


'''simple docstring'''



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      UpperCAmelCase__   (      UpperCAmelCase_       :							Features				)	->						Optional[int]:
     __lowerCamelCase  :      int														=						np.inf

     def set_batch_size(UpperCAmelCase_       :							FeatureType				) -> None:
          nonlocal batch_size
          if isinstance(UpperCAmelCase_ ,       UpperCAmelCase_				):
               __lowerCamelCase  :      Union[str, Any]														=						min(UpperCAmelCase_ ,       config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS				)
          elif isinstance(UpperCAmelCase_ ,       UpperCAmelCase_				):
               __lowerCamelCase  :      List[Any]														=						min(UpperCAmelCase_ ,       config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS				)
          elif isinstance(UpperCAmelCase_ ,       UpperCAmelCase_				) and feature.dtype == "binary":
               __lowerCamelCase  :      Optional[Any]														=						min(UpperCAmelCase_ ,       config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS				)

     _visit(UpperCAmelCase_ ,       UpperCAmelCase_				)

     return None if batch_size is np.inf else batch_size





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	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  :      Any														=						path_or_paths if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) else {self.split: path_or_paths}
         __lowerCamelCase  :      List[Any]														=						_PACKAGED_DATASETS_MODULES['parquet'][1]
         __lowerCamelCase  :      Optional[int]														=						Parquet(
             cache_dir=SCREAMING_SNAKE_CASE_     ,	data_files=SCREAMING_SNAKE_CASE_     ,	features=SCREAMING_SNAKE_CASE_     ,	hash=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_     ,	)







    def  lowercase_							(				self      )							->	int:
         # Build iterable dataset
         if self.streaming:
              __lowerCamelCase  :      Any														=						self.builder.as_streaming_dataset(split=self.split      )
         # Build regular (map-style) dataset
         else:
              __lowerCamelCase  :      str														=						None
              __lowerCamelCase  :      int														=						None
              __lowerCamelCase  :      Optional[Any]														=						None
              __lowerCamelCase  :      Any														=						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  :      List[str]														=						self.builder.as_dataset(
                  split=self.split     ,	verification_mode=SCREAMING_SNAKE_CASE_     ,	in_memory=self.keep_in_memory      )
         return dataset







class   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Tuple:
         __lowerCamelCase  :      Tuple														=						dataset
         __lowerCamelCase  :      List[Any]														=						path_or_buf
         __lowerCamelCase  :      Tuple														=						batch_size or get_writer_batch_size(dataset.features      )
         __lowerCamelCase  :      Any														=						parquet_writer_kwargs







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Union[str, Any]														=						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  :      Union[str, Any]														=						self._write(file_obj=SCREAMING_SNAKE_CASE_     ,	batch_size=SCREAMING_SNAKE_CASE_     ,	**self.parquet_writer_kwargs      )
         else:
              __lowerCamelCase  :      Optional[int]														=						self._write(file_obj=self.path_or_buf     ,	batch_size=SCREAMING_SNAKE_CASE_     ,	**self.parquet_writer_kwargs      )
         return written







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	int:
         __lowerCamelCase  :      List[Any]														=						0
         __lowerCamelCase  :      Optional[int]														=						parquet_writer_kwargs.pop('path_or_buf'     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						self.dataset.features.arrow_schema

         __lowerCamelCase  :      Any														=						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  :      Optional[Any]														=						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










'''simple docstring'''



from __future__ import annotations

A__						:   int				=    10



def      UpperCAmelCase__   (      UpperCAmelCase_       :							list[int]				)	->						list[int]:
     __lowerCamelCase  :      List[Any]														=						1
     __lowerCamelCase  :      Any														=						max(UpperCAmelCase_				)
     while placement <= max_digit:
          # declare and initialize empty buckets
          __lowerCamelCase  :      list[list]														=						[[] for _ in range(UpperCAmelCase_				)]
          # split list_of_ints between the buckets
          for i in list_of_ints:
               __lowerCamelCase  :      List[Any]														=						int((i / placement) % RADIX				)
               buckets[tmp].append(UpperCAmelCase_				)
          # put each buckets' contents into list_of_ints
          __lowerCamelCase  :      Tuple														=						0
          for b in range(UpperCAmelCase_				):
               for i in buckets[b]:
                    __lowerCamelCase  :      List[Any]														=						i
                    a += 1
        # move to next
          placement *= RADIX
     return list_of_ints


if __name__ == "__main__":
      import doctest

      doctest.testmod()






