





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 		lowerCAmelCase  (	UpperCamelCase__   :   Features					)      ->    Optional[int]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   np.inf

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






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = False			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(
										    _lowerCAmelCase			, split=_lowerCAmelCase			, features=_lowerCAmelCase			, cache_dir=_lowerCAmelCase			, keep_in_memory=_lowerCAmelCase			, streaming=_lowerCAmelCase			, num_proc=_lowerCAmelCase			, **_lowerCAmelCase			, )
										__SCREAMING_SNAKE_CASE:				List[Any]           =   path_or_paths if isinstance(_lowerCAmelCase			, _lowerCAmelCase					) else {self.split: path_or_paths}
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   _PACKAGED_DATASETS_MODULES['''parquet'''][1]
										__SCREAMING_SNAKE_CASE:				List[Any]           =   Parquet(
										    cache_dir=_lowerCAmelCase			, data_files=_lowerCAmelCase			, features=_lowerCAmelCase			, hash=_lowerCAmelCase			, **_lowerCAmelCase			, )






				def 			snake_case_    (  self					):

										"""simple docstring"""




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

																self.builder.download_and_prepare(
																    download_config=_lowerCAmelCase			, download_mode=_lowerCAmelCase			, verification_mode=_lowerCAmelCase			, base_path=_lowerCAmelCase			, num_proc=self.num_proc			, )
																__SCREAMING_SNAKE_CASE:				Tuple           =   self.builder.as_dataset(
																    split=self.split			, verification_mode=_lowerCAmelCase			, in_memory=self.keep_in_memory					)
										return dataset





class 				a			:






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   dataset
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   path_or_buf
										__SCREAMING_SNAKE_CASE:				List[Any]           =   batch_size or get_writer_batch_size(dataset.features					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   parquet_writer_kwargs






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				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:
																						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self._write(file_obj=_lowerCAmelCase			, batch_size=_lowerCAmelCase			, **self.parquet_writer_kwargs					)
										else:
																__SCREAMING_SNAKE_CASE:				int           =   self._write(file_obj=self.path_or_buf			, batch_size=_lowerCAmelCase			, **self.parquet_writer_kwargs					)
										return written






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   0
										__SCREAMING_SNAKE_CASE:				Dict           =   parquet_writer_kwargs.pop('''path_or_buf'''			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   self.dataset.features.arrow_schema

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   pq.ParquetWriter(_lowerCAmelCase			, schema=_lowerCAmelCase			, **_lowerCAmelCase					)

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










def 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]       ,			UpperCamelCase__   :   Dict					)      ->    List[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   [0 for i in range(r + 1					)]
						# nc0 = 1
						__SCREAMING_SNAKE_CASE:				Dict           =   1
						for i in range(1       ,			n + 1					):
												# to compute current row from previous row.
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   min(UpperCamelCase__       ,			UpperCamelCase__					)
												while j > 0:
																		c[j] += c[j - 1]
																		j -= 1
						return c[r]




print(binomial_coefficient(n=10, r=5))



