

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 							__SCREAMING_SNAKE_CASE     (   a__    :   Features       )					->				Optional[int]:
	__A :			List[Any]		    =			np.inf

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

	_visit(a__			,a__       )

	return None if batch_size is np.inf else batch_size






class      lowerCamelCase_      (	snake_case_	):




	def __init__(     self						:     int					,      __A						:     Dict					,      __A						:     int = None					,      __A						:     List[Any] = None					,      __A						:     Optional[int] = None					,      __A						:     Optional[int] = False					,      __A						:     Optional[int] = False					,      __A						:     Tuple = None					,      **__A						:     Union[str, Any]					,      ):
		super().__init__(
		    A_					,      split=A_					,      features=A_					,      cache_dir=A_					,      keep_in_memory=A_					,      streaming=A_					,      num_proc=A_					,      **A_					,      )
		__A :			Union[str, Any]		    =			path_or_paths if isinstance(A_					,      A_							) else {self.split: path_or_paths}
		__A :			List[Any]		    =			_PACKAGED_DATASETS_MODULES["""parquet"""][1]
		__A :			Dict		    =			Parquet(
		    cache_dir=A_					,      data_files=A_					,      features=A_					,      hash=A_					,      **A_					,      )




	def        lowerCAmelCase_       (     self						:     str							):






		if self.streaming:
			__A :			Dict		    =			self.builder.as_streaming_dataset(split=self.split							)
		# Build regular (map-style) dataset
		else:
			__A :			int		    =			None
			__A :			int		    =			None
			__A :			Tuple		    =			None
			__A :			List[Any]		    =			None

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






class      lowerCamelCase_      :




	def __init__(     self						:     str					,      __A						:     int					,      __A						:     Optional[int]					,      __A						:     Union[str, Any] = None					,      **__A						:     List[Any]					,      ):
		__A :			Optional[Any]		    =			dataset
		__A :			str		    =			path_or_buf
		__A :			List[str]		    =			batch_size or get_writer_batch_size(dataset.features							)
		__A :			Dict		    =			parquet_writer_kwargs




	def        lowerCAmelCase_       (     self						:     List[str]							):
		__A :			List[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:
				__A :			List[str]		    =			self._write(file_obj=A_					,      batch_size=A_					,      **self.parquet_writer_kwargs							)
		else:
			__A :			Optional[int]		    =			self._write(file_obj=self.path_or_buf					,      batch_size=A_					,      **self.parquet_writer_kwargs							)
		return written




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     List[str]					,      __A						:     Union[str, Any]					,      **__A						:     List[str]							):
		__A :			str		    =			0
		__A :			Dict		    =			parquet_writer_kwargs.pop("""path_or_buf"""					,      A_							)
		__A :			List[str]		    =			self.dataset.features.arrow_schema

		__A :			List[str]		    =			pq.ParquetWriter(A_					,      schema=A_					,      **A_							)

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







'''simple docstring'''
import numpy as np







def 		A_(     A						:		str		,     A						:		Optional[Any]		,     A						:		Tuple		,     A						:		Optional[int]		,     A						:		str):
							UpperCamelCase   			=     int(np.ceil((x_end - xa) / h))
							UpperCamelCase   			=     np.zeros((n + 1,))
							UpperCamelCase   			=     ya
							UpperCamelCase   			=     xa

							for k in range(A):
														UpperCamelCase   			=     f(A		,     y[k])
														UpperCamelCase   			=     f(x + 0.5 * h		,     y[k] + 0.5 * h * ka)
														UpperCamelCase   			=     f(x + 0.5 * h		,     y[k] + 0.5 * h * ka)
														UpperCamelCase   			=     f(x + h		,     y[k] + h * ka)
														UpperCamelCase   			=     y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
														x += h

							return y


if __name__ == "__main__":
						import doctest

						doctest.testmod()




