
'''simple docstring'''






import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def 		A_       (	_lowerCAmelCase   :    Union[str, Any]				,	_lowerCAmelCase   :    bool = True				,	_lowerCAmelCase   :    float = math.inf				,	_lowerCAmelCase   :    float = -math.inf				,	_lowerCAmelCase   :    float = math.inf				,	_lowerCAmelCase   :    float = -math.inf				,	_lowerCAmelCase   :    bool = False				,	_lowerCAmelCase   :    float = 100				,	_lowerCAmelCase   :    float = 0.0_1				,	_lowerCAmelCase   :    float = 1				,	):



      """simple docstring"""




      _lowerCamelCase				:   int								=   False
      _lowerCamelCase				:   Optional[int]								=   search_prob
      _lowerCamelCase				:   Dict								=   start_temperate
      _lowerCamelCase				:   List[str]								=   []
      _lowerCamelCase				:   Optional[Any]								=   0
      _lowerCamelCase				:   int								=   None

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



       """simple docstring"""




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

 # starting the problem with initial coordinates (12, 47)
 UpperCAmelCase_       :   str   =       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 UpperCAmelCase_       :   List[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)
 UpperCAmelCase_       :   Optional[Any]   =       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 UpperCAmelCase_       :   Dict   =       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 		A_       (	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    Dict							):



       """simple docstring"""




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

 UpperCAmelCase_       :   Union[str, Any]   =       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 UpperCAmelCase_       :   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()}'''
 )

 UpperCAmelCase_       :   str   =       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 UpperCAmelCase_       :   str   =       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 unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.albert.modeling_flax_albert import (
     FlaxAlbertForMaskedLM,
     FlaxAlbertForMultipleChoice,
     FlaxAlbertForPreTraining,
     FlaxAlbertForQuestionAnswering,
     FlaxAlbertForSequenceClassification,
     FlaxAlbertForTokenClassification,
     FlaxAlbertModel,
 )

class  UpperCAmelCase__						(       unittest.TestCase ):



    def __init__(	self      :       Union[str, Any],__A      :       Union[str, Any],__A      :       Optional[int]=1_3,__A      :       Optional[Any]=7,__A      :       int=True,__A      :       Any=True,__A      :       Dict=True,__A      :       List[Any]=True,__A      :       Optional[Any]=9_9,__A      :       List[str]=3_2,__A      :       int=5,__A      :       str=4,__A      :       int=3_7,__A      :       Optional[Any]="gelu",__A      :       Union[str, Any]=0.1,__A      :       str=0.1,__A      :       Any=5_1_2,__A      :       Any=1_6,__A      :       Optional[int]=2,__A      :       int=0.02,__A      :       Optional[int]=4,):
          _lowerCamelCase				:   Union[str, Any]								=   parent
          _lowerCamelCase				:   Tuple								=   batch_size
          _lowerCamelCase				:   List[str]								=   seq_length
          _lowerCamelCase				:   Optional[int]								=   is_training
          _lowerCamelCase				:   str								=   use_attention_mask
          _lowerCamelCase				:   str								=   use_token_type_ids
          _lowerCamelCase				:   List[str]								=   use_labels
          _lowerCamelCase				:   Tuple								=   vocab_size
          _lowerCamelCase				:   Dict								=   hidden_size
          _lowerCamelCase				:   str								=   num_hidden_layers
          _lowerCamelCase				:   List[str]								=   num_attention_heads
          _lowerCamelCase				:   Optional[Any]								=   intermediate_size
          _lowerCamelCase				:   Optional[int]								=   hidden_act
          _lowerCamelCase				:   Any								=   hidden_dropout_prob
          _lowerCamelCase				:   List[Any]								=   attention_probs_dropout_prob
          _lowerCamelCase				:   Tuple								=   max_position_embeddings
          _lowerCamelCase				:   str								=   type_vocab_size
          _lowerCamelCase				:   str								=   type_sequence_label_size
          _lowerCamelCase				:   int								=   initializer_range
          _lowerCamelCase				:   List[Any]								=   num_choices



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[Any]								=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size     )

          _lowerCamelCase				:   Union[str, Any]								=   None
          if self.use_attention_mask:
                _lowerCamelCase				:   Tuple								=   random_attention_mask([self.batch_size, self.seq_length]     )

          _lowerCamelCase				:   Any								=   None
          if self.use_token_type_ids:
                _lowerCamelCase				:   int								=   ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size     )

          _lowerCamelCase				:   int								=   AlbertConfig(
              vocab_size=self.vocab_size,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,is_decoder=__A,initializer_range=self.initializer_range,)

          return config, input_ids, token_type_ids, attention_mask



    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   str								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   config_and_inputs
          _lowerCamelCase				:   Any								=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict


@require_flax
class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		(
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   List[str]								=   FlaxAlbertModelTester(self     )



    @slow
    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase				:   str								=   model_class_name.from_pretrained("albert-base-v2"     )
                _lowerCamelCase				:   Any								=   model(np.ones((1, 1)     )     )
                self.assertIsNotNone(__A     )


@require_flax
class  UpperCAmelCase__						(       unittest.TestCase ):



    @slow
    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Optional[int]								=   FlaxAlbertModel.from_pretrained("albert-base-v2"     )
          _lowerCamelCase				:   int								=   np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]     )
          _lowerCamelCase				:   Union[str, Any]								=   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
          _lowerCamelCase				:   Optional[Any]								=   model(__A,attention_mask=__A     )[0]
          _lowerCamelCase				:   Dict								=   (1, 1_1, 7_6_8)
          self.assertEqual(output.shape,__A     )
          _lowerCamelCase				:   Tuple								=   np.array(
              [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]     )

          self.assertTrue(jnp.allclose(output[:, 1:4, 1:4],__A,atol=1e-4     )     )