
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


__A				=datasets.utils.logging.get_logger(__name__)




@dataclass
class     _SCREAMING_SNAKE_CASE     (      datasets.BuilderConfig						):
	lowerCAmelCase__         = 1_00_00
	lowerCAmelCase__         = None
	lowerCAmelCase__         = None




class     _SCREAMING_SNAKE_CASE     (      datasets.ArrowBasedBuilder						):
	lowerCAmelCase__         = ParquetConfig



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					return datasets.DatasetInfo(features=self.config.features )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Tuple:
					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_						=			dl_manager.download_and_extract(self.config.data_files )
					if isinstance(lowercase						,  (str, list, tuple) ):
									lowerCamelCase_						=			data_files
									if isinstance(lowercase						,  lowercase ):
													lowerCamelCase_						=			[files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									lowerCamelCase_						=			[dl_manager.iter_files(lowercase ) for file in files]
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,  gen_kwargs={"files": files} )]
					lowerCamelCase_						=			[]
					for split_name, files in data_files.items():
									if isinstance(lowercase						,  lowercase ):
													lowerCamelCase_						=			[files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									lowerCamelCase_						=			[dl_manager.iter_files(lowercase ) 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(lowercase ):
																	with open(lowercase						,  "rb" ) as f:
																					lowerCamelCase_						=			datasets.Features.from_arrow_schema(pq.read_schema(lowercase ) )
																	break
									splits.append(datasets.SplitGenerator(name=lowercase						,  gen_kwargs={"files": files} ) )
					return splits



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       pa.Table:
					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
									lowerCamelCase_						=			table_cast(lowercase						,  self.info.features.arrow_schema )
					return pa_table



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Dict:
					lowerCamelCase_						=			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(lowercase ) ):
									with open(lowercase						,  "rb" ) as f:
													lowerCamelCase_						=			pq.ParquetFile(lowercase )
													try:
																	for batch_idx, record_batch in enumerate(
																	    parquet_file.iter_batches(batch_size=self.config.batch_size						,  columns=self.config.columns ) ):
																					lowerCamelCase_						=			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(lowercase )
													except ValueError as e:
																	logger.error(f'Failed to read file \'{file}\' with error {type(lowercase )}: {e}' )
																	raise








from __future__ import annotations







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       tuple[float, list[float]]:



       """simple docstring"""

       A__       =    list(range(len(lowercase_						)						)						)
       A__       =    [v / w for v, w in zip(lowercase_ ,				lowercase_						)]
       index.sort(key=lambda lowercase_						: ratio[i] ,				reverse=lowercase_						)

       A__       =    0
       A__       =    [0] * len(lowercase_						)
       for i in index:
              if weight[i] <= capacity:
                     A__       =    1
                     max_value += value[i]
                     capacity -= weight[i]
              else:
                     A__       =    capacity / weight[i]
                     max_value += value[i] * capacity / weight[i]
                     break

       return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()


