'''simple docstring'''




import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  [
    {"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
    {"""dataset""": """snli""", """config_name""": """plain_text"""},
    {"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
    {"""dataset""": """wiki40b""", """config_name""": """en"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
    {"""dataset""": """natural_questions""", """config_name""": """default"""},
]

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[int]=True    )    ->    Tuple:


       """simple docstring"""







       if with_config:
              return [
                  {
                      "testcase_name": d["dataset"] + "/" + d["config_name"],
                      "dataset": d["dataset"],
                      "config_name": d["config_name"],
                  }
                  for d in DATASETS_ON_HF_GCP
              ]
       else:
              return [
                  {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
              ]

@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=snake_case__    )    )
class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						None
      __UpperCamelCase:  int						=						None




      def      _A	(       self						:				str						,					A						:				str						,					A						:				List[Any]       ):
             with TemporaryDirectory() as tmp_dir:
                    _UpperCAmelCase     :  int							      =    dataset_module_factory(A						,					cache_dir=A       )

                    _UpperCAmelCase     :  List[Any]							      =    import_main_class(dataset_module.module_path						,					dataset=A       )

                    _UpperCAmelCase     :  DatasetBuilder							      =    builder_cls(
                        cache_dir=A						,					config_name=A						,					hash=dataset_module.hash						,					)

                    _UpperCAmelCase     :  Tuple							      =    "/".join(
                        [
                            HF_GCP_BASE_URL,
                            builder_instance._relative_data_dir(with_hash=A       ).replace(os.sep						,					"/"       ),
                            config.DATASET_INFO_FILENAME,
                        ]       )
                    _UpperCAmelCase     :  Optional[Any]							      =    cached_path(A						,					cache_dir=A       )
                    self.assertTrue(os.path.exists(A       )       )

@pytest.mark.integration
def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]    )    ->    Tuple:


       """simple docstring"""







       _UpperCAmelCase     :  int							      =    tmp_path_factory.mktemp("test_hf_gcp"    ) / "test_wikipedia_simple"
       _UpperCAmelCase     :  Union[str, Any]							      =    dataset_module_factory("wikipedia"			,	cache_dir=_UpperCAmelCase    )
       _UpperCAmelCase     :  str							      =    import_main_class(dataset_module.module_path    )
       _UpperCAmelCase     :  DatasetBuilder							      =    builder_cls(
           cache_dir=_UpperCAmelCase			,	config_name="20220301.frr"			,	hash=dataset_module.hash			,	)
       # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
       _UpperCAmelCase     :  Dict							      =    None
       builder_instance.download_and_prepare()
       _UpperCAmelCase     :  List[str]							      =    builder_instance.as_dataset()
       assert ds


@pytest.mark.integration
def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[Any]    )    ->    Dict:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    dataset_module_factory("wikipedia"			,	cache_dir=_UpperCAmelCase    )
       _UpperCAmelCase     :  List[Any]							      =    import_main_class(dataset_module.module_path			,	dataset=_UpperCAmelCase    )
       _UpperCAmelCase     :  DatasetBuilder							      =    builder_cls(
           cache_dir=_UpperCAmelCase			,	config_name="20220301.frr"			,	hash=dataset_module.hash			,	)
       _UpperCAmelCase     :  Dict							      =    builder_instance.as_streaming_dataset()
       assert ds
       assert isinstance(_UpperCAmelCase			,	_UpperCAmelCase    )
       assert "train" in ds
       assert isinstance(ds["train"]			,	_UpperCAmelCase    )
       assert next(iter(ds["train"]    )    )






'''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 	__magic_name__						(    _UpperCamelCase   ):







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Union[str, "sqlalchemy.sql.Selectable"]	,_UpperCAmelCase					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]	,_UpperCAmelCase					:       Optional[Features] = None	,_UpperCAmelCase					:       str = None	,_UpperCAmelCase					:       bool = False	,**_UpperCAmelCase					:       Dict	,):
									super().__init__(features=_UpperCAmelCase	,cache_dir=_UpperCAmelCase	,keep_in_memory=_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      Tuple        =							Sql(
									    cache_dir=_UpperCAmelCase	,features=_UpperCAmelCase	,sql=_UpperCAmelCase	,con=_UpperCAmelCase	,**_UpperCAmelCase	,)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Optional[Any]        =							None
									_a     :      Dict        =							None
									_a     :      Dict        =							None
									_a     :      Optional[int]        =							None

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

									# Build dataset for splits
									_a     :      List[str]        =							self.builder.as_dataset(
									    split='train'	,verification_mode=_UpperCAmelCase	,in_memory=self.keep_in_memory		)
									return dataset

class 	__magic_name__						:







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Dataset	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       Optional[int] = None	,**_UpperCAmelCase					:       Dict	,):
									if num_proc is not None and num_proc <= 0:
											raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""		)

									_a     :      Dict        =							dataset
									_a     :      List[Any]        =							name
									_a     :      Tuple        =							con
									_a     :      Union[str, Any]        =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									_a     :      List[Any]        =							num_proc
									_a     :      Tuple        =							to_sql_kwargs







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Tuple        =							self.to_sql_kwargs.pop('sql'	,_UpperCAmelCase		)
									_a     :      str        =							self.to_sql_kwargs.pop('con'	,_UpperCAmelCase		)
									_a     :      Optional[Any]        =							self.to_sql_kwargs.pop('index'	,_UpperCAmelCase		)

									_a     :      Any        =							self._write(index=_UpperCAmelCase	,**self.to_sql_kwargs		)
									return written







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







							def 							__lowercase			(							self					:       int	,_UpperCAmelCase					:       Optional[int]	,**_UpperCAmelCase					:       List[Any]		):
									_a     :      Union[str, Any]        =							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:
											_a       ,					_a     :      List[Any]        =							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	,_UpperCAmelCase	,_UpperCAmelCase		)]	,)	,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




