
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			datasets.utils.logging.get_logger(__name__)







@dataclass
class  UpperCamelCase__		(    datasets.BuilderConfig   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				10_000
	_lowerCAmelCase							       =				None
	_lowerCAmelCase							       =				None







class  UpperCamelCase__		(    datasets.ArrowBasedBuilder   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				ParquetConfig





	def   __snake_case				(			self		):
					return datasets.DatasetInfo(features=self.config.features		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					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}"		)
					A__  :	Tuple         =							dl_manager.download_and_extract(self.config.data_files		)
					if isinstance(UpperCamelCase__				,       (str, list, tuple)		):
									A__  :	Optional[int]         =							data_files
									if isinstance(UpperCamelCase__				,       UpperCamelCase__		):
													A__  :	List[str]         =							[files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									A__  :	Optional[int]         =							[dl_manager.iter_files(UpperCamelCase__		) for file in files]
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,       gen_kwargs={'''files''': files}		)]
					A__  :	Dict         =							[]
					for split_name, files in data_files.items():
									if isinstance(UpperCamelCase__				,       UpperCamelCase__		):
													A__  :	Optional[Any]         =							[files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									A__  :	int         =							[dl_manager.iter_files(UpperCamelCase__		) for file in files]
									# Infer features is they are stoed in the arrow schema
									if self.info.features is None:
													for file in itertools.chain.from_iterable(UpperCamelCase__		):
																	with open(UpperCamelCase__				,       '''rb'''		) as f:
																					A__  :	List[Any]         =							datasets.Features.from_arrow_schema(pq.read_schema(UpperCamelCase__		)		)
																	break
									splits.append(datasets.SplitGenerator(name=UpperCamelCase__				,       gen_kwargs={'''files''': files}		)		)
					return splits





	def   __snake_case				(			self				,       UpperCamelCase__		):
					if self.info.features is not None:
									# more expensive cast to support nested features with keys in a different order
									# allows str <-> int/float or str to Audio for example
									A__  :	str         =							table_cast(UpperCamelCase__				,       self.info.features.arrow_schema		)
					return pa_table





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	Dict         =							self.info.features.arrow_schema if self.info.features is not None else None
					if self.info.features is not None and self.config.columns is not None:
									if sorted(field.name for field in schema		) != sorted(self.config.columns		):
													raise ValueError(
													    F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"		)
					for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__		)		):
									with open(UpperCamelCase__				,       '''rb'''		) as f:
													A__  :	Dict         =							pq.ParquetFile(UpperCamelCase__		)
													try:
																	for batch_idx, record_batch in enumerate(
																	    parquet_file.iter_batches(batch_size=self.config.batch_size				,       columns=self.config.columns		)		):
																					A__  :	Dict         =							pa.Table.from_batches([record_batch]		)
																					# 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 F"{file_idx}_{batch_idx}", self._cast_table(UpperCamelCase__		)
													except ValueError as e:
																	logger.error(F"Failed to read file '{file}' with error {type(UpperCamelCase__		)}: {e}"		)
																	raise



from PIL import Image

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Image							,						__UpperCamelCase :						float       ) ->       Image:

				"""simple docstring"""







				def brightness(__UpperCamelCase :						int       ) -> float:
								return 1_28 + level + (c - 1_28)

				if not -2_5_5.0 <= level <= 2_5_5.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''       )
				return img.point(__UpperCamelCase       )


if __name__ == "__main__":
					# Load image
					with Image.open('image_data/lena.jpg') as img:
										# Change brightness to 100
										_SCREAMING_SNAKE_CASE    :      Dict          =			change_brightness(img, 1_0_0)
										brigt_img.save('image_data/lena_brightness.png', format='png')