

import math

class  a     :


						def __init__(				self  ,    A_  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			x
										_UpperCAmelCase				:						Tuple       						=			y
										_UpperCAmelCase				:						List[str]       						=			step_size
										_UpperCAmelCase				:						Any       						=			function_to_optimize


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return self.function(self.x  ,    self.y		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			self.step_size
										return [
										    SearchProblem(lowerCamelCase__  ,    lowerCamelCase__  ,    lowerCamelCase__  ,    self.function		)
										    for x, y in (
										        (self.x - step_size, self.y - step_size),
										        (self.x - step_size, self.y),
										        (self.x - step_size, self.y + step_size),
										        (self.x, self.y - step_size),
										        (self.x, self.y + step_size),
										        (self.x + step_size, self.y - step_size),
										        (self.x + step_size, self.y),
										        (self.x + step_size, self.y + step_size),
										    )
										]


						def __hash__(				self		):







										'''simple docstring'''


										return hash(str(self		)		)


						def __eq__(				self  ,    A_		):







										'''simple docstring'''


										if isinstance(lowerCamelCase__  ,    lowerCamelCase__		):
														return hash(str(self		)		) == hash(str(lowerCamelCase__		)		)
										return False





						def __str__(				self		):







										'''simple docstring'''


										return f'x: {self.x} y: {self.y}'







def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Any     ,					lowerCAmelCase:     bool = True     ,					lowerCAmelCase:     float = math.inf     ,					lowerCAmelCase:     float = -math.inf     ,					lowerCAmelCase:     float = math.inf     ,					lowerCAmelCase:     float = -math.inf     ,					lowerCAmelCase:     bool = False     ,					lowerCAmelCase:     int = 1_0000     ,					)      ->				SearchProblem:
				_UpperCAmelCase				:						List[Any]       						=			search_prob
				_UpperCAmelCase				:						Optional[Any]       						=			[]  # list to store the current score at each iteration
				_UpperCAmelCase				:						Optional[Any]       						=			0
				_UpperCAmelCase				:						Dict       						=			False
				_UpperCAmelCase				:						Union[str, Any]       						=			set()
				while not solution_found and iterations < max_iter:
								visited.add(lowercase_       )
								iterations += 1
								_UpperCAmelCase				:						Optional[int]       						=			current_state.score()
								scores.append(lowercase_       )
								_UpperCAmelCase				:						str       						=			current_state.get_neighbors()
								_UpperCAmelCase				:						Optional[Any]       						=			-math.inf
								_UpperCAmelCase				:						List[Any]       						=			math.inf
								_UpperCAmelCase				:						Optional[Any]       						=			None  # to hold the next best neighbor
								for neighbor in neighbors:
												if neighbor in visited:
																continue  # do not want to visit the same state again
												if (
												    neighbor.x > max_x
												    or neighbor.x < min_x
												    or neighbor.y > max_y
												    or neighbor.y < min_y
												):
																continue  # neighbor outside our bounds
												_UpperCAmelCase				:						str       						=			neighbor.score() - current_score
												if find_max:  # finding max
																# going to direction with greatest ascent
																if change > max_change and change > 0:
																				_UpperCAmelCase				:						List[Any]       						=			change
																				_UpperCAmelCase				:						List[Any]       						=			neighbor
												else:  # finding min
																# to direction with greatest descent
																if change < min_change and change < 0:
																				_UpperCAmelCase				:						Optional[Any]       						=			change
																				_UpperCAmelCase				:						str       						=			neighbor
								if next_state is not None:
												# we found at least one neighbor which improved the current state
												_UpperCAmelCase				:						Optional[int]       						=			next_state
								else:
												# since we have no neighbor that improves the solution we stop the search
												_UpperCAmelCase				:						int       						=			True

				if visualization:
								from matplotlib import pyplot as plt

								plt.plot(range(lowercase_       )     ,					lowercase_       )
								plt.xlabel("Iterations"       )
								plt.ylabel("Function values"       )
								plt.show()

				return current_state


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Union[str, Any]     ,					lowerCAmelCase:     List[Any]       )      ->				List[Any]:
									return (x**2) + (y**2)

