




'''simple docstring'''



import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


UpperCAmelCase_				:     str     			=						datasets.utils.logging.get_logger(__name__)





@dataclass
class 			lowerCAmelCase      (       datasets.BuilderConfig):
       __lowercase     :					Optional[datasets.Features]       				=	None





class 			lowerCAmelCase      (       datasets.ArrowBasedBuilder):
       __lowercase     :					List[Any]       				=	ArrowConfig


       def 					lowerCAmelCase					(      self     )					->     str:



            '''simple docstring'''




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


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     int:



            '''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}'''     )
            __snake_case			    =     dl_manager.download_and_extract(self.config.data_files     )
            if isinstance(__SCREAMING_SNAKE_CASE			,     (str, list, tuple)     ):
                 __snake_case			    =     data_files
                 if isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     ):
                      __snake_case			    =     [files]
                 # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                 __snake_case			    =     [dl_manager.iter_files(__SCREAMING_SNAKE_CASE     ) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,     gen_kwargs={'''files''': files}     )]
            __snake_case			    =     []
            for split_name, files in data_files.items():
                 if isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     ):
                      __snake_case			    =     [files]
                 # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                 __snake_case			    =     [dl_manager.iter_files(__SCREAMING_SNAKE_CASE     ) 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(__SCREAMING_SNAKE_CASE     ):
                           with open(__SCREAMING_SNAKE_CASE			,     '''rb'''     ) as f:
                                __snake_case			    =     datasets.Features.from_arrow_schema(pa.ipc.open_stream(__SCREAMING_SNAKE_CASE     ).schema     )
                           break
                 splits.append(datasets.SplitGenerator(name=__SCREAMING_SNAKE_CASE			,     gen_kwargs={'''files''': files}     )     )
            return splits


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     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
                 __snake_case			    =     table_cast(__SCREAMING_SNAKE_CASE			,     self.info.features.arrow_schema     )
            return pa_table




       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     Dict:



            '''simple docstring'''




            for file_idx, file in enumerate(itertools.chain.from_iterable(__SCREAMING_SNAKE_CASE     )     ):
                 with open(__SCREAMING_SNAKE_CASE			,     '''rb'''     ) as f:
                      try:
                           for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__SCREAMING_SNAKE_CASE     )     ):
                                __snake_case			    =     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(__SCREAMING_SNAKE_CASE     )
                      except ValueError as e:
                           logger.error(F'''Failed to read file \'{file}\' with error {type(__SCREAMING_SNAKE_CASE     )}: {e}'''     )
                           raise












'''simple docstring'''



from __future__ import annotations







def 	_UpperCamelCase       (_lowerCamelCase							:   int      ,		_lowerCamelCase							:   list[int]      ,		_lowerCamelCase							:   list[int]      ,		_lowerCamelCase							:   int		)->      int:




     '''simple docstring'''





     if counter == 0 or capacity == 0:
          return 0

     # If weight of the nth item is more than Knapsack of capacity,
     #   then this item cannot be included in the optimal solution,
     # else return the maximum of two cases:
     #   (1) nth item included
     #   (2) not included
     if weights[counter - 1] > capacity:
          return knapsack(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
     else:
          __snake_case			    =     capacity - weights[counter - 1]
          __snake_case			    =     values[counter - 1] + knapsack(
              _lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
          __snake_case			    =     knapsack(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
          return max(_lowerCamelCase      ,		_lowerCamelCase		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






