




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


lowerCamelCase				:		Union[str, Any]     =   datasets.utils.logging.get_logger(__name__)







@dataclass
class   A(						datasets.BuilderConfig   ):



 '''simple docstring'''


 UpperCamelCase						   =						1_0000
 UpperCamelCase						   =						None
 UpperCamelCase						   =						None







class   A(						datasets.ArrowBasedBuilder   ):



 '''simple docstring'''


 UpperCamelCase						   =						ParquetConfig

 def 				a__ (				self					:  Optional[int]  )   ->	Union[str, Any]:

       """simple docstring"""






       return datasets.DatasetInfo(features=self.config.features  )

 def 				a__ (				self					:  int						,							A_					:  List[Any]  )   ->	str:

       """simple docstring"""






       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(A_						,							(str, list, tuple)  ):
             lowerCamelCase_		     =    data_files
             if isinstance(A_						,							A_  ):
                   lowerCamelCase_		     =    [files]
             # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
             lowerCamelCase_		     =    [dl_manager.iter_files(A_  ) 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(A_						,							A_  ):
                   lowerCamelCase_		     =    [files]
             # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
             lowerCamelCase_		     =    [dl_manager.iter_files(A_  ) 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(A_  ):
                         with open(A_						,							'rb'  ) as f:
                               lowerCamelCase_		     =    datasets.Features.from_arrow_schema(pq.read_schema(A_  )  )
                         break
             splits.append(datasets.SplitGenerator(name=A_						,							gen_kwargs={'files': files}  )  )
       return splits

 def 				a__ (				self					:  int						,							A_					:  Dict  )   ->	pa.Table:

       """simple docstring"""






       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(A_						,							self.info.features.arrow_schema  )
       return pa_table





 def 				a__ (				self					:  Optional[Any]						,							A_					:  Optional[Any]  )   ->	Optional[Any]:

       """simple docstring"""






       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(A_  )  ):
             with open(A_						,							'rb'  ) as f:
                   lowerCamelCase_		     =    pq.ParquetFile(A_  )
                   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(A_  )
                   except ValueError as e:
                         logger.error(f"""Failed to read file \'{file}\' with error {type(A_  )}: {e}"""  )
                         raise





def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		if index == number_of_items:
				return 0
		UpperCamelCase		=     0
		UpperCamelCase		=     0
		UpperCamelCase		=     knapsack(lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       index + 1 )
		if weights[index] <= max_weight:
				UpperCamelCase		=     values[index] + knapsack(
				    lowercase    ,       lowercase    ,       lowercase    ,       max_weight - weights[index]    ,       index + 1 )
		return max(lowercase    ,       lowercase )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
