



'''simple docstring'''





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										=     datasets.utils.logging.get_logger(__name__)






@dataclass
class 	lowerCAmelCase_							(    datasets.BuilderConfig					):
  '''simple docstring'''




  __lowercase							:				int           =				1_0_0_0_0
  __lowercase							:				Optional[List[str]]           =				None
  __lowercase							:				Optional[datasets.Features]           =				None







class 	lowerCAmelCase_							(    datasets.ArrowBasedBuilder					):
  '''simple docstring'''




  __lowercase							:				Optional[int]           =				ParquetConfig




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      return datasets.DatasetInfo(features=self.config.features				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		str:
      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__			:  List[str]											= dl_manager.download_and_extract(self.config.data_files				)
      if isinstance(__UpperCAmelCase  ,(str, list, tuple)				):
          lowerCAmelCase__			:  List[str]											= data_files
          if isinstance(__UpperCAmelCase  ,__UpperCAmelCase				):
              lowerCAmelCase__			:  List[Any]											= [files]
          # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
          lowerCAmelCase__			:  int											= [dl_manager.iter_files(__UpperCAmelCase				) for file in files]
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,gen_kwargs={"""files""": files}				)]
      lowerCAmelCase__			:  Union[str, Any]											= []
      for split_name, files in data_files.items():
          if isinstance(__UpperCAmelCase  ,__UpperCAmelCase				):
              lowerCAmelCase__			:  Optional[int]											= [files]
          # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
          lowerCAmelCase__			:  Union[str, Any]											= [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:
                      lowerCAmelCase__			:  Optional[Any]											= datasets.Features.from_arrow_schema(pq.read_schema(__UpperCAmelCase				)				)
                  break
          splits.append(datasets.SplitGenerator(name=__UpperCAmelCase  ,gen_kwargs={"""files""": files}				)				)
      return splits




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		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__			:  Any											= table_cast(__UpperCAmelCase  ,self.info.features.arrow_schema				)
      return pa_table






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		Optional[Any]:
      lowerCAmelCase__			:  List[Any]											= 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:
              lowerCAmelCase__			:  Any											= 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				)				):
                      lowerCAmelCase__			:  Any											= 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







'''simple docstring'''





def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    def count_of_possible_combinations(UpperCamelCase      ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item      ) for item in array      )

    return count_of_possible_combinations(UpperCamelCase      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase							,   UpperCamelCase      ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        lowerCAmelCase__			:  Any											= sum(
            count_of_possible_combinations_with_dp_array(target - item							,   UpperCamelCase      )
            for item in array      )
        lowerCAmelCase__			:  Tuple											= answer
        return answer

    lowerCAmelCase__			:  Optional[int]											= [-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase							,   UpperCamelCase      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  str											= [0] * (target + 1)
    lowerCAmelCase__			:  List[Any]											= 1

    for i in range(1							,   target + 1      ):
        for j in range(UpperCamelCase      ):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _lowerCAmelCase										=     3
 _lowerCAmelCase										=     5
 _lowerCAmelCase										=     [1, 2, 5]
 print(combination_sum_iv(n, array, target))


