

import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs


@pytest.mark.parametrize(
    '''kwargs, expected'''  ,		[
        ({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
        ({'''num_shards''': 10, '''max_num_jobs''': 1}, [range(10       )]),
        ({'''num_shards''': 10, '''max_num_jobs''': 10}, [range(SCREAMING_SNAKE_CASE__  ,		i + 1       ) for i in range(10       )]),
        ({'''num_shards''': 1, '''max_num_jobs''': 10}, [range(1       )]),
        ({'''num_shards''': 10, '''max_num_jobs''': 3}, [range(0  ,		4       ), range(4  ,		7       ), range(7  ,		10       )]),
        ({'''num_shards''': 3, '''max_num_jobs''': 10}, [range(0  ,		1       ), range(1  ,		2       ), range(2  ,		3       )]),
    ]  ,		)
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	_distribute_shards(**SCREAMING_SNAKE_CASE__       )
						assert out == expected


@pytest.mark.parametrize(
    '''gen_kwargs, max_num_jobs, expected'''  ,		[
        ({'''foo''': 0}, 10, [{'''foo''': 0}]),
        ({'''shards''': [0, 1, 2, 3]}, 1, [{'''shards''': [0, 1, 2, 3]}]),
        ({'''shards''': [0, 1, 2, 3]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}, {'''shards''': [2]}, {'''shards''': [3]}]),
        ({'''shards''': [0, 1]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}]),
        ({'''shards''': [0, 1, 2, 3]}, 2, [{'''shards''': [0, 1]}, {'''shards''': [2, 3]}]),
    ]  ,		)
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	_split_gen_kwargs(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
						assert out == expected


@pytest.mark.parametrize(
    '''gen_kwargs, expected'''  ,		[
        ({'''foo''': 0}, 1),
        ({'''shards''': [0]}, 1),
        ({'''shards''': [0, 1, 2, 3]}, 4),
        ({'''shards''': [0, 1, 2, 3], '''foo''': 0}, 4),
        ({'''shards''': [0, 1, 2, 3], '''other''': (0, 1)}, 4),
        ({'''shards''': [0, 1, 2, 3], '''shards2''': [0, 1]}, RuntimeError),
    ]  ,		)
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						if expected is RuntimeError:
												with pytest.raises(SCREAMING_SNAKE_CASE__       ):
																		_number_of_shards_in_gen_kwargs(SCREAMING_SNAKE_CASE__       )
						else:
												snake_case_   =	_number_of_shards_in_gen_kwargs(SCREAMING_SNAKE_CASE__       )
												assert out == expected


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				pyspark.sql.DataFrame							,   _UpperCamelCase :				Optional[NamedSplit] = None							,   _UpperCamelCase :				Optional[Features] = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = "arrow"							,   **_UpperCamelCase :				Tuple							,   )      ->str:
									super().__init__(
									    split=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   keep_in_memory=_UpperCamelCase							,   streaming=_UpperCamelCase							,   **_UpperCamelCase							,   )
									snake_case_   =	load_from_cache_file
									snake_case_   =	file_format
									snake_case_   =	Spark(
									    df=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   working_dir=_UpperCamelCase							,   **_UpperCamelCase							,   )







			def 				snake_case__(     self :				int							)      ->Tuple:
									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split							)
									snake_case_   =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_UpperCamelCase							,   file_format=self._file_format							,   )
									return self.builder.as_dataset(split=self.split							)