_	, lowercase_			:			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   ,						A   ,						A       )]   ,						)   ,						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(A       )

							return written











"""simple docstring"""



import math
import os

import cva
import numpy as np

__A		:	int      =		255.0



def 						lowercase   (    __snake_case    :			float		, __snake_case    :			float     ):
		lowercase_			:			Any								=       np.mean((original - contrast) ** 2     )
		if mse == 0:
				return 1_0_0

		return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__snake_case     )     )



def 						lowercase   (    ):
		lowercase_			:			Any								=       os.path.dirname(os.path.realpath(__snake_case     )     )
		# Loading images (original image and compressed image)
		lowercase_			:			Union[str, Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/original_image.png'''     )     )
		lowercase_			:			List[Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/compressed_image.png'''     )		, 1     )

		lowercase_			:			Tuple								=       cva.imread(os.path.join(__snake_case		, '''image_data/PSNR-example-base.png'''     )     )
		lowercase_			:			List[str]								=       cva.imread(
		    os.path.join(__snake_case		, '''image_data/PSNR-example-comp-10.jpg'''     )		, 1     )

		# Value expected: 29.73dB
		print('''-- First Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )

		# # Value expected: 31.53dB (Wikipedia Example)
		print('''\n-- Second Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )


if __name__ == "__main__":
						main()



