



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


UpperCAmelCase				:							str								=datasets.utils.logging.get_logger(__name__)


@dataclass
class  _lowercase				(datasets.BuilderConfig ):
				'''simple docstring'''







				lowercase__				  =  None
				lowercase__				  =  None
				lowercase__				  =  None
				lowercase__				  =  True
				lowercase__				  =  None
				lowercase__				  =  None
				lowercase__				  =  None
				lowercase__				  =  1_00_00
				lowercase__				  =  None

				def  _lowerCamelCase   (	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  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__ = None       ,	):

									'''simple docstring'''


									UpperCamelCase_											=				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
									UpperCamelCase_											=				config_kwargs["sql"]
									if not isinstance(snake_case__       ,	snake_case__      ):
														if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																			import sqlalchemy

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

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

				@property
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				{
									    "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  _lowercase				(datasets.ArrowBasedBuilder ):
				'''simple docstring'''







				lowercase__				  =  SqlConfig

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									return datasets.DatasetInfo(features=self.config.features      )

				def  _lowerCamelCase   (	self       ,	snake_case__      ):

									'''simple docstring'''


									return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,	gen_kwargs={}      )]

				def  _lowerCamelCase   (	self       ,	snake_case__      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.config.chunksize
									UpperCamelCase_											=				pd.read_sql(
									    self.config.sql       ,	self.config.con       ,	chunksize=snake_case__       ,	**self.config.pd_read_sql_kwargs      )
									UpperCamelCase_											=				[sql_reader] if chunksize is None else sql_reader
									for chunk_idx, df in enumerate(snake_case__      ):
														UpperCamelCase_											=				pa.Table.from_pandas(snake_case__      )
														yield chunk_idx, self._cast_table(snake_case__      )





from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments



def 				_lowerCAmelCase      ():
					UpperCamelCase_											=				HfArgumentParser(_lowerCAmelCase)
					try:
										UpperCamelCase_											=				parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										UpperCamelCase_											=				"Arg --no_{0} is no longer used, please use --no-{0} instead."
										UpperCamelCase_											=				" ".join(str(_lowerCAmelCase).split(" ")[:-1])
										UpperCamelCase_											=				""
										UpperCamelCase_											=				eval(str(_lowerCAmelCase).split(" ")[-1])
										UpperCamelCase_											=				[]
										for arg in depreciated_args:
															# arg[2:] removes '--'
															if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																				# arg[5:] removes '--no_'
																				full_error_msg += arg_error_msg.format(arg[5:])
															else:
																				wrong_args.append(_lowerCAmelCase)
										if len(_lowerCAmelCase) > 0:
															UpperCamelCase_											=				full_error_msg + begin_error_msg + str(_lowerCAmelCase)
										raise ValueError(_lowerCAmelCase)

					UpperCamelCase_											=				PyTorchBenchmark(args=_lowerCAmelCase)
					benchmark.run()


if __name__ == "__main__":
	main()
