





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







 def      A_	(      self  :      Any	)			->	Dict:
     lowerCamelCase__    :      Optional[Any]					 =		None
     lowerCamelCase__    :      Any					 =		None
     lowerCamelCase__    :      Any					 =		None
     lowerCamelCase__    :      Any					 =		None

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

     # Build dataset for splits
     lowerCamelCase__    :      Optional[int]					 =		self.builder.as_dataset(
         split='train'			,     verification_mode=lowercase_			,     in_memory=self.keep_in_memory	)
     return dataset


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

     lowerCamelCase__    :      Optional[int]					 =		dataset
     lowerCamelCase__    :      List[str]					 =		name
     lowerCamelCase__    :      int					 =		con
     lowerCamelCase__    :      int					 =		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     lowerCamelCase__    :      str					 =		num_proc
     lowerCamelCase__    :      Optional[int]					 =		to_sql_kwargs
 def      A_	(      self  :      Optional[int]	)			->	int:
     lowerCamelCase__    :      Optional[Any]					 =		self.to_sql_kwargs.pop('sql'			,     lowercase_	)
     lowerCamelCase__    :      Optional[Any]					 =		self.to_sql_kwargs.pop('con'			,     lowercase_	)
     lowerCamelCase__    :      List[Any]					 =		self.to_sql_kwargs.pop('index'			,     lowercase_	)

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







 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      Optional[int]			,     **UpperCAmelCase  :      Union[str, Any]	)			->	int:
     lowerCamelCase__    :      Optional[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:
         lowerCamelCase__ ,							lowerCamelCase__    :      Any					 =		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			,     lowercase_			,     lowercase_	)]			,     )			,     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 __future__ import annotations






class    UpperCAmelCase	:
 def __init__(				self      :Optional[int]						,       lowercase_      :int							)->							None:
     A__				=			order

     # a_{0} ... a_{k}
     A__				=			[1.0] + [0.0] * order
     # b_{0} ... b_{k}
     A__				=			[1.0] + [0.0] * order

     # x[n-1] ... x[n-k]
     A__				=			[0.0] * self.order
     # y[n-1] ... y[n-k]
     A__				=			[0.0] * self.order
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :list[float]						,       lowercase_      :list[float]							)->							None:
     if len(lowercase_							) < self.order:
         A__				=			[1.0, *a_coeffs]

     if len(lowercase_							) != self.order + 1:
         A__				=			(
             F"Expected a_coeffs to have {self.order + 1} elements "
             F"for {self.order}-order filter, got {len(lowercase_							)}"
         )
         raise ValueError(lowercase_							)

     if len(lowercase_							) != self.order + 1:
         A__				=			(
             F"Expected b_coeffs to have {self.order + 1} elements "
             F"for {self.order}-order filter, got {len(lowercase_							)}"
         )
         raise ValueError(lowercase_							)

     A__				=			a_coeffs
     A__				=			b_coeffs



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :float							)->							float:
     A__				=			0.0

     # Start at index 1 and do index 0 at the end.
     for i in range(1						,       self.order + 1							):
         result += (
             self.b_coeffs[i] * self.input_history[i - 1]
             - self.a_coeffs[i] * self.output_history[i - 1]
         )

     A__				=			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

     A__				=			self.input_history[:-1]
     A__				=			self.output_history[:-1]

     A__				=			sample
     A__				=			result

     return result



