"""simple docstring"""



import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__lowercase		:		int														=							get_tests_dir("fixtures")
__lowercase		:		str														=							get_tests_dir("fixtures/dummy_feature_extractor_config.json")
__lowercase		:		List[Any]														=							get_tests_dir("fixtures/dummy-config.json")



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Dict    )    ->       str:
         __snake_case				=							0
    def  lowercase					(		self							:     Dict    )    ->       Any:
         __snake_case				=							AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''    )
         self.assertIsInstance(A_	,      A_    )
    def  lowercase					(		self							:     List[str]    )    ->       str:
         __snake_case				=							AutoFeatureExtractor.from_pretrained(A_    )
         self.assertIsInstance(A_	,      A_    )
    def  lowercase					(		self							:     Dict    )    ->       List[str]:
         with tempfile.TemporaryDirectory() as tmpdirname:
              __snake_case				=							WavaVecaConfig()

              # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
              __snake_case				=							AutoFeatureExtractor.from_pretrained(A_    ).to_dict()

              config_dict.pop('''feature_extractor_type'''    )
              __snake_case				=							WavaVecaFeatureExtractor(**A_    )

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

              __snake_case				=							AutoFeatureExtractor.from_pretrained(A_    )

              # make sure private variable is not incorrectly saved
              __snake_case				=							json.loads(config.to_json_string()    )
              self.assertTrue('''_processor_class''' not in dict_as_saved    )

         self.assertIsInstance(A_	,      A_    )
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case				=							AutoFeatureExtractor.from_pretrained(A_    )
         self.assertIsInstance(A_	,      A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       Dict:
         with self.assertRaisesRegex(
             A_	,      '''bert-base is not a local folder and is not a valid model identifier'''    ):
              __snake_case				=							AutoFeatureExtractor.from_pretrained('''bert-base'''    )
    def  lowercase					(		self							:     str    )    ->       List[str]:
         with self.assertRaisesRegex(
             A_	,      R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''    ):
              __snake_case				=							AutoFeatureExtractor.from_pretrained(A_	,      revision='''aaaaaa'''    )
    def  lowercase					(		self							:     List[str]    )    ->       Any:
         with self.assertRaisesRegex(
             A_	,      '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''	,      ):
              __snake_case				=							AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''    )
    def  lowercase					(		self							:     int    )    ->       int:
         # If remote code is not set, we will time out when asking whether to load the model.
         with self.assertRaises(A_    ):
              __snake_case				=							AutoFeatureExtractor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_feature_extractor'''    )
         # If remote code is disabled, we can't load this config.
         with self.assertRaises(A_    ):
              __snake_case				=							AutoFeatureExtractor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_feature_extractor'''	,      trust_remote_code=A_    )

         __snake_case				=							AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''	,      trust_remote_code=A_    )
         self.assertEqual(feature_extractor.__class__.__name__	,      '''NewFeatureExtractor'''    )

         # Test feature extractor can be reloaded.
         with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(A_    )
              __snake_case				=							AutoFeatureExtractor.from_pretrained(A_	,      trust_remote_code=A_    )
         self.assertEqual(reloaded_feature_extractor.__class__.__name__	,      '''NewFeatureExtractor'''    )
    def  lowercase					(		self							:     Optional[int]    )    ->       List[Any]:
         try:
              AutoConfig.register('''custom'''	,      A_    )
              AutoFeatureExtractor.register(A_	,      A_    )
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(A_    ):
                   AutoFeatureExtractor.register(A_	,      A_    )

              # Now that the config is registered, it can be used as any other config with the auto-API
              __snake_case				=							CustomFeatureExtractor.from_pretrained(A_    )
              with tempfile.TemporaryDirectory() as tmp_dir:
                   feature_extractor.save_pretrained(A_    )
                   __snake_case				=							AutoFeatureExtractor.from_pretrained(A_    )
                   self.assertIsInstance(A_	,      A_    )

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







    def  lowercase					(		self							:     List[str]    )    ->       Optional[Any]:



         class      _A	(						_UpperCAmelCase					):




             """simple docstring"""




             UpperCamelCase_	:					Optional[int]							=					True

         try:
              AutoConfig.register('''custom'''	,      A_    )
              AutoFeatureExtractor.register(A_	,      A_    )
              # If remote code is not set, the default is to use local
              __snake_case				=							AutoFeatureExtractor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_feature_extractor'''    )
              self.assertEqual(feature_extractor.__class__.__name__	,      '''NewFeatureExtractor'''    )
              self.assertTrue(feature_extractor.is_local    )

              # If remote code is disabled, we load the local one.
              __snake_case				=							AutoFeatureExtractor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_feature_extractor'''	,      trust_remote_code=A_    )
              self.assertEqual(feature_extractor.__class__.__name__	,      '''NewFeatureExtractor'''    )
              self.assertTrue(feature_extractor.is_local    )

              # If remote is enabled, we load from the Hub
              __snake_case				=							AutoFeatureExtractor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_feature_extractor'''	,      trust_remote_code=A_    )
              self.assertEqual(feature_extractor.__class__.__name__	,      '''NewFeatureExtractor'''    )
              self.assertTrue(not hasattr(A_	,      '''is_local'''    )    )

         finally:
              if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
              if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                   del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
"""simple docstring"""



import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case = True,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = False,      snake_case = 1_00,      snake_case = 0.01,      snake_case = 1,      ):
     __snake_case				=							False
     __snake_case				=							search_prob
     __snake_case				=							start_temperate
     __snake_case				=							[]
     __snake_case				=							0
     __snake_case				=							None

     while not search_end:
          __snake_case				=							current_state.score()
          if best_state is None or current_score > best_state.score():
               __snake_case				=							current_state
          scores.append(snake_case)
          iterations += 1
          __snake_case				=							None
          __snake_case				=							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				=							random.randint(0,      len(snake_case) - 1)  # picking a random neighbor
               __snake_case				=							neighbors.pop(snake_case)
               __snake_case				=							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				=							change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    __snake_case				=							picked_neighbor
               else:
                    __snake_case				=							(math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         __snake_case				=							picked_neighbor
          __snake_case				=							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				=							True
          else:
               __snake_case				=							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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (x**2) + (y**2)

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Dict														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Union[str, Any]														=							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		:		Any														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		int														=							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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (3 * x**2) - (6 * y)

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