					# starting the problem with initial coordinates (3, 4)
					SCREAMING_SNAKE_CASE_						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE_						=					hill_climbing(prob, find_max=False)
					print(
					    'The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '
					    F'''{local_min.score()}'''
					)

					# starting the problem with initial coordinates (12, 47)
					SCREAMING_SNAKE_CASE_						=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE_						=					hill_climbing(
					    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()}'''
					)

					def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Optional[int]     ,					lowerCAmelCase:     Dict       )      ->				List[Any]:
									return (3 * x**2) - (6 * y)

					SCREAMING_SNAKE_CASE_						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE_						=					hill_climbing(prob, find_max=True)





					print(
					    'The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '
					    F'''{local_min.score()}'''
					)







"""simple docstring"""
import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    ErnieMForInformationExtraction,
			    ErnieMForMultipleChoice,
			    ErnieMForQuestionAnswering,
			    ErnieMForSequenceClassification,
			    ErnieMForTokenClassification,
			    ErnieMModel,
			)
			from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=1_3 ,				lowerCamelCase__=7 ,				lowerCamelCase__=True ,				lowerCamelCase__=True ,				lowerCamelCase__=True ,				lowerCamelCase__=9_9 ,				lowerCamelCase__=3_2 ,				lowerCamelCase__=5 ,				lowerCamelCase__=4 ,				lowerCamelCase__=3_7 ,				lowerCamelCase__="gelu" ,				lowerCamelCase__=0.1 ,				lowerCamelCase__=0.1 ,				lowerCamelCase__=5_1_2 ,				lowerCamelCase__=1_6 ,				lowerCamelCase__=2 ,				lowerCamelCase__=0.0_2 ,				lowerCamelCase__=3 ,				lowerCamelCase__=4 ,				lowerCamelCase__=None ,				):
									_lowerCamelCase					     = parent
									_lowerCamelCase					     = batch_size
									_lowerCamelCase					     = seq_length
									_lowerCamelCase					     = is_training
									_lowerCamelCase					     = use_input_mask
									_lowerCamelCase					     = use_labels
									_lowerCamelCase					     = vocab_size
									_lowerCamelCase					     = hidden_size
									_lowerCamelCase					     = num_hidden_layers
									_lowerCamelCase					     = num_attention_heads
									_lowerCamelCase					     = intermediate_size
									_lowerCamelCase					     = hidden_act
									_lowerCamelCase					     = hidden_dropout_prob
									_lowerCamelCase					     = attention_probs_dropout_prob
									_lowerCamelCase					     = max_position_embeddings
									_lowerCamelCase					     = type_vocab_size
									_lowerCamelCase					     = type_sequence_label_size
									_lowerCamelCase					     = initializer_range
									_lowerCamelCase					     = num_labels
									_lowerCamelCase					     = num_choices
									_lowerCamelCase					     = scope

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

									_lowerCamelCase					     = None
									if self.use_input_mask:
													_lowerCamelCase					     = random_attention_mask([self.batch_size, self.seq_length]      )

									_lowerCamelCase					     = None
									_lowerCamelCase					     = None
									_lowerCamelCase					     = None
									if self.use_labels:
													_lowerCamelCase					     = ids_tensor([self.batch_size] ,				self.type_sequence_label_size      )
													_lowerCamelCase					     = ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels      )
													_lowerCamelCase					     = ids_tensor([self.batch_size] ,				self.num_choices      )

									_lowerCamelCase					     = self.get_config()

									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

									_lowerCamelCase					     = None
									if self.use_input_mask:
													_lowerCamelCase					     = random_attention_mask([self.batch_size, self.seq_length]      )
									_lowerCamelCase					     = self.get_config()

									return config, input_ids, input_mask

					def 	snake_case__					(			self      ):
									return ErnieMConfig(
									    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 ,				initializer_range=self.initializer_range ,				)

