


import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


snake_case__  				=			get_logger(__name__)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Dict					)							->	str:
						return TestCommand(
						    args.dataset   ,    args.name   ,    args.cache_dir   ,    args.data_dir   ,    args.all_configs   ,    args.save_info or args.save_infos   ,    args.ignore_verifications   ,    args.force_redownload   ,    args.clear_cache   ,    )



class      snake_case__	(   _A	):
				__lowerCAmelCase		=   False  # to tell pytest it's not a test class
				@staticmethod
				def 			UpperCamelCase_    (	_UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				int				=   parser.add_parser('test'  ,   help='Test dataset implementation.'   )
										test_parser.add_argument('--name'  ,   type=_UpperCAmelCase  ,   default=_UpperCAmelCase  ,   help='Dataset processing name'   )
										test_parser.add_argument(
										    '--cache_dir'  ,   type=_UpperCAmelCase  ,   default=_UpperCAmelCase  ,   help='Cache directory where the datasets are stored.'  ,   )
										test_parser.add_argument(
										    '--data_dir'  ,   type=_UpperCAmelCase  ,   default=_UpperCAmelCase  ,   help='Can be used to specify a manual directory to get the files from.'  ,   )
										test_parser.add_argument('--all_configs'  ,   action='store_true'  ,   help='Test all dataset configurations'   )
										test_parser.add_argument(
										    '--save_info'  ,   action='store_true'  ,   help='Save the dataset infos in the dataset card (README.md)'   )
										test_parser.add_argument(
										    '--ignore_verifications'  ,   action='store_true'  ,   help='Run the test without checksums and splits checks.'  ,   )
										test_parser.add_argument('--force_redownload'  ,   action='store_true'  ,   help='Force dataset redownload'   )
										test_parser.add_argument(
										    '--clear_cache'  ,   action='store_true'  ,   help='Remove downloaded files and cached datasets after each config test'  ,   )
										# aliases
										test_parser.add_argument('--save_infos'  ,   action='store_true'  ,   help='alias to save_info'   )
										test_parser.add_argument('dataset'  ,   type=_UpperCAmelCase  ,   help='Name of the dataset to download'   )
										test_parser.set_defaults(func=_test_command_factory   )
				def __init__(	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   dataset
										__magic_name__					:				List[Any]				=   name
										__magic_name__					:				str				=   cache_dir
										__magic_name__					:				Dict				=   data_dir
										__magic_name__					:				Any				=   all_configs
										__magic_name__					:				Optional[int]				=   save_infos
										__magic_name__					:				Any				=   ignore_verifications
										__magic_name__					:				Union[str, Any]				=   force_redownload
										__magic_name__					:				str				=   clear_cache
										if clear_cache and not cache_dir:
																print(
																    'When --clear_cache is used, specifying a cache directory is mandatory.\n'
																    'The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'
																    'Please provide a --cache_dir that will be used to test the dataset script.'   )
																exit(1   )
										if save_infos:
																__magic_name__					:				Any				=   True





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										fl_logger().setLevel(_UpperCAmelCase   )
										if self._name is not None and self._all_configs:
																print('Both parameters `config` and `all_configs` can\'t be used at once.'   )
																exit(1   )
										__magic_name__		,			__magic_name__					:				List[str]				=   self._dataset, self._name
										__magic_name__					:				Dict				=   dataset_module_factory(_UpperCAmelCase   )
										__magic_name__					:				Optional[int]				=   import_main_class(module.module_path   )
										__magic_name__					:				str				=   len(builder_cls.BUILDER_CONFIGS   ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

										def get_builders() -> Generator[DatasetBuilder, None, None]:
																if self._all_configs and builder_cls.BUILDER_CONFIGS:
																						for i, config in enumerate(builder_cls.BUILDER_CONFIGS   ):
																												if "config_name" in module.builder_kwargs:
																																		yield builder_cls(
																																		    cache_dir=self._cache_dir  ,   data_dir=self._data_dir  ,   **module.builder_kwargs  ,   )
																												else:
																																		yield builder_cls(
																																		    config_name=config.name  ,   cache_dir=self._cache_dir  ,   data_dir=self._data_dir  ,   **module.builder_kwargs  ,   )
																else:
																						if "config_name" in module.builder_kwargs:
																												yield builder_cls(cache_dir=self._cache_dir  ,   data_dir=self._data_dir  ,   **module.builder_kwargs   )
																						else:
																												yield builder_cls(
																												    config_name=_UpperCAmelCase  ,   cache_dir=self._cache_dir  ,   data_dir=self._data_dir  ,   **module.builder_kwargs  ,   )

										for j, builder in enumerate(get_builders()   ):
																print(F"Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"   )
																__magic_name__					:				List[str]				=   os.path.exists(
																    os.path.join(builder.get_imported_module_dir()  ,   datasets.config.DATASETDICT_INFOS_FILENAME   )   )  # record checksums only if we need to update a (deprecated) dataset_infos.json
																builder.download_and_prepare(
																    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
																    if not self._force_redownload
																    else DownloadMode.FORCE_REDOWNLOAD  ,   verification_mode=VerificationMode.NO_CHECKS
																    if self._ignore_verifications
																    else VerificationMode.ALL_CHECKS  ,   try_from_hf_gcs=_UpperCAmelCase  ,   )
																builder.as_dataset()
																if self._save_infos:
																						builder._save_infos()

																# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
																# The dataset_infos are saved in the YAML part of the README.md

