'''simple docstring'''


import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
					import sqlitea

					import sqlalchemy






class 						A   (   UpperCAmelCase   ):





							def __init__(      self				:							Optional[Any]     ,		__a				:							Union[str, "sqlalchemy.sql.Selectable"]     ,		__a				:							Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]     ,		__a				:							Optional[Features] = None     ,		__a				:							str = None     ,		__a				:							bool = False     ,		**__a				:							Dict     ,		)							->	Tuple:
								super().__init__(features=__a     ,		cache_dir=__a     ,		keep_in_memory=__a     ,		**__a	)
								__UpperCAmelCase							=			Sql(
								    cache_dir=__a     ,		features=__a     ,		sql=__a     ,		con=__a     ,		**__a     ,		)






							def       snake_case__						(      self				:							Any	)							->	Optional[int]:
								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			None

								self.builder.download_and_prepare(
								    download_config=__a     ,		download_mode=__a     ,		verification_mode=__a     ,		base_path=__a     ,		)

								# Build dataset for splits
								__UpperCAmelCase							=			self.builder.as_dataset(
								    split='''train'''     ,		verification_mode=__a     ,		in_memory=self.keep_in_memory	)
								return dataset






class 						A   :





							def __init__(      self				:							List[str]     ,		__a				:							Dataset     ,		__a				:							str     ,		__a				:							Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]     ,		__a				:							Optional[int] = None     ,		__a				:							Optional[int] = None     ,		**__a				:							Optional[Any]     ,		)							->	Any:
								if num_proc is not None and num_proc <= 0:
									raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""	)

								__UpperCAmelCase							=			dataset
								__UpperCAmelCase							=			name
								__UpperCAmelCase							=			con
								__UpperCAmelCase							=			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								__UpperCAmelCase							=			num_proc
								__UpperCAmelCase							=			to_sql_kwargs





							def       snake_case__						(      self				:							List[Any]	)							->	int:
								__UpperCAmelCase							=			self.to_sql_kwargs.pop('''sql'''     ,		__a	)
								__UpperCAmelCase							=			self.to_sql_kwargs.pop('''con'''     ,		__a	)
								__UpperCAmelCase							=			self.to_sql_kwargs.pop('''index'''     ,		__a	)

								__UpperCAmelCase							=			self._write(index=__a     ,		**self.to_sql_kwargs	)
								return written





							def       snake_case__						(      self				:							str     ,		__a				:							Optional[Any]	)							->	List[str]:
								__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			args
								__UpperCAmelCase							=			{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
								__UpperCAmelCase							=			query_table(
								    table=self.dataset.data     ,		key=slice(__a     ,		offset + self.batch_size	)     ,		indices=self.dataset._indices     ,		)
								__UpperCAmelCase							=			batch.to_pandas()
								__UpperCAmelCase							=			df.to_sql(self.name     ,		self.con     ,		index=__a     ,		**__a	)
								return num_rows or len(__a	)






							def       snake_case__						(      self				:							str     ,		__a				:							Optional[int]     ,		**__a				:							List[str]	)							->	int:
								__UpperCAmelCase							=			0

								if self.num_proc is None or self.num_proc == 1:
									for offset in logging.tqdm(
									    range(0     ,		len(self.dataset	)     ,		self.batch_size	)     ,		unit='''ba'''     ,		disable=not logging.is_progress_bar_enabled()     ,		desc='''Creating SQL from Arrow format'''     ,		):
										written += self._batch_sql((offset, index, to_sql_kwargs)	)
								else:
									__UpperCAmelCase				,			__UpperCAmelCase							=			len(self.dataset	), self.batch_size
									with multiprocessing.Pool(self.num_proc	) as pool:
										for num_rows in logging.tqdm(
										    pool.imap(
										        self._batch_sql     ,		[(offset, index, to_sql_kwargs) for offset in range(0     ,		__a     ,		__a	)]     ,		)     ,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     ,		unit='''ba'''     ,		disable=not logging.is_progress_bar_enabled()     ,		desc='''Creating SQL from Arrow format'''     ,		):
											written += num_rows

								return written

'''simple docstring'''


from ...configuration_utils import PretrainedConfig






class 						A   (   UpperCAmelCase   ):
							a_        =      '''bert-generation'''





							def __init__(      self				:							str     ,		__a				:							str=5_0_3_5_8     ,		__a				:							int=1_0_2_4     ,		__a				:							Optional[Any]=2_4     ,		__a				:							Any=1_6     ,		__a				:							int=4_0_9_6     ,		__a				:							Any="gelu"     ,		__a				:							Union[str, Any]=0.1     ,		__a				:							Any=0.1     ,		__a				:							Union[str, Any]=5_1_2     ,		__a				:							int=0.0_2     ,		__a				:							str=1e-12     ,		__a				:							List[str]=0     ,		__a				:							Optional[int]=2     ,		__a				:							Tuple=1     ,		__a				:							str="absolute"     ,		__a				:							Optional[Any]=True     ,		**__a				:							Tuple     ,		)							->	Any:
								super().__init__(pad_token_id=__a     ,		bos_token_id=__a     ,		eos_token_id=__a     ,		**__a	)

								__UpperCAmelCase							=			vocab_size
								__UpperCAmelCase							=			hidden_size
								__UpperCAmelCase							=			num_hidden_layers
								__UpperCAmelCase							=			num_attention_heads
								__UpperCAmelCase							=			hidden_act
								__UpperCAmelCase							=			intermediate_size
								__UpperCAmelCase							=			hidden_dropout_prob
								__UpperCAmelCase							=			attention_probs_dropout_prob
								__UpperCAmelCase							=			max_position_embeddings
								__UpperCAmelCase							=			initializer_range
								__UpperCAmelCase							=			layer_norm_eps
								__UpperCAmelCase							=			position_embedding_type
								__UpperCAmelCase							=			use_cache
