




"""simple docstring"""


import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      bool = True	,     snake_case__							:      float = math.inf	,     snake_case__							:      float = -math.inf	,     snake_case__							:      float = math.inf	,     snake_case__							:      float = -math.inf	,     snake_case__							:      bool = False	,     snake_case__							:      float = 1_00	,     snake_case__							:      float = 0.01	,     snake_case__							:      float = 1	,     ):
  """simple docstring"""

  _snake_case							:	Union[str, Any]			=						False
  _snake_case							:	List[str]			=						search_prob
  _snake_case							:	Dict			=						start_temperate
  _snake_case							:	str			=						[]
  _snake_case							:	Any			=						0
  _snake_case							:	str			=						None

  while not search_end:
    _snake_case							:	int			=						current_state.score()
    if best_state is None or current_score > best_state.score():
      _snake_case							:	List[str]			=						current_state
    scores.append(snake_case__					)
    iterations += 1
    _snake_case							:	Union[str, Any]			=						None
    _snake_case							:	str			=						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							:	Tuple			=						random.randint(0	,     len(snake_case__					) - 1					)  # picking a random neighbor
      _snake_case							:	Dict			=						neighbors.pop(snake_case__					)
      _snake_case							:	Dict			=						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							:	Tuple			=						change * -1  # in case we are finding minimum
      if change > 0:  # improves the solution
        _snake_case							:	int			=						picked_neighbor
      else:
        _snake_case							:	int			=						(math.e) ** (
            change / current_temp
        )  # probability generation function
        if random.random() < probability:  # random number within probability
          _snake_case							:	List[str]			=						picked_neighbor
    _snake_case							:	List[str]			=						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							:	Optional[Any]			=						next_state

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(snake_case__					)	,     snake_case__					)
    plt.xlabel("""Iterations"""					)
    plt.ylabel("""Function values"""					)
    plt.show()
  return best_state


if __name__ == "__main__":


     def       UpperCAmelCase__ (snake_case__							:      Any	,     snake_case__							:      Dict					):
       """simple docstring"""

       return (x**2) + (y**2)

     # starting the problem with initial coordinates (12, 47)
     A_      			=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
     A_      			=       simulated_annealing(
         prob, find_max=False, max_x=1_00, 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)
     A_      			=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
     A_      			=       simulated_annealing(
         prob, find_max=True, max_x=1_00, 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       UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      List[str]					):
       """simple docstring"""

       return (3 * x**2) - (6 * y)

     A_      			=       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     A_      			=       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()}'''
     )

     A_      			=       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     A_      			=       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()}'''
     )








"""simple docstring"""


import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def       UpperCAmelCase__ (snake_case__							:      Optional[Any]	,     snake_case__							:      Dict					):
  """simple docstring"""

  assert isinstance(snake_case__	,     snake_case__					)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("""keep_in_memory"""	,     [False, True]					)
def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Optional[int]	,     snake_case__							:      Dict					):
  """simple docstring"""

  _snake_case							:	str			=						tmp_path / """cache"""
  _snake_case							:	int			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case							:	str			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__	,     keep_in_memory=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)


@pytest.mark.parametrize(
    """features"""	,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]	,     )
def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      int	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	str			=						tmp_path / """cache"""
  _snake_case							:	List[Any]			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	List[Any]			=						features.copy() if features else default_expected_features
  _snake_case							:	List[Any]			=						(
      Features({feature: Value(snake_case__					) for feature, dtype in features.items()}					) if features is not None else None
  )
  _snake_case							:	Optional[Any]			=						ParquetDatasetReader(snake_case__	,     features=snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)


@pytest.mark.parametrize("""split"""	,     [None, NamedSplit("""train"""					), """train""", """test"""]					)
def       UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      int	,     snake_case__							:      int					):
  """simple docstring"""

  _snake_case							:	List[str]			=						tmp_path / """cache"""
  _snake_case							:	Any			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	int			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__	,     split=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)
  assert dataset.split == split if split else "train"


