

'''simple docstring'''


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


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



@dataclass
class        lowercase    (	datasets.BuilderConfig			):
							_lowerCamelCase		:   Union[str, "sqlalchemy.sql.Selectable"]= None
							_lowerCamelCase		:   Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]= None
							_lowerCamelCase		:   Optional[Union[str, List[str]]]= None
							_lowerCamelCase		:   bool= True
							_lowerCamelCase		:   Optional[Union[List, Tuple, Dict]]= None
							_lowerCamelCase		:   Optional[Union[List, Dict]]= None
							_lowerCamelCase		:   Optional[List[str]]= None
							_lowerCamelCase		:   Optional[int]= 10000
							_lowerCamelCase		:   Optional[datasets.Features]= None


							def     _snake_case				(      self)				->					Dict:
								if self.sql is None:
									raise ValueError('sql must be specified')
								if self.con is None:
									raise ValueError('con must be specified')


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None     ,      )				->					str:
								UpperCAmelCase_		:   Tuple													=       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_		:   Union[str, Any]													=       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_		:   Tuple													=       sqlalchemy.create_engine(config_kwargs['con'].split('://')[0] + '://')
											UpperCAmelCase_		:   int													=       str(sql.compile(dialect=engine.dialect))
											UpperCAmelCase_		:   List[Any]													=       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_		:   int													=       config_kwargs['con']
								if not isinstance(_snake_case     ,      _snake_case):
									UpperCAmelCase_		:   str													=       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     _snake_case				(      self)				->					List[str]:
								UpperCAmelCase_		:   Optional[Any]													=       {
								    '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			):
							_lowerCamelCase		:   Optional[int]= SqlConfig


							def     _snake_case				(      self)				->					Optional[Any]:
								return datasets.DatasetInfo(features=self.config.features)


							def     _snake_case				(      self     ,      _snake_case)				->					Any:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,      gen_kwargs={})]


							def     _snake_case				(      self     ,      _snake_case)				->					pa.Table:
								if self.config.features is not None:
									UpperCAmelCase_		:   str													=       self.config.features.arrow_schema
									if all(not require_storage_cast(_snake_case) for feature in self.config.features.values()):
										# cheaper cast
										UpperCAmelCase_		:   Union[str, Any]													=       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_		:   List[str]													=       table_cast(_snake_case     ,      _snake_case)
								return pa_table





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









'''simple docstring'''


from __future__ import annotations







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     int:

	# Base Case
	if counter == 0 or capacity == 0:
		return 0

	# If weight of the nth item is more than Knapsack of capacity,
	#   then this item cannot be included in the optimal solution,
	# else return the maximum of two cases:
	#   (1) nth item included
	#   (2) not included
	if weights[counter - 1] > capacity:
		return knapsack(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,counter - 1	)
	else:
		UpperCAmelCase_		:   int													=       capacity - weights[counter - 1]
		UpperCAmelCase_		:   Optional[int]													=       values[counter - 1] + knapsack(
		    UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,counter - 1	)
		UpperCAmelCase_		:   Any													=       knapsack(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,counter - 1	)
		return max(UpperCamelCase ,UpperCamelCase	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()






