


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


snake_case				:    List[Any]       				=     datasets.utils.logging.get_logger(__name__)







@dataclass
class 		_snake_case  (      datasets.BuilderConfig							):
 SCREAMING_SNAKE_CASE__      =       1_0000
 SCREAMING_SNAKE_CASE__      =       None
 SCREAMING_SNAKE_CASE__      =       None







class 		_snake_case  (      datasets.ArrowBasedBuilder							):
 SCREAMING_SNAKE_CASE__      =       ParquetConfig




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return datasets.DatasetInfo(features=self.config.features    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       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}'''    )
       a						:Union[str, Any] 							=       dl_manager.download_and_extract(self.config.data_files    )
       if isinstance(_lowerCamelCase					,  (str, list, tuple)    ):
             a						:Dict 							=       data_files
             if isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
                   a						:List[Any] 							=       [files]
             # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
             a						:List[Any] 							=       [dl_manager.iter_files(_lowerCamelCase    ) for file in files]
             return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,  gen_kwargs={'''files''': files}    )]
       a						:List[str] 							=       []
       for split_name, files in data_files.items():
             if isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
                   a						:Dict 							=       [files]
             # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
             a						:Any 							=       [dl_manager.iter_files(_lowerCamelCase    ) 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(_lowerCamelCase    ):
                         with open(_lowerCamelCase					,  '''rb'''    ) as f:
                               a						:int 							=       datasets.Features.from_arrow_schema(pq.read_schema(_lowerCamelCase    )    )
                         break
             splits.append(datasets.SplitGenerator(name=_lowerCamelCase					,  gen_kwargs={'''files''': files}    )    )
       return splits




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       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
             a						:Dict 							=       table_cast(_lowerCamelCase					,  self.info.features.arrow_schema    )
       return pa_table




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:str 							=       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(_lowerCamelCase    )    ):
             with open(_lowerCamelCase					,  '''rb'''    ) as f:
                   a						:str 							=       pq.ParquetFile(_lowerCamelCase    )
                   try:
                         for batch_idx, record_batch in enumerate(
                             parquet_file.iter_batches(batch_size=self.config.batch_size					,  columns=self.config.columns    )    ):
                               a						:int 							=       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(_lowerCamelCase    )
                   except ValueError as e:
                         logger.error(F'''Failed to read file \'{file}\' with error {type(_lowerCamelCase    )}: {e}'''    )
                         raise









'''simple docstring'''

import math





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  math.loga(math.sqrt(4 * positive_integer + 1		) / 2 + 1 / 2		)

 return exponent == int(UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						float = 1 / 12345		):
 lowerCamelCase_      =  0
 lowerCamelCase_      =  0

 lowerCamelCase_      =  3
 while True:
  lowerCamelCase_      =  (integer**2 - 1) / 4
  # if candidate is an integer, then there is a partition for k
  if partition_candidate == int(UpperCAmelCase_		):
   lowerCamelCase_      =  int(UpperCAmelCase_		)
   total_partitions += 1
   if check_partition_perfect(UpperCAmelCase_		):
    perfect_partitions += 1
  if perfect_partitions > 0:
   if perfect_partitions / total_partitions < max_proportion:
    return int(UpperCAmelCase_		)
  integer += 1


if __name__ == "__main__":
     print(f'''{solution() = }''')



