



import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy





def 							A	(     _UpperCAmelCase				:   Optional[int]							,   _UpperCAmelCase				:   Optional[int]    )				-> str:







			'''simple docstring'''
			assert isinstance(_UpperCAmelCase							,   _UpperCAmelCase    )
			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





@require_sqlalchemy
@pytest.mark.parametrize('keep_in_memory'							,   [False, True]    )
def 							A	(     _UpperCAmelCase				:   Union[str, Any]							,   _UpperCAmelCase				:   Union[str, Any]							,   _UpperCAmelCase				:   Union[str, Any]							,   _UpperCAmelCase				:   List[Any]    )				-> Any:







			'''simple docstring'''
			_UpperCAmelCase		       =   tmp_path / '''cache'''
			_UpperCAmelCase		       =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						_UpperCAmelCase		       =   SqlDatasetReader(
						    'dataset'							,   'sqlite:///' + sqlite_path							,   cache_dir=_UpperCAmelCase							,   keep_in_memory=_UpperCAmelCase    ).read()
			_check_sql_dataset(_UpperCAmelCase							,   _UpperCAmelCase    )





@require_sqlalchemy
@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 							A	(     _UpperCAmelCase				:   Union[str, Any]							,   _UpperCAmelCase				:   Union[str, Any]							,   _UpperCAmelCase				:   Optional[Any]							,   _UpperCAmelCase				:   Dict    )				-> Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase		       =   tmp_path / '''cache'''
			_UpperCAmelCase		       =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			_UpperCAmelCase		       =   features.copy() if features else default_expected_features
			_UpperCAmelCase		       =   (
			    Features({feature: Value(_UpperCAmelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
			)
			_UpperCAmelCase		       =   SqlDatasetReader('dataset'							,   'sqlite:///' + sqlite_path							,   features=_UpperCAmelCase							,   cache_dir=_UpperCAmelCase    ).read()
			_check_sql_dataset(_UpperCAmelCase							,   _UpperCAmelCase    )





def 							A	(     _UpperCAmelCase				:   Optional[Any]    )				-> Dict:







			'''simple docstring'''
			with contextlib.closing(sqlitea.connect(_UpperCAmelCase    )    ) as con:
						_UpperCAmelCase		       =   con.cursor()
						cur.execute('SELECT * FROM dataset'    )
						for row in cur:
									yield row





@require_sqlalchemy
def 							A	(     _UpperCAmelCase				:   Any							,   _UpperCAmelCase				:   Dict							,   _UpperCAmelCase				:   int    )				-> Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase		       =   tmp_path / '''cache'''
			_UpperCAmelCase		       =   os.path.join(_UpperCAmelCase							,   'tmp.sql'    )
			_UpperCAmelCase		       =   SqlDatasetReader('dataset'							,   'sqlite:///' + sqlite_path							,   cache_dir=_UpperCAmelCase    ).read()
			SqlDatasetWriter(_UpperCAmelCase							,   'dataset'							,   'sqlite:///' + output_sqlite_path							,   num_proc=1    ).write()

			_UpperCAmelCase		       =   iter_sql_file(_UpperCAmelCase    )
			_UpperCAmelCase		       =   iter_sql_file(_UpperCAmelCase    )

			for rowa, rowa in zip(_UpperCAmelCase							,   _UpperCAmelCase    ):
						assert rowa == rowa





@require_sqlalchemy
def 							A	(     _UpperCAmelCase				:   Optional[int]							,   _UpperCAmelCase				:   Tuple							,   _UpperCAmelCase				:   List[Any]    )				-> Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase		       =   tmp_path / '''cache'''
			_UpperCAmelCase		       =   os.path.join(_UpperCAmelCase							,   'tmp.sql'    )
			_UpperCAmelCase		       =   SqlDatasetReader('dataset'							,   'sqlite:///' + sqlite_path							,   cache_dir=_UpperCAmelCase    ).read()
			SqlDatasetWriter(_UpperCAmelCase							,   'dataset'							,   'sqlite:///' + output_sqlite_path							,   num_proc=2    ).write()

			_UpperCAmelCase		       =   iter_sql_file(_UpperCAmelCase    )
			_UpperCAmelCase		       =   iter_sql_file(_UpperCAmelCase    )

			for rowa, rowa in zip(_UpperCAmelCase							,   _UpperCAmelCase    ):
						assert rowa == rowa





@require_sqlalchemy
def 							A	(     _UpperCAmelCase				:   Optional[Any]							,   _UpperCAmelCase				:   int							,   _UpperCAmelCase				:   str    )				-> Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase		       =   tmp_path / '''cache'''
			_UpperCAmelCase		       =   os.path.join(_UpperCAmelCase							,   'tmp.sql'    )
			_UpperCAmelCase		       =   SqlDatasetReader('dataset'							,   'sqlite:///' + sqlite_path							,   cache_dir=_UpperCAmelCase    ).read()
			with pytest.raises(_UpperCAmelCase    ):
						SqlDatasetWriter(_UpperCAmelCase							,   'dataset'							,   'sqlite:///' + output_sqlite_path							,   num_proc=0    ).write()

'''simple docstring'''

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__a: Optional[int]          =   4
__a: Optional[Any]          =   3



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       pass


def 			__UpperCamelCase       (   UpperCAmelCase     ):
 for shard in shards:
  for i in range(UpperCAmelCase     ):
   yield {"i": i, "shard": shard}
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					int(os.environ['''RANK''']     )
 lowercase__				:    List[str]  =					int(os.environ['''WORLD_SIZE''']     )

 lowercase__				:    Optional[Any]  =					ArgumentParser()
 parser.add_argument('''--streaming'''		,     type=UpperCAmelCase     )
 parser.add_argument('''--local_rank'''		,     type=UpperCAmelCase     )
 parser.add_argument('''--num_workers'''		,     type=UpperCAmelCase		,     default=0     )
 lowercase__				:    List[Any]  =					parser.parse_args()
 lowercase__				:    List[str]  =					args.streaming
 lowercase__				:    str  =					args.num_workers

 lowercase__				:    Optional[int]  =					{'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(UpperCAmelCase     )]}
 lowercase__				:    Tuple  =					IterableDataset.from_generator(UpperCAmelCase		,     gen_kwargs=UpperCAmelCase     )
 if not streaming:
  lowercase__				:    int  =					Dataset.from_list(list(UpperCAmelCase     )     )

 lowercase__				:    str  =					split_dataset_by_node(UpperCAmelCase		,     rank=UpperCAmelCase		,     world_size=UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					torch.utils.data.DataLoader(UpperCAmelCase		,     num_workers=UpperCAmelCase     )

 lowercase__				:    Dict  =					NUM_SHARDS * NUM_ITEMS_PER_SHARD
 lowercase__				:    Optional[Any]  =					full_size // world_size
 expected_local_size += int(rank < (full_size % world_size)     )

 lowercase__				:    Union[str, Any]  =					sum(1 for _ in dataloader     )
 if local_size != expected_local_size:
  raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
  main()

