


"""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_  :  Any     		=    datasets.utils.logging.get_logger(__name__)






@dataclass
class  __A							(			datasets.BuilderConfig     ):






 """simple docstring"""




 __lowerCAmelCase					   =    None
 __lowerCAmelCase					   =    None
 __lowerCAmelCase					   =    None
 __lowerCAmelCase					   =    True
 __lowerCAmelCase					   =    None
 __lowerCAmelCase					   =    None
 __lowerCAmelCase					   =    None
 __lowerCAmelCase					   =    10000
 __lowerCAmelCase					   =    None


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


 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A = None					,       )   ->	str:
     a											=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
     a											=config_kwargs['''sql''']
     if not isinstance(__A					,       __A		):
         if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
             import sqlalchemy

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

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





 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     a											={
         '''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  __A							(			datasets.ArrowBasedBuilder     ):






 """simple docstring"""




 __lowerCAmelCase					   =    SqlConfig


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


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


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





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



"""simple docstring"""







from .stack import Stack
def _A		(		lowercase						):







    """simple docstring"""
    a											=Stack()
    a											={'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
    for bracket in parentheses:
        if bracket in bracket_pairs:
            stack.push(lowercase						)
        elif bracket in (")", "]", "}"):
            if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                return False
    return stack.is_empty()


if __name__ == "__main__":
       from doctest import testmod

       testmod()

       lowerCamelCase_  :  List[str]     		=    ["""((()))""", """((())""", """(()))"""]
       print("""Balanced parentheses demonstration:\n""")
       for example in examples:
              lowerCamelCase_  :  str     		=    """""" if balanced_parentheses(example) else """not """
              print(F'{example} is {not_str}balanced')