




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__       				=	datasets.utils.logging.get_logger(__name__)
@dataclass
class 							__lowercase   (			datasets.BuilderConfig    ):
     _lowerCAmelCase		       =						None
     _lowerCAmelCase		       =						None
     _lowerCAmelCase		       =						None
     _lowerCAmelCase		       =						True
     _lowerCAmelCase		       =						None
     _lowerCAmelCase		       =						None
     _lowerCAmelCase		       =						None
     _lowerCAmelCase		       =						1_00_00
     _lowerCAmelCase		       =						None



     def 				__magic_name__		(  self   :							List[Any]       ):





       if self.sql is None:
         raise ValueError('''sql must be specified'''       )
       if self.con is None:
         raise ValueError('''con must be specified'''       )



     def 				__magic_name__		(  self   :							Optional[Any]      ,		lowercase__   :							dict      ,		lowercase__   :							Optional[datasets.Features] = None      ,		):
       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 				__magic_name__		(  self   :							List[Any]       ):
       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 							__lowercase   (			datasets.ArrowBasedBuilder    ):
     _lowerCAmelCase		       =						SqlConfig



     def 				__magic_name__		(  self   :							List[str]       ):
       return datasets.DatasetInfo(features=self.config.features       )



     def 				__magic_name__		(  self   :							Optional[Any]      ,		lowercase__   :							Optional[Any]       ):
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,		gen_kwargs={}       )]



     def 				__magic_name__		(  self   :							Union[str, Any]      ,		lowercase__   :							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 				__magic_name__		(  self   :							Optional[Any]       ):
       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_       )





import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A       =logging.getLogger(__name__)

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Dict						=      ArgumentParser()
		__UpperCAmelCase				,   __UpperCAmelCase       :	int						=      parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')		):
						parser.add_argument(arg.split('''='''		)[0]		)

		return parser.parse_args()

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      parse_args()
		__UpperCAmelCase       :	Tuple						=      88_88
		__UpperCAmelCase       :	Any						=      int(os.environ['''SM_NUM_GPUS''']		)
		__UpperCAmelCase       :	Optional[int]						=      json.loads(os.environ['''SM_HOSTS''']		)
		__UpperCAmelCase       :	int						=      len(_UpperCAmelCase		)
		__UpperCAmelCase       :	Optional[Any]						=      os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase       :	List[str]						=      hosts.index(_UpperCAmelCase		)
		__UpperCAmelCase       :	Dict						=      '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase       :	Tuple						=      f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		else:
				__UpperCAmelCase       :	Dict						=      f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		try:
				subprocess.run(_UpperCAmelCase					,  shell=_UpperCAmelCase		)
		except Exception as e:
				logger.info(_UpperCAmelCase		)


if __name__ == "__main__":
				main()





