


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       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     **UpperCAmelCase		,     )     ->   str:
      super().__init__(features=UpperCAmelCase		,     cache_dir=UpperCAmelCase		,     keep_in_memory=UpperCAmelCase		,     **UpperCAmelCase       )
      _lowercase							      =Sql(
          cache_dir=UpperCAmelCase		,     features=UpperCAmelCase		,     sql=UpperCAmelCase		,     con=UpperCAmelCase		,     **UpperCAmelCase		,     )





     def      __A					(self       )     ->   Dict:
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None

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

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







class       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     **UpperCAmelCase		,     )     ->   Optional[int]:
      if num_proc is not None and num_proc <= 0:
       raise ValueError(f"num_proc {num_proc} must be an integer > 0."       )

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





     def      __A					(self       )     ->   int:
      _lowercase							      =self.to_sql_kwargs.pop('''sql'''		,     UpperCAmelCase       )
      _lowercase							      =self.to_sql_kwargs.pop('''con'''		,     UpperCAmelCase       )
      _lowercase							      =self.to_sql_kwargs.pop('''index'''		,     UpperCAmelCase       )

      _lowercase							      =self._write(index=UpperCAmelCase		,     **self.to_sql_kwargs       )
      return written





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





     def      __A					(self		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   int:
      _lowercase							      =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:
       _lowercase      ,    _lowercase							      =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





from typing import TYPE_CHECKING

from ..utils import _LazyModule


UpperCAmelCase__   	=							{
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
  from .config import (
      EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
      OnnxConfig,
      OnnxConfigWithPast,
      OnnxSeqaSeqConfigWithPast,
      PatchingSpec,
  )
  from .convert import export, validate_model_outputs
  from .features import FeaturesManager
  from .utils import ParameterFormat, compute_serialized_parameters_size

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

