import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


class _lowercase       (							_UpperCAmelCase    ):





							"""simple docstring"""




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







														'''simple docstring'''






														super().__init__(
														    __lowerCAmelCase  , split=__lowerCAmelCase  , features=__lowerCAmelCase  , cache_dir=__lowerCAmelCase  , keep_in_memory=__lowerCAmelCase  , streaming=__lowerCAmelCase  , num_proc=__lowerCAmelCase  , **__lowerCAmelCase  , )
														_lowercase					=						field
														_lowercase					=						path_or_paths if isinstance(__lowerCAmelCase  , __lowerCAmelCase	) else {self.split: path_or_paths}
														_lowercase					=						Json(
														    cache_dir=__lowerCAmelCase  , data_files=__lowerCAmelCase  , features=__lowerCAmelCase  , field=__lowerCAmelCase  , **__lowerCAmelCase  , )



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






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

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






class _lowercase       :





							"""simple docstring"""




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







														'''simple docstring'''






														if num_proc is not None and num_proc <= 0:
																					raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''	)

														_lowercase					=						dataset
														_lowercase					=						path_or_buf
														_lowercase					=						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
														_lowercase					=						num_proc
														_lowercase					=						'''utf-8'''
														_lowercase					=						to_json_kwargs



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						self.to_json_kwargs.pop("""path_or_buf"""  , __lowerCAmelCase	)
														_lowercase					=						self.to_json_kwargs.pop("""orient"""  , """records"""	)
														_lowercase					=						self.to_json_kwargs.pop("""lines"""  , True if orient == """records""" else False	)
														_lowercase					=						self.to_json_kwargs.pop("""index"""  , False if orient in ["""split""", """table"""] else True	)
														_lowercase					=						self.to_json_kwargs.pop("""compression"""  , __lowerCAmelCase	)

														if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																					raise NotImplementedError(F'''`datasets` currently does not support {compression} compression'''	)

														if isinstance(self.path_or_buf  , (str, bytes, os.PathLike)	):
																					with fsspec.open(self.path_or_buf  , """wb"""  , compression=__lowerCAmelCase	) as buffer:
																												_lowercase					=						self._write(file_obj=__lowerCAmelCase  , orient=__lowerCAmelCase  , lines=__lowerCAmelCase  , index=__lowerCAmelCase  , **self.to_json_kwargs	)
														else:
																					if compression:
																												raise NotImplementedError(
																												    F'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
																												    """ was passed. Please provide a local path instead."""	)
																					_lowercase					=						self._write(
																					    file_obj=self.path_or_buf  , orient=__lowerCAmelCase  , lines=__lowerCAmelCase  , index=__lowerCAmelCase  , **self.to_json_kwargs	)
														return written



							def      _UpperCAmelCase							(     self  , UpperCAmelCase	):







														'''simple docstring'''






														_lowercase					=						args

														_lowercase					=						query_table(
														    table=self.dataset.data  , key=slice(__lowerCAmelCase  , offset + self.batch_size	)  , indices=self.dataset._indices  , )
														_lowercase					=						batch.to_pandas().to_json(
														    path_or_buf=__lowerCAmelCase  , orient=__lowerCAmelCase  , lines=__lowerCAmelCase  , index=__lowerCAmelCase  , **__lowerCAmelCase	)
														if not json_str.endswith("""\n"""	):
																					json_str += "\n"
														return json_str.encode(self.encoding	)



							def      _UpperCAmelCase							(     self  , UpperCAmelCase  , UpperCAmelCase  , UpperCAmelCase  , UpperCAmelCase  , **UpperCAmelCase  , ):







														'''simple docstring'''






														_lowercase					=						0

														if self.num_proc is None or self.num_proc == 1:
																					for offset in logging.tqdm(
																					    range(0  , len(self.dataset	)  , self.batch_size	)  , unit="""ba"""  , disable=not logging.is_progress_bar_enabled()  , desc="""Creating json from Arrow format"""  , ):
																												_lowercase					=						self._batch_json((offset, orient, lines, index, to_json_kwargs)	)
																												written += file_obj.write(__lowerCAmelCase	)
														else:
																					_lowercase					=						len(self.dataset	), self.batch_size
																					with multiprocessing.Pool(self.num_proc	) as pool:
																												for json_str in logging.tqdm(
																												    pool.imap(
																												        self._batch_json  , [(offset, orient, lines, index, to_json_kwargs) for offset in range(0  , __lowerCAmelCase  , __lowerCAmelCase	)]  , )  , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size  , unit="""ba"""  , disable=not logging.is_progress_bar_enabled()  , desc="""Creating json from Arrow format"""  , ):
																																			written += file_obj.write(__lowerCAmelCase	)

														return written










import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				assert isinstance(snake_case,	snake_case			)
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize('''keep_in_memory''',	[False, True]			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   tmp_path / '''cache'''
				__magic_name__		:int   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__		:Optional[Any]   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case,	keep_in_memory=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)

