"""simple docstring"""




import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE = True  ,      SCREAMING_SNAKE_CASE = math.inf  ,      SCREAMING_SNAKE_CASE = -math.inf  ,      SCREAMING_SNAKE_CASE = math.inf  ,      SCREAMING_SNAKE_CASE = -math.inf  ,      SCREAMING_SNAKE_CASE = False  ,      SCREAMING_SNAKE_CASE = 100  ,      SCREAMING_SNAKE_CASE = 0.01  ,      SCREAMING_SNAKE_CASE = 1  ,      ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		False
    __UpperCamelCase					:Tuple        =		search_prob
    __UpperCamelCase					:int        =		start_temperate
    __UpperCamelCase					:Any        =		[]
    __UpperCamelCase					:Union[str, Any]        =		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(SCREAMING_SNAKE_CASE     )
        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					:Optional[int]        =		random.randint(0  ,      len(SCREAMING_SNAKE_CASE     ) - 1     )  # picking a random neighbor
            __UpperCamelCase					:Optional[Any]        =		neighbors.pop(SCREAMING_SNAKE_CASE     )
            __UpperCamelCase					:List[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					:Dict        =		picked_neighbor
            else:
                __UpperCamelCase					:Tuple        =		(math.e) ** (
                    change / current_temp
                )  # probability generation function
                if random.random() < probability:  # random number within probability
                    __UpperCamelCase					:Dict        =		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					:Tuple        =		True
        else:
            __UpperCamelCase					:int        =		next_state

    if visualization:
        from matplotlib import pyplot as plt

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


if __name__ == "__main__":



    def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
        '''simple docstring'''







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

    # starting the problem with initial coordinates (12, 47)
    __lowercase										= SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    __lowercase										= 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)
    __lowercase										= SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    __lowercase										= 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  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
        '''simple docstring'''







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

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

    __lowercase										= SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    __lowercase										= 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 json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


class      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:str        =		0







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''')
        self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:int        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Dict        =		Path(__lowercase) / '''config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

            __UpperCamelCase					:Union[str, Any]        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        # Ensure we can load the image processor from the feature extractor config
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:str        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Union[str, Any]        =		Path(__lowercase) / '''config.json'''
            json.dump(
                {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:int        =		CLIPConfig()

            # Create a dummy config file with image_proceesor_type
            __UpperCamelCase					:Tuple        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Optional[Any]        =		Path(__lowercase) / '''config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

            # remove image_processor_type to make sure config.json alone is enough to load image processor locally
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(__lowercase).to_dict()

            config_dict.pop('''image_processor_type''')
            __UpperCamelCase					:List[str]        =		CLIPImageProcessor(**__lowercase)

            # save in new folder
            model_config.save_pretrained(__lowercase)
            config.save_pretrained(__lowercase)

            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained(__lowercase)

            # make sure private variable is not incorrectly saved
            __UpperCamelCase					:Union[str, Any]        =		json.loads(config.to_json_string())
            self.assertTrue('''_processor_class''' not in dict_as_saved)

        self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     List[str]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:Tuple        =		Path(__lowercase) / '''preprocessor_config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)

            __UpperCamelCase					:List[str]        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with self.assertRaisesRegex(
            __lowercase			,			'''clip-base is not a local folder and is not a valid model identifier'''):
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained('''clip-base''')







    def       UpperCamelCase__       (     self)	->     List[Any]:
        with self.assertRaisesRegex(
            __lowercase			,			r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
            __UpperCamelCase					:str        =		AutoImageProcessor.from_pretrained(__lowercase			,			revision='''aaaaaa''')







    def       UpperCamelCase__       (     self)	->     List[str]:
        with self.assertRaisesRegex(
            __lowercase			,			'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,			):
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''')







    def       UpperCamelCase__       (     self)	->     str:
        # If remote code is not set, we will time out when asking whether to load the model.
        with self.assertRaises(__lowercase):
            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(__lowercase):
            __UpperCamelCase					:List[Any]        =		AutoImageProcessor.from_pretrained(
                '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)

        __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(
            '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)
        self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')

        # Test image processor can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(__lowercase)
            __UpperCamelCase					:List[Any]        =		AutoImageProcessor.from_pretrained(__lowercase			,			trust_remote_code=__lowercase)
        self.assertEqual(reloaded_image_processor.__class__.__name__			,			'''NewImageProcessor''')







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        try:
            AutoConfig.register('''custom'''			,			__lowercase)
            AutoImageProcessor.register(__lowercase			,			__lowercase)
            # Trying to register something existing in the Transformers library will raise an error
            with self.assertRaises(__lowercase):
                AutoImageProcessor.register(__lowercase			,			__lowercase)

            with tempfile.TemporaryDirectory() as tmpdirname:
                __UpperCamelCase					:int        =		Path(__lowercase) / '''preprocessor_config.json'''
                __UpperCamelCase					:List[str]        =		Path(__lowercase) / '''config.json'''
                json.dump(
                    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
                json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

                __UpperCamelCase					:int        =		CustomImageProcessor.from_pretrained(__lowercase)

            # Now that the config is registered, it can be used as any other config with the auto-API
            with tempfile.TemporaryDirectory() as tmp_dir:
                image_processor.save_pretrained(__lowercase)
                __UpperCamelCase					:int        =		AutoImageProcessor.from_pretrained(__lowercase)
                self.assertIsInstance(__lowercase			,			__lowercase)

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







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


        class      lowerCamelCase_					(       UpperCAmelCase_			):






            '''simple docstring'''






            a__     :						List[str]      =				True

        try:
            AutoConfig.register('''custom'''			,			__lowercase)
            AutoImageProcessor.register(__lowercase			,			__lowercase)
            # If remote code is not set, the default is to use local
            __UpperCamelCase					:str        =		AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
            self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')
            self.assertTrue(image_processor.is_local)

            # If remote code is disabled, we load the local one.
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(
                '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)
            self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')
            self.assertTrue(image_processor.is_local)

            # If remote is enabled, we load from the Hub
            __UpperCamelCase					:List[str]        =		AutoImageProcessor.from_pretrained(
                '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)
            self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')
            self.assertTrue(not hasattr(__lowercase			,			'''is_local'''))

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