																# Let's move it to the original directory of the dataset script, to allow the user to
																# upload them on S3 at the same time afterwards.
																if self._save_infos:
																						__magic_name__					:				List[Any]				=   os.path.join(builder_cls.get_imported_module_dir()  ,   'README.md'   )
																						__magic_name__					:				List[str]				=   Path(_UpperCAmelCase   ).name + '.py'
																						__magic_name__					:				List[str]				=   os.path.join(_UpperCAmelCase  ,   _UpperCAmelCase   )
																						if os.path.isfile(_UpperCAmelCase   ):
																												__magic_name__					:				Optional[int]				=   os.path.dirname(_UpperCAmelCase   )
																						elif os.path.isfile(_UpperCAmelCase   ):
																												__magic_name__					:				List[Any]				=   path
																						elif os.path.isdir(_UpperCAmelCase   ):  # for local directories containing only data files
																												__magic_name__					:				Tuple				=   path
																						else:  # in case of a remote dataset
																												__magic_name__					:				Dict				=   None
																												print(F"Dataset card saved at {dataset_readme_path}"   )

																						# Move dataset_info back to the user
																						if dataset_dir is not None:
																												__magic_name__					:				Union[str, Any]				=   os.path.join(_UpperCAmelCase  ,   'README.md'   )
																												copyfile(_UpperCAmelCase  ,   _UpperCAmelCase   )
																												print(F"Dataset card saved at {user_dataset_readme_path}"   )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
																if self._clear_cache:
																						if os.path.isdir(builder._cache_dir   ):
																												logger.warning(F"Clearing cache at {builder._cache_dir}"   )
																												rmtree(builder._cache_dir   )
																						__magic_name__					:				Any				=   os.path.join(self._cache_dir  ,   datasets.config.DOWNLOADED_DATASETS_DIR   )
																						if os.path.isdir(_UpperCAmelCase   ):
																												logger.warning(F"Clearing cache at {download_dir}"   )
																												rmtree(_UpperCAmelCase   )

										print('Test successful.'   )






import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
			import sqlitea

			import sqlalchemy


snake_case__  				=			datasets.utils.logging.get_logger(__name__)



@dataclass
class      snake_case__	(   datasets.BuilderConfig	):
				__lowerCAmelCase		=   None
				__lowerCAmelCase		=   None
				__lowerCAmelCase		=   None
				__lowerCAmelCase		=   True
				__lowerCAmelCase		=   None
				__lowerCAmelCase		=   None
				__lowerCAmelCase		=   None
				__lowerCAmelCase		=   10_000
				__lowerCAmelCase		=   None
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										if self.sql is None:
																raise ValueError('sql must be specified'   )
										if self.con is None:
																raise ValueError('con must be specified'   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None  ,   ):




										"""simple docstring"""




										__magic_name__					:				Optional[int]				=   config_kwargs.copy()
										# We need to stringify the Selectable object to make its hash deterministic

										# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
										__magic_name__					:				Union[str, Any]				=   config_kwargs['sql']
										if not isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																						import sqlalchemy

																						if isinstance(_UpperCAmelCase  ,   sqlalchemy.sql.Selectable   ):
																												__magic_name__					:				List[Any]				=   sqlalchemy.create_engine(config_kwargs['con'].split('://'   )[0] + '://'   )
																												__magic_name__					:				Any				=   str(sql.compile(dialect=engine.dialect   )   )
																												__magic_name__					:				str				=   sql_str
																						else:
																												raise TypeError(
																												    F"Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(_UpperCAmelCase   )}: {sql}"   )
																else:
																						raise TypeError(
																						    F"Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(_UpperCAmelCase   )}: {sql}"   )
										__magic_name__					:				List[Any]				=   config_kwargs['con']
										if not isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																__magic_name__					:				Dict				=   id(_UpperCAmelCase   )
																logger.info(
																    F"SQL connection 'con' of type {type(_UpperCAmelCase   )} couldn't be hashed properly. To enable hashing, specify 'con' as URI string instead."   )

										return super().create_config_id(_UpperCAmelCase  ,   custom_features=_UpperCAmelCase   )





				@property
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				str				=   {
										    'index_col': self.index_col,
										    'columns': self.columns,
										    'params': self.params,
										    'coerce_float': self.coerce_float,
										    'parse_dates': self.parse_dates,
										}
										return pd_read_sql_kwargs






class      snake_case__	(   datasets.ArrowBasedBuilder	):
				__lowerCAmelCase		=   SqlConfig
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return datasets.DatasetInfo(features=self.config.features   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,   gen_kwargs={}   )]
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										if self.config.features is not None:
																__magic_name__					:				List[Any]				=   self.config.features.arrow_schema
																if all(not require_storage_cast(_UpperCAmelCase   ) for feature in self.config.features.values()   ):
																						# cheaper cast
																						__magic_name__					:				Dict				=   pa.Table.from_arrays([pa_table[field.name] for field in schema]  ,   schema=_UpperCAmelCase   )
																else:
																						# more expensive cast; allows str <-> int/float or str to Audio for example
																						__magic_name__					:				List[Any]				=   table_cast(_UpperCAmelCase  ,   _UpperCAmelCase   )
										return pa_table





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				int				=   self.config.chunksize
										__magic_name__					:				Tuple				=   pd.read_sql(
										    self.config.sql  ,   self.config.con  ,   chunksize=_UpperCAmelCase  ,   **self.config.pd_read_sql_kwargs   )
										__magic_name__					:				Dict				=   [sql_reader] if chunksize is None else sql_reader
										for chunk_idx, df in enumerate(_UpperCAmelCase   ):
																__magic_name__					:				Optional[Any]				=   pa.Table.from_pandas(_UpperCAmelCase   )
																yield chunk_idx, self._cast_table(_UpperCAmelCase   )


