
'''simple docstring'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


lowerCAmelCase					:    Optional[Any]           = re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')


@total_ordering
@dataclass
class 			SCREAMING_SNAKE_CASE__      :
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							None
				lowerCAmelCase_     			=							None
				lowerCAmelCase_     			=							None
				lowerCAmelCase_     			=							None







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     _str_to_version_tuple(self.version_str     )







				def __repr__(       self     )->		Any:







											'''simple docstring'''






											return F'''{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'''







				@property
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											return self.major, self.minor, self.patch







				def 						UpperCAmelCase_    (       self						,				A_     )->		Union[str, Any]:







											'''simple docstring'''






											if isinstance(A_						,				A_     ):
																		return Version(A_     )
											elif isinstance(A_						,				A_     ):
																		return other
											raise TypeError(F'''{other} (type {type(A_     )}) cannot be compared to version.'''     )







				def __eq__(       self						,				A_     )->		Tuple:







											'''simple docstring'''






											try:
																		UpperCamelCase   			=     self._validate_operand(A_     )
											except (TypeError, ValueError):
																		return False
											else:
																		return self.tuple == other.tuple







				def __lt__(       self						,				A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     self._validate_operand(A_     )
											return self.tuple < other.tuple







				def __hash__(       self     )->		int:







											'''simple docstring'''






											return hash(_version_tuple_to_str(self.tuple     )     )







				@classmethod
				def 						UpperCAmelCase_    (       cls						,				A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     {f.name for f in dataclasses.fields(cls     )}
											return cls(**{k: v for k, v in dic.items() if k in field_names}     )







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											return self.version_str







def 		A_(     A						:		List[str]):
							UpperCamelCase   			=     _VERSION_REG.match(A)
							if not res:
														raise ValueError(f'''Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.''')
							return tuple(int(A) for v in [res.group('major'), res.group('minor'), res.group('patch')])







def 		A_(     A						:		Dict):
							return ".".join(str(A) for v in version_tuple)






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			)		->					Optional[int]:

		"""simple docstring"""






		_UpperCamelCase         =						np.inf

		def set_batch_size(lowerCAmelCase			) -> None:
				nonlocal batch_size
				if isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS			)
				elif isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS			)
				elif isinstance(lowerCAmelCase    ,	lowerCAmelCase			) and feature.dtype == "binary":
						_UpperCamelCase         =						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__   ( __lowercase						):

						def __init__(      self							,				a							,				a = None							,				a = None							,				a = None							,				a = False							,				a = False							,				a = None							,				**a							,				)       ->   Tuple:


								'''simple docstring'''

								super().__init__(
								    a							,				split=a							,				features=a							,				cache_dir=a							,				keep_in_memory=a							,				streaming=a							,				num_proc=a							,				**a							,				)
								_UpperCamelCase         =						path_or_paths if isinstance(a							,				a					) else {self.split: path_or_paths}
								_UpperCamelCase         =						_PACKAGED_DATASETS_MODULES["""parquet"""][1]
								_UpperCamelCase         =						Parquet(
								    cache_dir=a							,				data_files=a							,				features=a							,				hash=a							,				**a							,				)







						def 			A_					(      self					)       ->   Optional[int]:


								'''simple docstring'''

								if self.streaming:
										_UpperCamelCase         =						self.builder.as_streaming_dataset(split=self.split					)
								# Build regular (map-style) dataset
								else:
										_UpperCamelCase         =						None
										_UpperCamelCase         =						None
										_UpperCamelCase         =						None
										_UpperCamelCase         =						None

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







class 							lowerCAmelCase__   :

						def __init__(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   str:


								'''simple docstring'''

								_UpperCamelCase         =						dataset
								_UpperCamelCase         =						path_or_buf
								_UpperCamelCase         =						batch_size or get_writer_batch_size(dataset.features					)
								_UpperCamelCase         =						parquet_writer_kwargs

						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						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:
												_UpperCamelCase         =						self._write(file_obj=a							,				batch_size=a							,				**self.parquet_writer_kwargs					)
								else:
										_UpperCamelCase         =						self._write(file_obj=self.path_or_buf							,				batch_size=a							,				**self.parquet_writer_kwargs					)
								return written







						def 			A_					(      self							,				a							,				a							,				**a					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						0
								_UpperCamelCase         =						parquet_writer_kwargs.pop("""path_or_buf"""							,				a					)
								_UpperCamelCase         =						self.dataset.features.arrow_schema

								_UpperCamelCase         =						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"""							,				):
										_UpperCamelCase         =						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