@pytest.mark.parametrize(
    '''features''',	[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ],	)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   tmp_path / '''cache'''
				__magic_name__		:int   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:Tuple   =   features.copy() if features else default_expected_features
				__magic_name__		:Union[str, Any]   =   (
				    Features({feature: Value(snake_case			) for feature, dtype in features.items()}			) if features is not None else None
				)
				__magic_name__		:int   =   ParquetDatasetReader(snake_case,	features=snake_case,	cache_dir=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)

@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''			), '''train''', '''test''']			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   tmp_path / '''cache'''
				__magic_name__		:List[Any]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:int   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case,	split=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)
				assert dataset.split == split if split else "train"

@pytest.mark.parametrize('''path_type''',	[str, list]			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				if issubclass(snake_case,	snake_case			):
								__magic_name__		:Union[str, Any]   =   parquet_path
				elif issubclass(snake_case,	snake_case			):
								__magic_name__		:Union[str, Any]   =   [parquet_path]
				__magic_name__		:Optional[int]   =   tmp_path / '''cache'''
				__magic_name__		:Optional[int]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:str   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)

def   __lowercase       (			snake_case,	snake_case,	snake_case=("train",)			):







				"""simple docstring"""






				assert isinstance(snake_case,	snake_case			)
				for split in splits:
								__magic_name__		:Optional[Any]   =   dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 3
								assert dataset.column_names == ["col_1", "col_2", "col_3"]
								for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize('''keep_in_memory''',	[False, True]			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Any   =   tmp_path / '''cache'''
				__magic_name__		:Optional[int]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__		:Tuple   =   ParquetDatasetReader(
								    {'''train''': parquet_path},	cache_dir=snake_case,	keep_in_memory=snake_case			).read()
				_check_parquet_datasetdict(snake_case,	snake_case			)

@pytest.mark.parametrize(
    '''features''',	[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ],	)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   tmp_path / '''cache'''
				__magic_name__		:Dict   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:int   =   features.copy() if features else default_expected_features
				__magic_name__		:List[Any]   =   (
				    Features({feature: Value(snake_case			) for feature, dtype in features.items()}			) if features is not None else None
				)
				__magic_name__		:Optional[int]   =   ParquetDatasetReader({'''train''': parquet_path},	features=snake_case,	cache_dir=snake_case			).read()
				_check_parquet_datasetdict(snake_case,	snake_case			)

@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''			), '''train''', '''test''']			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				if split:
								__magic_name__		:Dict   =   {split: parquet_path}
				else:
								__magic_name__		:Optional[int]   =   '''train'''
								__magic_name__		:Dict   =   {'''train''': parquet_path, '''test''': parquet_path}
				__magic_name__		:List[Any]   =   tmp_path / '''cache'''
				__magic_name__		:Optional[Any]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:Optional[Any]   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case			).read()
				_check_parquet_datasetdict(snake_case,	snake_case,	splits=list(path.keys()			)			)
				assert all(dataset[split].split == split for split in path.keys()			)

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   ParquetDatasetWriter(snake_case,	tmp_path / '''foo.parquet'''			)
				assert writer.write() > 0
				__magic_name__		:List[Any]   =   pq.ParquetFile(tmp_path / '''foo.parquet'''			)
				__magic_name__		:List[Any]   =   pf.read()
				assert dataset.data.table == output_table

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   str(shared_datadir / '''test_image_rgb.jpg'''			)
				__magic_name__		:Tuple   =   {'''image''': [image_path]}
				__magic_name__		:List[Any]   =   Features({'''image''': Image()}			)
				__magic_name__		:Tuple   =   Dataset.from_dict(snake_case,	features=snake_case			)
				__magic_name__		:Union[str, Any]   =   ParquetDatasetWriter(snake_case,	tmp_path / '''foo.parquet'''			)
				assert writer.write() > 0

				__magic_name__		:List[str]   =   Dataset.from_parquet(str(tmp_path / '''foo.parquet'''			)			)
				assert dataset.features == reloaded_dataset.features

				__magic_name__		:List[str]   =   ParquetDatasetReader(str(tmp_path / '''foo.parquet'''			),	streaming=snake_case			).read()
				assert dataset.features == reloaded_iterable_dataset.features

@pytest.mark.parametrize(
    '''feature, expected''',	[
        (Features({'''foo''': Value('''int32'''			)}			), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''			)}			), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()			)}			), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ],	)
def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				assert get_writer_batch_size(snake_case			) == expected






