
import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__ = True			,		UpperCamelCase__ = math.inf			,		UpperCamelCase__ = -math.inf			,		UpperCamelCase__ = math.inf			,		UpperCamelCase__ = -math.inf			,		UpperCamelCase__ = False			,		UpperCamelCase__ = 1_0_0			,		UpperCamelCase__ = 0.01			,		UpperCamelCase__ = 1			,		):
      UpperCamelCase__     : Tuple											=					False
      UpperCamelCase__     : int											=					search_prob
      UpperCamelCase__     : Dict											=					start_temperate
      UpperCamelCase__     : List[Any]											=					[]
      UpperCamelCase__     : str											=					0
      UpperCamelCase__     : List[str]											=					None

      while not search_end:
            UpperCamelCase__     : Dict											=					current_state.score()
            if best_state is None or current_score > best_state.score():
                  UpperCamelCase__     : Dict											=					current_state
            scores.append(UpperCamelCase__		)
            iterations += 1
            UpperCamelCase__     : Any											=					None
            UpperCamelCase__     : int											=					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__     : str											=					neighbors.pop(UpperCamelCase__		)
                  UpperCamelCase__     : 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:
                        UpperCamelCase__     : Any											=					change * -1  # in case we are finding minimum
                  if change > 0:  # improves the solution
                        UpperCamelCase__     : Union[str, Any]											=					picked_neighbor
                  else:
                        UpperCamelCase__     : str											=					(math.e) ** (
                            change / current_temp
                        )  # probability generation function
                        if random.random() < probability:  # random number within probability
                              UpperCamelCase__     : List[Any]											=					picked_neighbor
            UpperCamelCase__     : Tuple											=					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[Any]											=					True
            else:
                  UpperCamelCase__     : Union[str, 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 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
          return (x**2) + (y**2)

    # starting the problem with initial coordinates (12, 47)
    lowerCamelCase  =SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
    lowerCamelCase  =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)
    lowerCamelCase  =SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
    lowerCamelCase  =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 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
          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()}'''
    )




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
    from transformers import TvltImageProcessor

if is_speech_available():
    from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					'''ZinengTang/tvlt-base'''
           UpperCamelCase__     : Optional[Any]											=					tempfile.mkdtemp()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           return TvltImageProcessor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           return TvltFeatureExtractor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           shutil.rmtree(self.tmpdirname	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)
           processor.save_pretrained(self.tmpdirname	)
           UpperCamelCase__     : Optional[Any]											=					TvltProcessor.from_pretrained(self.tmpdirname	)

           self.assertIsInstance(processor.feature_extractor    ,    __SCREAMING_SNAKE_CASE	)
           self.assertIsInstance(processor.image_processor    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.get_image_processor()
           UpperCamelCase__     : int											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : str											=					np.ones([1_2_0_0_0]	)

           UpperCamelCase__     : Dict											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Dict											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in audio_dict.keys():
                 self.assertAlmostEqual(audio_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : str											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : Optional[int]											=					image_processor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Tuple											=					processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in image_dict.keys():
                 self.assertAlmostEqual(image_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.get_image_processor()
           UpperCamelCase__     : Optional[Any]											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Dict											=					np.ones([1_2_0_0_0]	)
           UpperCamelCase__     : str											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : List[Any]											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    images=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(list(inputs.keys()	)    ,    ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']	)

           # test if it raises when no input is passed
           with pytest.raises(__SCREAMING_SNAKE_CASE	):
                 processor()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					self.get_image_processor()
           UpperCamelCase__     : str											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(
               processor.model_input_names    ,    image_processor.model_input_names + feature_extractor.model_input_names    ,    msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''    ,    )


