






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase__						:      Union[str, Any]								=					logging.getLogger()





def   SCREAMING_SNAKE_CASE__						(   )   ->  List[Any]:


	"""simple docstring"""



	a		=			argparse.ArgumentParser()
	parser.add_argument('''-f'''  )
	a		=			parser.parse_args()
	return args.f







class 				lowerCamelCase_     (	SCREAMING_SNAKE_CASE__     ):
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]					):





								'''simple docstring'''



								a		=			logging.StreamHandler(sys.stdout					)
								logger.addHandler(__lowerCamelCase					)

								a		=			self.get_auto_remove_tmp_dir()
								a		=			F"""\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            """.split()

								with patch.object(__lowerCamelCase							,'''argv'''							,__lowerCamelCase					):
									a		=			run_glue_with_pabee.main()
									for value in result.values():
										self.assertGreaterEqual(__lowerCamelCase							,0.75					)













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__						:      Tuple								=					datasets.utils.logging.get_logger(__name__)







@dataclass
class 				lowerCamelCase_     (	datasets.BuilderConfig     ):
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			True
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			1_00_00
							SCREAMING_SNAKE_CASE_						     =			None
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]					):





								'''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  SCREAMING_SNAKE_CASE_ (						self		:					Any							,__lowerCamelCase		:					dict							,__lowerCamelCase		:					Optional[datasets.Features] = None							,):





								'''simple docstring'''



								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(__lowerCamelCase							,__lowerCamelCase					):
									if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
										import sqlalchemy

										if isinstance(__lowerCamelCase							,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(__lowerCamelCase					)}: {sql}"""					)
									else:
										raise TypeError(
										    F"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(__lowerCamelCase					)}: {sql}"""					)
								a		=			config_kwargs['''con''']
								if not isinstance(__lowerCamelCase							,__lowerCamelCase					):
									a		=			id(__lowerCamelCase					)
									logger.info(
									    F"""SQL connection 'con' of type {type(__lowerCamelCase					)} couldn't be hashed properly. To enable hashing, specify 'con' as URI string instead."""					)

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




							@property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								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 				lowerCamelCase_     (	datasets.ArrowBasedBuilder     ):
							SCREAMING_SNAKE_CASE_						     =			SqlConfig
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]					):





								'''simple docstring'''



								return datasets.DatasetInfo(features=self.config.features					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,gen_kwargs={}					)]
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,__lowerCamelCase		:					pa.Table					):





								'''simple docstring'''



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




							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



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





