






"""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        lowercase__      (							snake_case__       ):


			def __init__(				self					:  int       ,	snake_case__					:  Union[str, "sqlalchemy.sql.Selectable"]       ,	snake_case__					:  Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]       ,	snake_case__					:  Optional[Features] = None       ,	snake_case__					:  str = None       ,	snake_case__					:  bool = False       ,	**snake_case__					:  Any       ,	):
										super().__init__(features=snake_case__       ,	cache_dir=snake_case__       ,	keep_in_memory=snake_case__       ,	**snake_case__ )
										lowerCamelCase_	:							int									=Sql(
										    cache_dir=snake_case__       ,	features=snake_case__       ,	sql=snake_case__       ,	con=snake_case__       ,	**snake_case__       ,	)


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_	:							str									=None
										lowerCamelCase_	:							Any									=None
										lowerCamelCase_	:							Tuple									=None
										lowerCamelCase_	:							str									=None

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

										# Build dataset for splits
										lowerCamelCase_	:							List[Any]									=self.builder.as_dataset(
										    split="train"       ,	verification_mode=snake_case__       ,	in_memory=self.keep_in_memory )
										return dataset







class        lowercase__      :


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

										lowerCamelCase_	:							Tuple									=dataset
										lowerCamelCase_	:							Dict									=name
										lowerCamelCase_	:							Optional[Any]									=con
										lowerCamelCase_	:							List[Any]									=batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
										lowerCamelCase_	:							List[str]									=num_proc
										lowerCamelCase_	:							Tuple									=to_sql_kwargs


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							List[str]									=self.to_sql_kwargs.pop("sql"       ,	snake_case__ )
										lowerCamelCase_	:							Optional[int]									=self.to_sql_kwargs.pop("con"       ,	snake_case__ )
										lowerCamelCase_	:							int									=self.to_sql_kwargs.pop("index"       ,	snake_case__ )

										lowerCamelCase_	:							List[str]									=self._write(index=snake_case__       ,	**self.to_sql_kwargs )
										return written


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Dict ):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Tuple									=args
										lowerCamelCase_	:							Union[str, Any]									={**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
										lowerCamelCase_	:							int									=query_table(
										    table=self.dataset.data       ,	key=slice(snake_case__       ,	offset + self.batch_size )       ,	indices=self.dataset._indices       ,	)
										lowerCamelCase_	:							str									=batch.to_pandas()
										lowerCamelCase_	:							List[Any]									=df.to_sql(self.name       ,	self.con       ,	index=snake_case__       ,	**snake_case__ )
										return num_rows or len(snake_case__ )


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  Optional[Any]       ,	**snake_case__					:  Optional[Any] ):
										lowerCamelCase_	:							int									=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:
																	lowerCamelCase_							,						lowerCamelCase_	:							Optional[int]									=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       ,	snake_case__       ,	snake_case__ )]       ,	)       ,	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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__	:    List[Any]			 = {
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    List[str]			 = [
						    'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'LiltForQuestionAnswering',
						    'LiltForSequenceClassification',
						    'LiltForTokenClassification',
						    'LiltModel',
						    'LiltPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

						A__	:    Optional[Any]			 = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



