



"""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       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: Dict					):
							assert isinstance(_lowerCamelCase  ,       _lowerCamelCase					)
							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       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: int					):
							__a		:		Tuple									=       tmp_path / """cache"""
							__a		:		Dict									=       {"""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		:		Dict									=       SqlDatasetReader(
														    """dataset"""  ,       """sqlite:///""" + sqlite_path  ,       cache_dir=_lowerCamelCase  ,       keep_in_memory=_lowerCamelCase					).read()
							_check_sql_dataset(_lowerCamelCase  ,       _lowerCamelCase					)





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





def       __magic_name__    (   _lowerCamelCase				: List[str]					):
							with contextlib.closing(sqlitea.connect(_lowerCamelCase					)					) as con:
														__a		:		str									=       con.cursor()
														cur.execute("""SELECT * FROM dataset"""					)
														for row in cur:
																					yield row





@require_sqlalchemy
def       __magic_name__    (   _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Union[str, Any]					):
							__a		:		Optional[int]									=       tmp_path / """cache"""
							__a		:		Optional[Any]									=       os.path.join(_lowerCamelCase  ,       """tmp.sql"""					)
							__a		:		int									=       SqlDatasetReader("""dataset"""  ,       """sqlite:///""" + sqlite_path  ,       cache_dir=_lowerCamelCase					).read()
							SqlDatasetWriter(_lowerCamelCase  ,       """dataset"""  ,       """sqlite:///""" + output_sqlite_path  ,       num_proc=1					).write()

							__a		:		Union[str, Any]									=       iter_sql_file(_lowerCamelCase					)
							__a		:		Optional[int]									=       iter_sql_file(_lowerCamelCase					)

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





@require_sqlalchemy
def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: Tuple					):
							__a		:		Any									=       tmp_path / """cache"""
							__a		:		Any									=       os.path.join(_lowerCamelCase  ,       """tmp.sql"""					)
							__a		:		Optional[Any]									=       SqlDatasetReader("""dataset"""  ,       """sqlite:///""" + sqlite_path  ,       cache_dir=_lowerCamelCase					).read()
							SqlDatasetWriter(_lowerCamelCase  ,       """dataset"""  ,       """sqlite:///""" + output_sqlite_path  ,       num_proc=2					).write()

							__a		:		Dict									=       iter_sql_file(_lowerCamelCase					)
							__a		:		Optional[Any]									=       iter_sql_file(_lowerCamelCase					)

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







@require_sqlalchemy
def       __magic_name__    (   _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: Dict					):
							__a		:		Dict									=       tmp_path / """cache"""
							__a		:		int									=       os.path.join(_lowerCamelCase  ,       """tmp.sql"""					)
							__a		:		str									=       SqlDatasetReader("""dataset"""  ,       """sqlite:///""" + sqlite_path  ,       cache_dir=_lowerCamelCase					).read()
							with pytest.raises(_lowerCamelCase					):
														SqlDatasetWriter(_lowerCamelCase  ,       """dataset"""  ,       """sqlite:///""" + output_sqlite_path  ,       num_proc=0					).write()






"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42


if is_transformers_available() and is_torch_available():
	from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline

