



'''simple docstring'''




import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import BertGenerationDecoder, BertGenerationEncoder

class     UpperCamelCase_				:





	def __init__(						self	,	A	,	A=13	,	A=7	,	A=True	,	A=True	,	A=99	,	A=32	,	A=5	,	A=4	,	A=37	,	A="gelu"	,	A=0.1	,	A=0.1	,	A=50	,	A=0.0_2	,	A=True	,	A=None	,	)    ->    Optional[Any]:
								UpperCAmelCase  :	Any							  =	parent
								UpperCAmelCase  :	Union[str, Any]							  =	batch_size
								UpperCAmelCase  :	str							  =	seq_length
								UpperCAmelCase  :	Optional[int]							  =	is_training
								UpperCAmelCase  :	Optional[int]							  =	use_input_mask
								UpperCAmelCase  :	Tuple							  =	vocab_size
								UpperCAmelCase  :	Dict							  =	hidden_size
								UpperCAmelCase  :	Tuple							  =	num_hidden_layers
								UpperCAmelCase  :	List[Any]							  =	num_attention_heads
								UpperCAmelCase  :	str							  =	intermediate_size
								UpperCAmelCase  :	Union[str, Any]							  =	hidden_act
								UpperCAmelCase  :	List[str]							  =	hidden_dropout_prob
								UpperCAmelCase  :	List[Any]							  =	attention_probs_dropout_prob
								UpperCAmelCase  :	Any							  =	max_position_embeddings
								UpperCAmelCase  :	Dict							  =	initializer_range
								UpperCAmelCase  :	Any							  =	use_labels
								UpperCAmelCase  :	Optional[Any]							  =	scope





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	str							  =	ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size					)

								UpperCAmelCase  :	Any							  =	None
								if self.use_input_mask:
															UpperCAmelCase  :	Dict							  =	random_attention_mask([self.batch_size, self.seq_length]					)

								if self.use_labels:
															UpperCAmelCase  :	Any							  =	ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size					)

								UpperCAmelCase  :	Optional[Any]							  =	self.get_config()

								return config, input_ids, input_mask, token_labels





	def 				_lowercase(						self					)    ->    Any:
								return BertGenerationConfig(
								    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	,	is_decoder=A	,	initializer_range=self.initializer_range	,	)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								(
								    (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  
								)  :	Union[str, Any]							  =	self.prepare_config_and_inputs()

								UpperCAmelCase  :	List[Any]							  =	True
								UpperCAmelCase  :	int							  =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
								UpperCAmelCase  :	List[Any]							  =	ids_tensor([self.batch_size, self.seq_length]	,	vocab_size=2					)

								return (
								    config,
								    input_ids,
								    input_mask,
								    token_labels,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A	,	**A	,	)    ->    List[Any]:
								UpperCAmelCase  :	Optional[int]							  =	BertGenerationEncoder(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Dict							  =	model(A	,	attention_mask=A					)
								UpperCAmelCase  :	int							  =	model(A					)
								self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size)					)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A	,	A	,	A	,	**A	,	)    ->    Any:
								UpperCAmelCase  :	Any							  =	True
								UpperCAmelCase  :	Optional[Any]							  =	BertGenerationEncoder(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Tuple							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	encoder_attention_mask=A	,	)
								UpperCAmelCase  :	Dict							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	)
								self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size)					)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A	,	A	,	A	,	**A	,	)    ->    Dict:
								UpperCAmelCase  :	Optional[int]							  =	True
								UpperCAmelCase  :	Optional[int]							  =	True
								UpperCAmelCase  :	List[str]							  =	BertGenerationDecoder(config=A					).to(A					).eval()

								# first forward pass
								UpperCAmelCase  :	int							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	encoder_attention_mask=A	,	use_cache=A	,	)
								UpperCAmelCase  :	List[Any]							  =	outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								UpperCAmelCase  :	int							  =	ids_tensor((self.batch_size, 3)	,	config.vocab_size					)
								UpperCAmelCase  :	List[Any]							  =	ids_tensor((self.batch_size, 3)	,	vocab_size=2					)

								# append to next input_ids and
								UpperCAmelCase  :	List[str]							  =	torch.cat([input_ids, next_tokens]	,	dim=-1					)
								UpperCAmelCase  :	Any							  =	torch.cat([input_mask, next_mask]	,	dim=-1					)

								UpperCAmelCase  :	Optional[int]							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	encoder_attention_mask=A	,	output_hidden_states=A	,	)["""hidden_states"""][0]
								UpperCAmelCase  :	Dict							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	encoder_attention_mask=A	,	past_key_values=A	,	output_hidden_states=A	,	)["""hidden_states"""][0]

								# select random slice
								UpperCAmelCase  :	Tuple							  =	ids_tensor((1,)	,	output_from_past.shape[-1]					).item()
								UpperCAmelCase  :	Tuple							  =	output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase  :	List[str]							  =	output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(A	,	A	,	atol=1e-3					)					)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A	,	*A	,	)    ->    Optional[int]:
								UpperCAmelCase  :	int							  =	BertGenerationDecoder(A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	int							  =	model(A	,	attention_mask=A	,	labels=A					)
								self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.vocab_size)					)






	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	List[Any]							  =	self.prepare_config_and_inputs()
								UpperCAmelCase  :	int							  =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_torch
class     UpperCamelCase_				(							__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  unittest.TestCase    ):
	lowercase    =				(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
	lowercase    =				(BertGenerationDecoder,) if is_torch_available() else ()
	lowercase    =				(
	    {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
	    if is_torch_available()
	    else {}
	)





	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	int							  =	BertGenerationEncoderTester(self					)
								UpperCAmelCase  :	List[str]							  =	ConfigTester(self	,	config_class=A	,	hidden_size=37					)





	def 				_lowercase(						self					)    ->    int:
								self.config_tester.run_common_tests()





	def 				_lowercase(						self					)    ->    Optional[int]:
								UpperCAmelCase  :	Any							  =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A					)





	def 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	List[str]							  =	self.model_tester.prepare_config_and_inputs()
								UpperCAmelCase  :	str							  =	"""bert"""
								self.model_tester.create_and_check_model(A	,	A	,	A	,	A					)





	def 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	List[str]							  =	self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(*A					)





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	List[str]							  =	self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(*A					)





	def 				_lowercase(						self					)    ->    List[str]:
								# This regression test was failing with PyTorch < 1.3
								(
								    (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  
								)  :	str							  =	self.model_tester.prepare_config_and_inputs_for_decoder()

								UpperCAmelCase  :	Optional[Any]							  =	None

								self.model_tester.create_and_check_model_as_decoder(
								    A	,	A	,	A	,	A	,	A	,	A	,	)





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	int							  =	self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_for_causal_lm(*A					)






	@slow
	def 				_lowercase(						self					)    ->    Optional[int]:
								UpperCAmelCase  :	Optional[int]							  =	BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""					)
								self.assertIsNotNone(A					)




@require_torch
class     UpperCamelCase_				(							unittest.TestCase    ):





	@slow
	def 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	Optional[int]							  =	BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""					)
								UpperCAmelCase  :	str							  =	torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]					)
								with torch.no_grad():
															UpperCAmelCase  :	Tuple							  =	model(A					)[0]
								UpperCAmelCase  :	Optional[Any]							  =	torch.Size([1, 8, 1024]					)
								self.assertEqual(output.shape	,	A					)
								UpperCAmelCase  :	Optional[int]							  =	torch.tensor(
								    [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]]					)
								self.assertTrue(torch.allclose(output[:, :3, :3]	,	A	,	atol=1e-4					)					)




@require_torch
class     UpperCamelCase_				(							unittest.TestCase    ):





	@slow
	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	Dict							  =	BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""					)
								UpperCAmelCase  :	Dict							  =	torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]					)
								with torch.no_grad():
															UpperCAmelCase  :	Union[str, Any]							  =	model(A					)[0]
								UpperCAmelCase  :	str							  =	torch.Size([1, 8, 50358]					)
								self.assertEqual(output.shape	,	A					)
								UpperCAmelCase  :	List[str]							  =	torch.tensor(
								    [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]]					)
								self.assertTrue(torch.allclose(output[:, :3, :3]	,	A	,	atol=1e-4					)					)






'''simple docstring'''




import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def    __lowerCamelCase						(      _lowercase			,       _lowercase = True			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = False			,       _lowercase = 1_0_0			,       _lowercase = 0.01			,       _lowercase = 1			,       )   ->    Any:
							UpperCAmelCase  :	Optional[int]							  =	False
							UpperCAmelCase  :	Any							  =	search_prob
							UpperCAmelCase  :	Any							  =	start_temperate
							UpperCAmelCase  :	Optional[Any]							  =	[]
							UpperCAmelCase  :	Optional[Any]							  =	0
							UpperCAmelCase  :	Optional[Any]							  =	None

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

							if visualization:
														from matplotlib import pyplot as plt

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


if __name__ == "__main__":



							def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
														return (x**2) + (y**2)

							# starting the problem with initial coordinates (12, 47)
							a     :    Dict      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(
							    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, 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)
							a     :    List[str]      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(
							    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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						(      _lowercase			,       _lowercase	)   ->    Optional[Any]:
														return (3 * x**2) - (6 * y)

							a     :    Tuple      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Any      						=					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()}'''
							)

							a     :    List[Any]      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					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()}'''
							)

