




import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



def 							_A       (						lowerCAmelCase_					: Features      ):
     """simple docstring"""

     lowerCAmelCase__          =			np.inf

     def set_batch_size(lowerCAmelCase_					: FeatureType      ) -> None:
          nonlocal batch_size
          if isinstance(lowerCAmelCase_       ,    lowerCAmelCase_      ):
               lowerCAmelCase__          =			min(lowerCAmelCase_       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS      )
          elif isinstance(lowerCAmelCase_       ,    lowerCAmelCase_      ):
               lowerCAmelCase__          =			min(lowerCAmelCase_       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS      )
          elif isinstance(lowerCAmelCase_       ,    lowerCAmelCase_      ) and feature.dtype == "binary":
               lowerCAmelCase__          =			min(lowerCAmelCase_       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS      )

     _visit(lowerCAmelCase_       ,    lowerCAmelCase_      )

     return None if batch_size is np.inf else batch_size





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					NestedDataStructureLike[PathLike]       ,     SCREAMING_SNAKE_CASE__					:					Optional[NamedSplit] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Features] = None       ,     SCREAMING_SNAKE_CASE__					:					str = None       ,     SCREAMING_SNAKE_CASE__					:					bool = False       ,     SCREAMING_SNAKE_CASE__					:					bool = False       ,     SCREAMING_SNAKE_CASE__					:					Optional[int] = None       ,     **SCREAMING_SNAKE_CASE__					:					List[Any]       ,     )				->	Optional[int]:
         super().__init__(
             SCREAMING_SNAKE_CASE__       ,     split=SCREAMING_SNAKE_CASE__       ,     features=SCREAMING_SNAKE_CASE__       ,     cache_dir=SCREAMING_SNAKE_CASE__       ,     keep_in_memory=SCREAMING_SNAKE_CASE__       ,     streaming=SCREAMING_SNAKE_CASE__       ,     num_proc=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			path_or_paths if isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else {self.split: path_or_paths}
         lowerCAmelCase__          =			_PACKAGED_DATASETS_MODULES["parquet"][1]
         lowerCAmelCase__          =			Parquet(
             cache_dir=SCREAMING_SNAKE_CASE__       ,     data_files=SCREAMING_SNAKE_CASE__       ,     features=SCREAMING_SNAKE_CASE__       ,     hash=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )


    def        a				(	self					:					Optional[Any]       )				->	List[str]:
         # Build iterable dataset
         if self.streaming:
              lowerCAmelCase__          =			self.builder.as_streaming_dataset(split=self.split       )
         # Build regular (map-style) dataset
         else:
              lowerCAmelCase__          =			None
              lowerCAmelCase__          =			None
              lowerCAmelCase__          =			None
              lowerCAmelCase__          =			None

              self.builder.download_and_prepare(
                  download_config=SCREAMING_SNAKE_CASE__       ,     download_mode=SCREAMING_SNAKE_CASE__       ,     verification_mode=SCREAMING_SNAKE_CASE__       ,     base_path=SCREAMING_SNAKE_CASE__       ,     num_proc=self.num_proc       ,     )
              lowerCAmelCase__          =			self.builder.as_dataset(
                  split=self.split       ,     verification_mode=SCREAMING_SNAKE_CASE__       ,     in_memory=self.keep_in_memory       )
         return dataset






class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dataset       ,     SCREAMING_SNAKE_CASE__					:					Union[PathLike, BinaryIO]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int] = None       ,     **SCREAMING_SNAKE_CASE__					:					List[str]       ,     )				->	Optional[Any]:
         lowerCAmelCase__          =			dataset
         lowerCAmelCase__          =			path_or_buf
         lowerCAmelCase__          =			batch_size or get_writer_batch_size(dataset.features       )
         lowerCAmelCase__          =			parquet_writer_kwargs


    def        a				(	self					:					str       )				->	int:
         lowerCAmelCase__          =			self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf       ,     (str, bytes, os.PathLike)       ):
              with open(self.path_or_buf       ,     "wb+"       ) as buffer:
                   lowerCAmelCase__          =			self._write(file_obj=SCREAMING_SNAKE_CASE__       ,     batch_size=SCREAMING_SNAKE_CASE__       ,     **self.parquet_writer_kwargs       )
         else:
              lowerCAmelCase__          =			self._write(file_obj=self.path_or_buf       ,     batch_size=SCREAMING_SNAKE_CASE__       ,     **self.parquet_writer_kwargs       )
         return written


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					BinaryIO       ,     SCREAMING_SNAKE_CASE__					:					int       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	int:
         lowerCAmelCase__          =			0
         lowerCAmelCase__          =			parquet_writer_kwargs.pop("path_or_buf"       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.dataset.features.arrow_schema

         lowerCAmelCase__          =			pq.ParquetWriter(SCREAMING_SNAKE_CASE__       ,     schema=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )

         for offset in logging.tqdm(
             range(0       ,     len(self.dataset       )       ,     SCREAMING_SNAKE_CASE__       )       ,     unit="ba"       ,     disable=not logging.is_progress_bar_enabled()       ,     desc="Creating parquet from Arrow format"       ,     ):
              lowerCAmelCase__          =			query_table(
                  table=self.dataset._data       ,     key=slice(SCREAMING_SNAKE_CASE__       ,     offset + batch_size       )       ,     indices=self.dataset._indices if self.dataset._indices is not None else None       ,     )
              writer.write_table(SCREAMING_SNAKE_CASE__       )
              written += batch.nbytes
         writer.close()
         return written






from maths.prime_factors import prime_factors



def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     if not isinstance(lowerCAmelCase_       ,    lowerCAmelCase_      ):
          lowerCAmelCase__          =			F'Input value of [number={number}] must be an integer'
          raise TypeError(lowerCAmelCase_      )
     if number < 1:
          raise ValueError("Input must be a positive integer"      )
     return -1 if len(prime_factors(lowerCAmelCase_      )      ) % 2 else 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()
