



'''simple docstring'''




import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam

class     UpperCamelCase_				(							datasets.BeamBasedBuilder    ):





	def 				_lowercase(						self					)    ->    Optional[Any]:
								return datasets.DatasetInfo(
								    features=datasets.Features({"""content""": datasets.Value("""string"""					)}					)	,	supervised_keys=A	,	)





	def 				_lowercase(						self	,	A	,	A					)    ->    Dict:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,	gen_kwargs={"""examples""": get_test_dummy_examples()}					)]






	def 				_lowercase(						self	,	A	,	A					)    ->    List[str]:
								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(A					)




class     UpperCamelCase_				(							datasets.BeamBasedBuilder    ):





	def 				_lowercase(						self					)    ->    Dict:
								return datasets.DatasetInfo(
								    features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string"""					)}					)}					)	,	supervised_keys=A	,	)





	def 				_lowercase(						self	,	A	,	A					)    ->    Optional[Any]:
								return [
								    datasets.SplitGenerator(name=datasets.Split.TRAIN	,	gen_kwargs={"""examples""": get_test_nested_examples()}					)
								]






	def 				_lowercase(						self	,	A	,	A					)    ->    Optional[int]:
								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(A					)




def    __lowerCamelCase						(      )   ->    Any:
							return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""]	)]



def    __lowerCamelCase						(      )   ->    int:
							return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""]	)]


class     UpperCamelCase_				(							__magic_name__    ):





	@require_beam
	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	int							  =	len(get_test_dummy_examples()					)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
															UpperCAmelCase  :	Any							  =	DummyBeamDataset(cache_dir=A	,	beam_runner="""DirectRunner"""					)
															builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(A	,	builder.name	,	"""default"""	,	"""0.0.0"""	,	f'''{builder.name}-train.arrow'''					)					)					)
															self.assertDictEqual(builder.info.features	,	datasets.Features({"""content""": datasets.Value("""string"""					)}					)					)
															UpperCAmelCase  :	List[str]							  =	builder.as_dataset()
															self.assertEqual(dset["""train"""].num_rows	,	A					)
															self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples	,	A					)
															self.assertDictEqual(dset["""train"""][0]	,	get_test_dummy_examples()[0][1]					)
															self.assertDictEqual(
															    dset["""train"""][expected_num_examples - 1]	,	get_test_dummy_examples()[expected_num_examples - 1][1]					)
															self.assertTrue(
															    os.path.exists(os.path.join(A	,	builder.name	,	"""default"""	,	"""0.0.0"""	,	"""dataset_info.json"""					)					)					)
															del dset





	@require_beam
	def 				_lowercase(						self					)    ->    Any:
								import apache_beam as beam

								UpperCAmelCase  :	List[str]							  =	beam.io.parquetio.WriteToParquet

								UpperCAmelCase  :	Union[str, Any]							  =	len(get_test_dummy_examples()					)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
															UpperCAmelCase  :	Union[str, Any]							  =	DummyBeamDataset(cache_dir=A	,	beam_runner="""DirectRunner"""					)
															with patch("""apache_beam.io.parquetio.WriteToParquet"""					) as write_parquet_mock:
																						UpperCAmelCase  :	List[Any]							  =	partial(A	,	num_shards=2					)
																						builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(
															            A	,	builder.name	,	"""default"""	,	"""0.0.0"""	,	f'''{builder.name}-train-00000-of-00002.arrow'''					)					)					)
															self.assertTrue(
															    os.path.exists(
															        os.path.join(
															            A	,	builder.name	,	"""default"""	,	"""0.0.0"""	,	f'''{builder.name}-train-00000-of-00002.arrow'''					)					)					)
															self.assertDictEqual(builder.info.features	,	datasets.Features({"""content""": datasets.Value("""string"""					)}					)					)
															UpperCAmelCase  :	Dict							  =	builder.as_dataset()
															self.assertEqual(dset["""train"""].num_rows	,	A					)
															self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples	,	A					)
															# Order is not preserved when sharding, so we just check that all the elements are there
															self.assertListEqual(sorted(dset["""train"""]["""content"""]					)	,	sorted(["""foo""", """bar""", """foobar"""]					)					)
															self.assertTrue(
															    os.path.exists(os.path.join(A	,	builder.name	,	"""default"""	,	"""0.0.0"""	,	"""dataset_info.json"""					)					)					)
															del dset





	@require_beam
	def 				_lowercase(						self					)    ->    List[str]:
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
															UpperCAmelCase  :	Tuple							  =	DummyBeamDataset(cache_dir=A					)
															self.assertRaises(datasets.builder.MissingBeamOptions	,	builder.download_and_prepare					)






	@require_beam
	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Tuple							  =	len(get_test_nested_examples()					)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
															UpperCAmelCase  :	Any							  =	NestedBeamDataset(cache_dir=A	,	beam_runner="""DirectRunner"""					)
															builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(A	,	builder.name	,	"""default"""	,	"""0.0.0"""	,	f'''{builder.name}-train.arrow'''					)					)					)
															self.assertDictEqual(
															    builder.info.features	,	datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string"""					)}					)}					)					)
															UpperCAmelCase  :	Optional[Any]							  =	builder.as_dataset()
															self.assertEqual(dset["""train"""].num_rows	,	A					)
															self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples	,	A					)
															self.assertDictEqual(dset["""train"""][0]	,	get_test_nested_examples()[0][1]					)
															self.assertDictEqual(
															    dset["""train"""][expected_num_examples - 1]	,	get_test_nested_examples()[expected_num_examples - 1][1]					)
															self.assertTrue(
															    os.path.exists(os.path.join(A	,	builder.name	,	"""default"""	,	"""0.0.0"""	,	"""dataset_info.json"""					)					)					)
															del dset






'''simple docstring'''




import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def    __lowerCamelCase						(      _lowercase			,       _lowercase = True			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = False			,       _lowercase = 1_0_0			,       _lowercase = 0.01			,       _lowercase = 1			,       )   ->    Any:
							UpperCAmelCase  :	Optional[int]							  =	False
							UpperCAmelCase  :	Any							  =	search_prob
							UpperCAmelCase  :	Any							  =	start_temperate
							UpperCAmelCase  :	Optional[Any]							  =	[]
							UpperCAmelCase  :	Optional[Any]							  =	0
							UpperCAmelCase  :	Optional[Any]							  =	None

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

							if visualization:
														from matplotlib import pyplot as plt

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


if __name__ == "__main__":



							def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
														return (x**2) + (y**2)

							# starting the problem with initial coordinates (12, 47)
							a     :    Dict      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(
							    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, 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     :    List[str]      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(
							    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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						(      _lowercase			,       _lowercase	)   ->    Optional[Any]:
														return (3 * x**2) - (6 * y)

							a     :    Tuple      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Any      						=					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     :    List[Any]      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					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()}'''
							)

