

'''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_			(    _snake_case							):






     '''simple docstring'''



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




     def        UpperCAmelCase_	(		self			:					Tuple	)						->     Tuple:
      UpperCAmelCase	:     List[Any]						 =			None
      UpperCAmelCase	:     Union[str, Any]						 =			None
      UpperCAmelCase	:     Dict						 =			None
      UpperCAmelCase	:     Union[str, Any]						 =			None

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

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





class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					Dataset					,      lowercase_			:					str					,      lowercase_			:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]					,      lowercase_			:					Optional[int] = None					,      lowercase_			:					Optional[int] = None					,      **lowercase_			:					Any					,      )						->     str:


      if num_proc is not None and num_proc <= 0:
       raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""	)

      UpperCAmelCase	:     Union[str, Any]						 =			dataset
      UpperCAmelCase	:     Any						 =			name
      UpperCAmelCase	:     str						 =			con
      UpperCAmelCase	:     List[Any]						 =			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
      UpperCAmelCase	:     List[Any]						 =			num_proc
      UpperCAmelCase	:     List[Any]						 =			to_sql_kwargs


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     int:
      UpperCAmelCase	:     str						 =			self.to_sql_kwargs.pop('sql'					,      __UpperCAmelCase	)
      UpperCAmelCase	:     List[str]						 =			self.to_sql_kwargs.pop('con'					,      __UpperCAmelCase	)
      UpperCAmelCase	:     List[str]						 =			self.to_sql_kwargs.pop('index'					,      __UpperCAmelCase	)

      UpperCAmelCase	:     str						 =			self._write(index=__UpperCAmelCase					,      **self.to_sql_kwargs	)
      return written


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




     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					List[Any]					,      **lowercase_			:					List[str]	)						->     int:
      UpperCAmelCase	:     Dict						 =			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	:     Dict						 =			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					,      __UpperCAmelCase					,      __UpperCAmelCase	)]					,      )					,      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, is_vision_available


lowercase__         =      {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "VanForImageClassification",
     "VanModel",
     "VanPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_van import (
      VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      VanForImageClassification,
      VanModel,
      VanPreTrainedModel,
  )

else:
 import sys

 lowercase__         =      _LazyModule(__name__, globals()["__file__"], _import_structure)




