


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    (			lowercase__							):


  '''simple docstring'''







  def __init__(      self      :					List[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      ,				):


    '''simple docstring'''





    super().__init__(features=__lowercase      ,				cache_dir=__lowercase      ,				keep_in_memory=__lowercase      ,				**__lowercase							)
    _snake_case   :				Any														=	Sql(
        cache_dir=__lowercase      ,				features=__lowercase      ,				sql=__lowercase      ,				con=__lowercase      ,				**__lowercase      ,				)







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				Any														=	None
    _snake_case   :				List[str]														=	None
    _snake_case   :				Any														=	None
    _snake_case   :				Optional[int]														=	None

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

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




class        _lowerCAmelCase    :


  '''simple docstring'''







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


    '''simple docstring'''





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

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







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				int														=	self.to_sql_kwargs.pop('sql'      ,				__lowercase							)
    _snake_case   :				str														=	self.to_sql_kwargs.pop('con'      ,				__lowercase							)
    _snake_case   :				List[Any]														=	self.to_sql_kwargs.pop('index'      ,				__lowercase							)

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







  def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					Any							):


    '''simple docstring'''





    _snake_case							,					_snake_case							,					_snake_case   :				str														=	args
    _snake_case   :				str														=	{**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
    _snake_case   :				Dict														=	query_table(
        table=self.dataset.data      ,				key=slice(__lowercase      ,				offset + self.batch_size							)      ,				indices=self.dataset._indices      ,				)
    _snake_case   :				Optional[int]														=	batch.to_pandas()
    _snake_case   :				List[str]														=	df.to_sql(self.name      ,				self.con      ,				index=__lowercase      ,				**__lowercase							)
    return num_rows or len(__lowercase							)







  def UpperCamelCase_ (      self      :					int      ,				UpperCamelCase      :					int      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    _snake_case   :				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:
      _snake_case							,					_snake_case   :				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











import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple    ,							lowerCAmelCase:	bool = True    ,							lowerCAmelCase:	float = math.inf    ,							lowerCAmelCase:	float = -math.inf    ,							lowerCAmelCase:	float = math.inf    ,							lowerCAmelCase:	float = -math.inf    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	float = 1_00    ,							lowerCAmelCase:	float = 0.0_1    ,							lowerCAmelCase:	float = 1    ,							)->		Any:
  _snake_case   :				int														=	False
  _snake_case   :				Any														=	search_prob
  _snake_case   :				Tuple														=	start_temperate
  _snake_case   :				Any														=	[]
  _snake_case   :				List[str]														=	0
  _snake_case   :				Optional[Any]														=	None

  while not search_end:
    _snake_case   :				List[Any]														=	current_state.score()
    if best_state is None or current_score > best_state.score():
      _snake_case   :				Dict														=	current_state
    scores.append(lowerCAmelCase				)
    iterations += 1
    _snake_case   :				Optional[int]														=	None
    _snake_case   :				Union[str, Any]														=	current_state.get_neighbors()
    while (
        next_state is None and neighbors
    ):  # till we do not find a neighbor that we can move to
      _snake_case   :				Dict														=	random.randint(0    ,							len(lowerCAmelCase				) - 1				)  # picking a random neighbor
      _snake_case   :				int														=	neighbors.pop(lowerCAmelCase				)
      _snake_case   :				Union[str, Any]														=	picked_neighbor.score() - current_score

      if (
          picked_neighbor.x > max_x
          or picked_neighbor.x < min_x
          or picked_neighbor.y > max_y
          or picked_neighbor.y < min_y
      ):
        continue  # neighbor outside our bounds

      if not find_max:
        _snake_case   :				Union[str, Any]														=	change * -1  # in case we are finding minimum
      if change > 0:  # improves the solution
        _snake_case   :				Union[str, Any]														=	picked_neighbor
      else:
        _snake_case   :				Optional[Any]														=	(math.e) ** (
            change / current_temp
        )  # probability generation function
        if random.random() < probability:  # random number within probability
          _snake_case   :				int														=	picked_neighbor
    _snake_case   :				List[Any]														=	current_temp - (current_temp * rate_of_decrease)

    if current_temp < threshold_temp or next_state is None:
      # temperature below threshold, or could not find a suitable neighbor
      _snake_case   :				List[str]														=	True
    else:
      _snake_case   :				Union[str, Any]														=	next_state

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(lowerCAmelCase				)    ,							lowerCAmelCase				)
    plt.xlabel('Iterations'				)
    plt.ylabel('Function values'				)
    plt.show()
  return best_state


if __name__ == "__main__":


      def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	List[Any]				)->		List[Any]:
        return (x**2) + (y**2)

      # starting the problem with initial coordinates (12, 47)
      lowerCAmelCase_						   =    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    simulated_annealing(
          prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
      )
      print(
          """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
          F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
      )

      # starting the problem with initial coordinates (12, 47)
      lowerCAmelCase_						   =    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    simulated_annealing(
          prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
      )
      print(
          """The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
          F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
      )


      def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	Dict				)->		Dict:
        return (3 * x**2) - (6 * y)

      lowerCAmelCase_						   =    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    simulated_annealing(prob, find_max=False, visualization=True)
      print(
          """The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
          F"""{local_min.score()}"""
      )

      lowerCAmelCase_						   =    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    simulated_annealing(prob, find_max=True, visualization=True)







      print(
          """The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
          F"""{local_min.score()}"""
      )




