




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							(   UpperCamelCase__		):

     def __init__(  self		,     _a		,     _a		,     _a = None		,     _a = None		,     _a = False		,     **_a		,     )						->							int:
           super().__init__(features=_a		,     cache_dir=_a		,     keep_in_memory=_a		,     **_a				)
           _A							:     Optional[int]			=						Sql(
               cache_dir=_a		,     features=_a		,     sql=_a		,     con=_a		,     **_a		,     )



     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Tuple			=						None
           _A							:     int			=						None
           _A							:     Tuple			=						None
           _A							:     Union[str, Any]			=						None

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

           # Build dataset for splits
           _A							:     int			=						self.builder.as_dataset(
               split="""train"""		,     verification_mode=_a		,     in_memory=self.keep_in_memory				)
           return dataset







class 						lowercase							:

     def __init__(  self		,     _a		,     _a		,     _a		,     _a = None		,     _a = None		,     **_a		,     )						->							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.'''				)

           _A							:     Dict			=						dataset
           _A							:     int			=						name
           _A							:     Union[str, Any]			=						con
           _A							:     str			=						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           _A							:     str			=						num_proc
           _A							:     Optional[Any]			=						to_sql_kwargs

     def 					a__	(  self				)						->							int:
           _A							:     Any			=						self.to_sql_kwargs.pop("""sql"""		,     _a				)
           _A							:     List[str]			=						self.to_sql_kwargs.pop("""con"""		,     _a				)
           _A							:     int			=						self.to_sql_kwargs.pop("""index"""		,     _a				)

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

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



     def 					a__	(  self		,     _a		,     **_a				)						->							int:
           _A							:     Any			=						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:
                 _A			,   _A							:     Tuple			=						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







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )
      if n == 0:
            return 0
      _A							:     Tuple			=						float("""-inf"""     )
      for i in range(1,n + 1     ):
            _A							:     str			=						max(
                snake_case_,prices[i - 1] + naive_cut_rod_recursive(n - i,snake_case_     )     )

      return max_revue







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )
      _A							:     Dict			=						[float("""-inf"""     ) for _ in range(n + 1     )]
      return _top_down_cut_rod_recursive(snake_case_,snake_case_,snake_case_     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      if max_rev[n] >= 0:
            return max_rev[n]
      elif n == 0:
            return 0
      else:
            _A							:     List[str]			=						float("""-inf"""     )
            for i in range(1,n + 1     ):
                  _A							:     Optional[Any]			=						max(
                      snake_case_,prices[i - 1] + _top_down_cut_rod_recursive(n - i,snake_case_,snake_case_     ),)

            _A							:     Tuple			=						max_revenue

      return max_rev[n]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )

      # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
      # length 0.
      _A							:     List[Any]			=						[float("""-inf"""     ) for _ in range(n + 1     )]
      _A							:     Any			=						0

      for i in range(1,n + 1     ):
            _A							:     Optional[Any]			=						max_rev[i]
            for j in range(1,i + 1     ):
                  _A							:     int			=						max(snake_case_,prices[j - 1] + max_rev[i - j]     )

            _A							:     int			=						max_revenue_i

      return max_rev[n]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      if n < 0:
            _A							:     Optional[Any]			=						f'''n must be greater than or equal to 0. Got n = {n}'''
            raise ValueError(snake_case_     )

      if n > len(snake_case_     ):
            _A							:     Any			=						(
                """Each integral piece of rod must have a corresponding price. """
                f'''Got n = {n} but length of prices = {len(snake_case_     )}'''
            )
            raise ValueError(snake_case_     )







def 				lowerCAmelCase_	(     ):
      _A							:     Tuple			=						[6, 10, 12, 15, 20, 23]
      _A							:     List[Any]			=						len(snake_case_     )

      # the best revenue comes from cutting the rod into 6 pieces, each
      # of length 1 resulting in a revenue of 6 * 6 = 36.
      _A							:     Any			=						36

      _A							:     List[Any]			=						top_down_cut_rod(snake_case_,snake_case_     )
      _A							:     List[Any]			=						bottom_up_cut_rod(snake_case_,snake_case_     )
      _A							:     Dict			=						naive_cut_rod_recursive(snake_case_,snake_case_     )

      assert expected_max_revenue == max_rev_top_down
      assert max_rev_top_down == max_rev_bottom_up
      assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
  main()

