




"""simple docstring"""





import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase = True				,       UpperCamelCase = math.inf				,       UpperCamelCase = -math.inf				,       UpperCamelCase = math.inf				,       UpperCamelCase = -math.inf				,       UpperCamelCase = False				,       UpperCamelCase = 100				,       UpperCamelCase = 0.01				,       UpperCamelCase = 1				,       )			->			Any:


			"""simple docstring"""
			__UpperCAmelCase	: List[str]		=					False
			__UpperCAmelCase	: Any		=					search_prob
			__UpperCAmelCase	: str		=					start_temperate
			__UpperCAmelCase	: Any		=					[]
			__UpperCAmelCase	: List[str]		=					0
			__UpperCAmelCase	: Tuple		=					None

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

			if visualization:
						from matplotlib import pyplot as plt

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


if __name__ == "__main__":

					def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Any:


								"""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=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)
					A   =       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					A   =       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 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


								"""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 os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					"ylacombe/bark-small"
						__UpperCAmelCase	: Any		=					tempfile.mkdtemp()
						__UpperCAmelCase	: List[str]		=					"en_speaker_1"
						__UpperCAmelCase	: List[Any]		=					"This is a test string"
						__UpperCAmelCase	: List[str]		=					"speaker_embeddings_path.json"
						__UpperCAmelCase	: Tuple		=					"speaker_embeddings"


			def       a_     (      self      :    Optional[int]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




						return AutoTokenizer.from_pretrained(self.checkpoint      ,  **UpperCamelCase_)


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.get_tokenizer()

						__UpperCAmelCase	: Optional[Any]		=					BarkProcessor(tokenizer=UpperCamelCase_)

						processor.save_pretrained(self.tmpdirname)
						__UpperCAmelCase	: Optional[int]		=					BarkProcessor.from_pretrained(self.tmpdirname)

						self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer.get_vocab())


			@slow
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					BarkProcessor.from_pretrained(
						    pretrained_processor_name_or_path=self.checkpoint      ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,  )
						processor.save_pretrained(
						    self.tmpdirname      ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,  speaker_embeddings_directory=self.speaker_embeddings_directory      ,  )

						__UpperCAmelCase	: Optional[Any]		=					self.get_tokenizer(bos_token="(BOS)"      ,  eos_token="(EOS)")

						__UpperCAmelCase	: Optional[Any]		=					BarkProcessor.from_pretrained(
						    self.tmpdirname      ,  self.speaker_embeddings_dict_path      ,  bos_token="(BOS)"      ,  eos_token="(EOS)"      ,  )

						self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer_add_kwargs.get_vocab())


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					BarkProcessor.from_pretrained(
						    pretrained_processor_name_or_path=self.checkpoint      ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,  )

						__UpperCAmelCase	: Optional[int]		=					35
						__UpperCAmelCase	: Union[str, Any]		=					2
						__UpperCAmelCase	: List[Any]		=					8

						__UpperCAmelCase	: str		=					{
						    "semantic_prompt": np.ones(UpperCamelCase_),
						    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
						    "fine_prompt": np.ones((nb_codebooks_total, seq_len)),
						}

						# test providing already loaded voice_preset
						__UpperCAmelCase	: Union[str, Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)

						__UpperCAmelCase	: str		=					inputs["history_prompt"]
						for key in voice_preset:
									self.assertListEqual(voice_preset[key].tolist()      ,  processed_voice_preset.get(UpperCamelCase_      ,  np.array([])).tolist())

						# test loading voice preset from npz file
						__UpperCAmelCase	: int		=					os.path.join(self.tmpdirname      ,  "file.npz")
						np.savez(UpperCamelCase_      ,  **UpperCamelCase_)
						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					inputs["history_prompt"]

						for key in voice_preset:
									self.assertListEqual(voice_preset[key].tolist()      ,  processed_voice_preset.get(UpperCamelCase_      ,  np.array([])).tolist())

						# test loading voice preset from the hub
						__UpperCAmelCase	: Tuple		=					processor(text=self.input_string      ,  voice_preset=self.voice_preset)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_tokenizer()

						__UpperCAmelCase	: Any		=					BarkProcessor(tokenizer=UpperCamelCase_)

						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string)

						__UpperCAmelCase	: List[Any]		=					tokenizer(
						    self.input_string      ,  padding="max_length"      ,  max_length=256      ,  add_special_tokens=UpperCamelCase_      ,  return_attention_mask=UpperCamelCase_      ,  return_token_type_ids=UpperCamelCase_      ,  )

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]      ,  encoded_processor[key].squeeze().tolist())


