





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 )	->						Optional[int]:







      '''simple docstring'''






      __A          =				np.inf

      def set_batch_size(lowerCamelCase:   FeatureType ) -> None:
            nonlocal batch_size
            if isinstance(lowerCamelCase					,				lowerCamelCase ):
                  __A          =				min(lowerCamelCase					,				config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
            elif isinstance(lowerCamelCase					,				lowerCamelCase ):
                  __A          =				min(lowerCamelCase					,				config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
            elif isinstance(lowerCamelCase					,				lowerCamelCase ) and feature.dtype == "binary":
                  __A          =				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 A_					(			_lowerCamelCase   ):




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






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

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





class A_					:




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




   def _lowerCAmelCase      (self	:Dict )->					int:
         __A          =				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:
                     __A          =				self._write(file_obj=_UpperCamelCase    ,   batch_size=_UpperCamelCase    ,   **self.parquet_writer_kwargs )
         else:
               __A          =				self._write(file_obj=self.path_or_buf    ,   batch_size=_UpperCamelCase    ,   **self.parquet_writer_kwargs )
         return written






   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:BinaryIO    ,   _UpperCamelCase	:int    ,   **_UpperCamelCase	:Any )->					int:
         __A          =				0
         __A          =				parquet_writer_kwargs.pop('''path_or_buf'''    ,   _UpperCamelCase )
         __A          =				self.dataset.features.arrow_schema

         __A          =				pq.ParquetWriter(_UpperCamelCase    ,   schema=_UpperCamelCase    ,   **_UpperCamelCase )

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












def _a				(	lowerCamelCase:   int = 2_00 )	->						int:







      '''simple docstring'''






      __A          =				[1, 2, 5, 10, 20, 50, 1_00, 2_00]
      __A          =				[0] * (pence + 1)
      __A          =				1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(lowerCamelCase					,				pence + 1					,				1 ):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73682