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = ErnieMModel(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = model(lowerCamelCase__ ,				return_dict=lowerCamelCase__      )
									self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = ErnieMForQuestionAnswering(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = model(
									    lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				start_positions=lowerCamelCase__ ,				end_positions=lowerCamelCase__ ,				)
									self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)      )
									self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = ErnieMForInformationExtraction(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = torch.ones_like(lowerCamelCase__ ,				dtype=torch.floataa      )
									_lowerCamelCase					     = model(
									    lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				start_positions=lowerCamelCase__ ,				end_positions=lowerCamelCase__ ,				)
									self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)      )
									self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.num_labels
									_lowerCamelCase					     = ErnieMForSequenceClassification(lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = model(lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				labels=lowerCamelCase__      )
									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.num_labels
									_lowerCamelCase					     = ErnieMForTokenClassification(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									input_ids.to(lowerCamelCase__      )
									input_mask.to(lowerCamelCase__      )
									token_labels.to(lowerCamelCase__      )

									_lowerCamelCase					     = model(lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				labels=lowerCamelCase__      )

									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.num_choices
									_lowerCamelCase					     = ErnieMForMultipleChoice(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = input_ids.unsqueeze(1      ).expand(-1 ,				self.num_choices ,				-1      ).contiguous()
									_lowerCamelCase					     = input_mask.unsqueeze(1      ).expand(-1 ,				self.num_choices ,				-1      ).contiguous()
									_lowerCamelCase					     = model(
									    lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				labels=lowerCamelCase__ ,				)
									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices)      )





					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.prepare_config_and_inputs()
									(
									    (
									    _lowerCamelCase
									)     ,   (
									    _lowerCamelCase
									)     ,   (
									    _lowerCamelCase
									)     ,   (
									    _lowerCamelCase
									)     ,   (
									    _lowerCamelCase
									)     ,   (
									    _lowerCamelCase
									)     ,   
									)					     = config_and_inputs
									_lowerCamelCase					     = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
									return config, inputs_dict


@require_torch
class 							lowerCamelCase_(      A__, A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				Optional[Any]        =       (
					    (
					        ErnieMModel,
					        ErnieMForMultipleChoice,
					        ErnieMForQuestionAnswering,
					        ErnieMForSequenceClassification,
					        ErnieMForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					lowercase__      :				List[Any]        =       ()
					lowercase__      :				Union[str, Any]        =       (
					    {
					        'feature-extraction': ErnieMModel,
					        'question-answering': ErnieMForQuestionAnswering,
					        'text-classification': ErnieMForSequenceClassification,
					        'token-classification': ErnieMForTokenClassification,
					        'zero-shot': ErnieMForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					lowercase__      :				Optional[int]        =       False

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									if pipeline_test_casse_name == "QAPipelineTests":
													return True

									return False

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = ErnieMModelTester(self      )
									_lowerCamelCase					     = ConfigTester(self ,				config_class=lowerCamelCase__ ,				hidden_size=3_7      )

					def 	snake_case__					(			self      ):
									self.config_tester.run_common_tests()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
													_lowerCamelCase					     = type
													self.model_tester.create_and_check_model(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_information_extraction(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__      )





					@slow
					def 	snake_case__					(			self      ):
									for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowerCamelCase					     = ErnieMModel.from_pretrained(lowerCamelCase__      )
													self.assertIsNotNone(lowerCamelCase__      )


@require_torch
class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



					@slow
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = ErnieMModel.from_pretrained('''susnato/ernie-m-base_pytorch'''      )
									model.eval()
									_lowerCamelCase					     = torch.tensor([[0, 1, 2, 3, 4, 5]]      )
									_lowerCamelCase					     = model(lowerCamelCase__      )[0]

									# TODO Replace vocab size
									_lowerCamelCase					     = 7_6_8

									_lowerCamelCase					     = torch.Size((1, 6, hidden_size)      )
									self.assertEqual(output.shape ,				lowerCamelCase__      )

									_lowerCamelCase					     = torch.tensor(
									    [[[-0.0_0_1_2, 0.1_2_4_5, -0.0_2_1_4], [-0.0_7_4_2, 0.0_2_4_4, -0.0_7_7_1], [-0.0_3_3_3, 0.1_1_6_4, -0.1_5_5_4]]]      )

									self.assertTrue(torch.allclose(output[:, :3, :3] ,				lowerCamelCase__ ,				atol=1e-3      )      )
