

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




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




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

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	iter_sql_file(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	iter_sql_file(__lowerCAmelCase							)

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




@require_sqlalchemy
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Any:
      SCREAMING_SNAKE_CASE__   :      Any      		=	tmp_path / """cache"""
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	os.path.join(__lowerCAmelCase  ,					"""tmp.sql"""							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	SqlDatasetReader("""dataset"""  ,					"""sqlite:///""" + sqlite_path  ,					cache_dir=__lowerCAmelCase							).read()
      SqlDatasetWriter(__lowerCAmelCase  ,					"""dataset"""  ,					"""sqlite:///""" + output_sqlite_path  ,					num_proc=2							).write()

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	iter_sql_file(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	iter_sql_file(__lowerCAmelCase							)

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




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


import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
  import sqlitea

  import sqlalchemy

class      __a							(UpperCamelCase_):






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a = None	,    _a = None	,    _a = False	,    **_a	,    )  ->      Union[str, Any]:


           """simple docstring"""


           super().__init__(features=_a	,    cache_dir=_a	,    keep_in_memory=_a	,    **_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	Sql(
               cache_dir=_a	,    features=_a	,    sql=_a	,    con=_a	,    **_a	,    )





     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	None
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	None
           SCREAMING_SNAKE_CASE__   :      Dict      		=	None
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	None

           self.builder.download_and_prepare(
               download_config=_a	,    download_mode=_a	,    verification_mode=_a	,    base_path=_a	,    )

           # Build dataset for splits
           SCREAMING_SNAKE_CASE__   :      str      		=	self.builder.as_dataset(
               split="""train"""	,    verification_mode=_a	,    in_memory=self.keep_in_memory  )
           return dataset





class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a	,    _a = None	,    _a = None	,    **_a	,    )  ->      Any:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__   :      int      		=	dataset
           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	con
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           SCREAMING_SNAKE_CASE__   :      int      		=	num_proc
           SCREAMING_SNAKE_CASE__   :      int      		=	to_sql_kwargs

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.to_sql_kwargs.pop("""sql"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.to_sql_kwargs.pop("""con"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.to_sql_kwargs.pop("""index"""	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self._write(index=_a	,    **self.to_sql_kwargs  )
           return written

     def 					_a			(						self	,    _a  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	args
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
           SCREAMING_SNAKE_CASE__   :      Any      		=	query_table(
               table=self.dataset.data	,    key=slice(_a	,    offset + self.batch_size  )	,    indices=self.dataset._indices	,    )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	batch.to_pandas()
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	df.to_sql(self.name	,    self.con	,    index=_a	,    **_a  )
           return num_rows or len(_a  )





     def 					_a			(						self	,    _a	,    **_a  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	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 SQL from Arrow format"""	,    ):
                       written += self._batch_sql((offset, index, to_sql_kwargs)  )
           else:
                 SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      str      		=	len(self.dataset  ), self.batch_size
                 with multiprocessing.Pool(self.num_proc  ) as pool:
                       for num_rows in logging.tqdm(
                           pool.imap(
                               self._batch_sql	,    [(offset, index, to_sql_kwargs) for offset in range(0	,    _a	,    _a  )]	,    )	,    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 SQL from Arrow format"""	,    ):
                             written += num_rows

           return written




