


snake_case__  				=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

snake_case__  				=			[{'type': 'code', 'content': INSTALL_CONTENT}]
snake_case__  				=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}






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   )


