import math
import random
from typing import Any

from .hill_climbing import SearchProblem




def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   bool = True   , lowerCAmelCase							:   float = math.inf   , lowerCAmelCase							:   float = -math.inf   , lowerCAmelCase							:   float = math.inf   , lowerCAmelCase							:   float = -math.inf   , lowerCAmelCase							:   bool = False   , lowerCAmelCase							:   float = 1_0_0   , lowerCAmelCase							:   float = 0.01   , lowerCAmelCase							:   float = 1   , ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    False
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    search_prob
		SCREAMING_SNAKE_CASE_  :		Dict				      =    start_temperate
		SCREAMING_SNAKE_CASE_  :		int				      =    []
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0
		SCREAMING_SNAKE_CASE_  :		Any				      =    None

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

		if visualization:
				from matplotlib import pyplot as plt

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


if __name__ == "__main__":




		def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Any	):
				"""simple docstring"""







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

		# starting the problem with initial coordinates (12, 47)
		__lowerCamelCase					:	List[str]       =  SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		__lowerCamelCase					:	List[Any]       =  simulated_annealing(
		    prob, find_max=False, max_x=1_00, 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)
		__lowerCamelCase					:	Any       =  SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		__lowerCamelCase					:	int       =  simulated_annealing(
		    prob, find_max=True, max_x=1_00, 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       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   Union[str, Any]	):
				"""simple docstring"""







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

		__lowerCamelCase					:	Any       =  SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		__lowerCamelCase					:	str       =  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					:	List[str]       =  SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		__lowerCamelCase					:	List[Any]       =  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 tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
					import os

					from flax.core.frozen_dict import unfreeze
					from flax.traverse_util import flatten_dict

					from transformers import FlaxBertModel

					A     :	int    = '0.12'  # assumed parallelism: 8







@require_flax
@is_staging_test
class 			__A( unittest.TestCase			):




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    TOKEN
								HfFolder.save_token(_snake_case				)




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls				)    ->      Union[str, Any]:


								'''simple docstring'''




								try:
												delete_repo(token=cls._token		,     repo_id='''test-model-flax'''				)
								except HTTPError:
												pass

								try:
												delete_repo(token=cls._token		,     repo_id='''valid_org/test-model-flax-org'''				)
								except HTTPError:
												pass




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    BertConfig(
								    vocab_size=99		,     hidden_size=32		,     num_hidden_layers=5		,     num_attention_heads=4		,     intermediate_size=37				)
								__a										=    FlaxBertModel(_snake_case				)
								model.push_to_hub('''test-model-flax'''		,     use_auth_token=self._token				)

								__a										=    FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""				)

								__a										=    flatten_dict(unfreeze(model.params				)				)
								__a										=    flatten_dict(unfreeze(new_model.params				)				)

								for key in base_params.keys():
												__a										=    (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(_snake_case		,     1E-3		,     msg=F"""{key} not identical"""				)

								# Reset repo
								delete_repo(token=self._token		,     repo_id='''test-model-flax'''				)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(_snake_case		,     repo_id='''test-model-flax'''		,     push_to_hub=_snake_case		,     use_auth_token=self._token				)

								__a										=    FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""				)

								__a										=    flatten_dict(unfreeze(model.params				)				)
								__a										=    flatten_dict(unfreeze(new_model.params				)				)

								for key in base_params.keys():
												__a										=    (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(_snake_case		,     1E-3		,     msg=F"""{key} not identical"""				)




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


								'''simple docstring'''




								__a										=    BertConfig(
								    vocab_size=99		,     hidden_size=32		,     num_hidden_layers=5		,     num_attention_heads=4		,     intermediate_size=37				)
								__a										=    FlaxBertModel(_snake_case				)
								model.push_to_hub('''valid_org/test-model-flax-org'''		,     use_auth_token=self._token				)

								__a										=    FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''				)

								__a										=    flatten_dict(unfreeze(model.params				)				)
								__a										=    flatten_dict(unfreeze(new_model.params				)				)

								for key in base_params.keys():
												__a										=    (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(_snake_case		,     1E-3		,     msg=F"""{key} not identical"""				)

								# Reset repo
								delete_repo(token=self._token		,     repo_id='''valid_org/test-model-flax-org'''				)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(
												    _snake_case		,     repo_id='''valid_org/test-model-flax-org'''		,     push_to_hub=_snake_case		,     use_auth_token=self._token				)

								__a										=    FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''				)

								__a										=    flatten_dict(unfreeze(model.params				)				)
								__a										=    flatten_dict(unfreeze(new_model.params				)				)

								for key in base_params.keys():
												__a										=    (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(_snake_case		,     1E-3		,     msg=F"""{key} not identical"""				)



def     __lowerCAmelCase			(			a__						,    a__  )       ->  str:
				__a										=    True
				__a										=    flatten_dict(modela.params  )
				__a										=    flatten_dict(modela.params  )
				for key in flat_params_a.keys():
								if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]  )  ) > 1e-4:
												__a										=    False

				return models_are_equal







@require_flax
class 			__A( unittest.TestCase			):




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


								'''simple docstring'''




								__a										=    BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)
								__a										=    FlaxBertModel(_snake_case				)

								__a										=    '''bert'''
								with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(os.path.join(_snake_case		,     _snake_case				)				)

												with self.assertRaises(_snake_case				):
																__a										=    FlaxBertModel.from_pretrained(_snake_case				)

												__a										=    FlaxBertModel.from_pretrained(_snake_case		,     subfolder=_snake_case				)

								self.assertTrue(check_models_equal(_snake_case		,     _snake_case				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)
								__a										=    FlaxBertModel(_snake_case				)

								__a										=    '''bert'''
								with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(os.path.join(_snake_case		,     _snake_case				)		,     max_shard_size='''10KB'''				)

												with self.assertRaises(_snake_case				):
																__a										=    FlaxBertModel.from_pretrained(_snake_case				)

												__a										=    FlaxBertModel.from_pretrained(_snake_case		,     subfolder=_snake_case				)

								self.assertTrue(check_models_equal(_snake_case		,     _snake_case				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    '''bert'''
								__a										=    '''hf-internal-testing/tiny-random-bert-subfolder'''

								with self.assertRaises(_snake_case				):
												__a										=    FlaxBertModel.from_pretrained(_snake_case				)

								__a										=    FlaxBertModel.from_pretrained(_snake_case		,     subfolder=_snake_case				)

								self.assertIsNotNone(_snake_case				)




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


								'''simple docstring'''




								__a										=    '''bert'''
								__a										=    '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
								with self.assertRaises(_snake_case				):
												__a										=    FlaxBertModel.from_pretrained(_snake_case				)

								__a										=    FlaxBertModel.from_pretrained(_snake_case		,     subfolder=_snake_case				)

								self.assertIsNotNone(_snake_case				)