





'''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


_lowercase       						=			datasets.utils.logging.get_logger(__name__)
@dataclass
class 			_lowercase						(       datasets.BuilderConfig					):
     _UpperCAmelCase										=					10_000
     _UpperCAmelCase										=					None
     _UpperCAmelCase										=					None




class 			_lowercase						(       datasets.ArrowBasedBuilder					):
     _UpperCAmelCase										=					ParquetConfig







     def     UpperCamelCase			( self    ) ->	Tuple:
      return datasets.DatasetInfo(features=self.config.features    )







     def     UpperCamelCase			( self    ,   A__    ) ->	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}"""    )
      snake_case				=			dl_manager.download_and_extract(self.config.data_files    )
      if isinstance(A__    ,   (str, list, tuple)    ):
       snake_case				=			data_files
       if isinstance(A__    ,   A__    ):
        snake_case				=			[files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       snake_case				=			[dl_manager.iter_files(A__    ) 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(A__    ,   A__    ):
        snake_case				=			[files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       snake_case				=			[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:
          snake_case				=			datasets.Features.from_arrow_schema(pq.read_schema(A__    )    )
         break
       splits.append(datasets.SplitGenerator(name=A__    ,   gen_kwargs={'''files''': files}    )    )
      return splits







     def     UpperCamelCase			( self    ,   A__    ) ->	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
       snake_case				=			table_cast(A__    ,   self.info.features.arrow_schema    )
      return pa_table







     def     UpperCamelCase			( self    ,   A__    ) ->	Optional[int]:
      snake_case				=			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:
        snake_case				=			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    )    ):
          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(A__    )
        except ValueError as e:
         logger.error(F"""Failed to read file '{file}' with error {type(A__    )}: {e}"""    )
         raise








'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	None:
      snake_case				=			value
      snake_case				=			None
      snake_case				=			None




class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	None:
      snake_case				=			tree







     def     UpperCamelCase			( self    ,   A__    ) ->	int:
      if node is None:
       return 0
      return node.value + (
          self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
      )







     def __iter__( self    ) ->	Iterator[int]:
      yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

