






"""simple docstring"""



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      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					int      )		->Dict:
						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      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					Optional[Any]      )		->List[Any]:
						A__						:  Dict		  =  tmp_path / """cache"""
						A__						:  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():
												A__						:  str		  =  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      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					List[str]      )		->List[Any]:
						A__						:  int		  =  tmp_path / """cache"""
						A__						:  str		  =  {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						A__						:  Dict		  =  features.copy() if features else default_expected_features
						A__						:  Tuple		  =  (
						    Features({feature: Value(UpperCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
						)
						A__						:  Optional[int]		  =  SqlDatasetReader("""dataset""",						"""sqlite:///""" + sqlite_path,						features=UpperCAmelCase__,						cache_dir=UpperCAmelCase__      ).read()
						_check_sql_dataset(UpperCAmelCase__,						UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Any      )		->List[str]:
						with contextlib.closing(sqlitea.connect(UpperCAmelCase__      )      ) as con:
												A__						:  Union[str, Any]		  =  con.cursor()
												cur.execute("""SELECT * FROM dataset"""      )
												for row in cur:
																		yield row




@require_sqlalchemy
def      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Optional[int]      )		->Tuple:
						A__						:  Union[str, Any]		  =  tmp_path / """cache"""
						A__						:  Tuple		  =  os.path.join(UpperCAmelCase__,						"""tmp.sql"""      )
						A__						:  Dict		  =  SqlDatasetReader("""dataset""",						"""sqlite:///""" + sqlite_path,						cache_dir=UpperCAmelCase__      ).read()
						SqlDatasetWriter(UpperCAmelCase__,						"""dataset""",						"""sqlite:///""" + output_sqlite_path,						num_proc=1      ).write()

						A__						:  Union[str, Any]		  =  iter_sql_file(UpperCAmelCase__      )
						A__						:  Optional[int]		  =  iter_sql_file(UpperCAmelCase__      )

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




@require_sqlalchemy
def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Optional[Any]      )		->Optional[int]:
						A__						:  Optional[Any]		  =  tmp_path / """cache"""
						A__						:  Dict		  =  os.path.join(UpperCAmelCase__,						"""tmp.sql"""      )
						A__						:  Dict		  =  SqlDatasetReader("""dataset""",						"""sqlite:///""" + sqlite_path,						cache_dir=UpperCAmelCase__      ).read()
						SqlDatasetWriter(UpperCAmelCase__,						"""dataset""",						"""sqlite:///""" + output_sqlite_path,						num_proc=2      ).write()

						A__						:  Any		  =  iter_sql_file(UpperCAmelCase__      )
						A__						:  Dict		  =  iter_sql_file(UpperCAmelCase__      )

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




@require_sqlalchemy
def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Optional[Any]      )		->Tuple:
						A__						:  int		  =  tmp_path / """cache"""
						A__						:  int		  =  os.path.join(UpperCAmelCase__,						"""tmp.sql"""      )
						A__						:  Union[str, Any]		  =  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"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    =							{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    =							[
						    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''SEWForCTC''',
						    '''SEWForSequenceClassification''',
						    '''SEWModel''',
						    '''SEWPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_sew import (
												    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SEWForCTC,
												    SEWForSequenceClassification,
												    SEWModel,
												    SEWPreTrainedModel,
												)


else:
						import sys

						A_    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