@pytest.mark.parametrize("""path_type"""	,     [str, list]					)
def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      str	,     snake_case__							:      str					):
  """simple docstring"""

  if issubclass(snake_case__	,     snake_case__					):
    _snake_case							:	Optional[Any]			=						parquet_path
  elif issubclass(snake_case__	,     snake_case__					):
    _snake_case							:	int			=						[parquet_path]
  _snake_case							:	Union[str, Any]			=						tmp_path / """cache"""
  _snake_case							:	Tuple			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	List[str]			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)


def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Optional[int]	,     snake_case__							:      str=("train",)					):
  """simple docstring"""

  assert isinstance(snake_case__	,     snake_case__					)
  for split in splits:
    _snake_case							:	int			=						dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("""keep_in_memory"""	,     [False, True]					)
def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      str	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	Tuple			=						tmp_path / """cache"""
  _snake_case							:	Optional[int]			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case							:	Tuple			=						ParquetDatasetReader(
        {"""train""": parquet_path}	,     cache_dir=snake_case__	,     keep_in_memory=snake_case__					).read()
  _check_parquet_datasetdict(snake_case__	,     snake_case__					)


@pytest.mark.parametrize(
    """features"""	,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]	,     )
def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      Tuple	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	Optional[int]			=						tmp_path / """cache"""
  _snake_case							:	Dict			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	Optional[Any]			=						features.copy() if features else default_expected_features
  _snake_case							:	Dict			=						(
      Features({feature: Value(snake_case__					) for feature, dtype in features.items()}					) if features is not None else None
  )
  _snake_case							:	Optional[int]			=						ParquetDatasetReader({"""train""": parquet_path}	,     features=snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_datasetdict(snake_case__	,     snake_case__					)


@pytest.mark.parametrize("""split"""	,     [None, NamedSplit("""train"""					), """train""", """test"""]					)
def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      Optional[Any]	,     snake_case__							:      Tuple					):
  """simple docstring"""

  if split:
    _snake_case							:	int			=						{split: parquet_path}
  else:
    _snake_case							:	Optional[Any]			=						"""train"""
    _snake_case							:	int			=						{"""train""": parquet_path, """test""": parquet_path}
  _snake_case							:	Dict			=						tmp_path / """cache"""
  _snake_case							:	Any			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	Union[str, Any]			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_datasetdict(snake_case__	,     snake_case__	,     splits=list(path.keys()					)					)
  assert all(dataset[split].split == split for split in path.keys()					)


def       UpperCAmelCase__ (snake_case__							:      Tuple	,     snake_case__							:      Tuple					):
  """simple docstring"""

  _snake_case							:	List[Any]			=						ParquetDatasetWriter(snake_case__	,     tmp_path / """foo.parquet"""					)
  assert writer.write() > 0
  _snake_case							:	str			=						pq.ParquetFile(tmp_path / """foo.parquet"""					)
  _snake_case							:	int			=						pf.read()
  assert dataset.data.table == output_table


def       UpperCAmelCase__ (snake_case__							:      int	,     snake_case__							:      int					):
  """simple docstring"""

  _snake_case							:	Optional[Any]			=						str(shared_datadir / """test_image_rgb.jpg"""					)
  _snake_case							:	Tuple			=						{"""image""": [image_path]}
  _snake_case							:	Optional[int]			=						Features({"""image""": Image()}					)
  _snake_case							:	int			=						Dataset.from_dict(snake_case__	,     features=snake_case__					)
  _snake_case							:	Optional[Any]			=						ParquetDatasetWriter(snake_case__	,     tmp_path / """foo.parquet"""					)
  assert writer.write() > 0

  _snake_case							:	List[str]			=						Dataset.from_parquet(str(tmp_path / """foo.parquet"""					)					)
  assert dataset.features == reloaded_dataset.features

  _snake_case							:	Optional[Any]			=						ParquetDatasetReader(str(tmp_path / """foo.parquet"""					)	,     streaming=snake_case__					).read()
  assert dataset.features == reloaded_iterable_dataset.features






@pytest.mark.parametrize(
    """feature, expected"""	,     [
        (Features({"""foo""": Value("""int32"""					)}					), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""					)}					), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()					)}					), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]	,     )
def       UpperCAmelCase__ (snake_case__							:      List[Any]	,     snake_case__							:      str					):
  """simple docstring"""

  assert get_writer_batch_size(snake_case__					) == expected


