




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      : int							,    lowerCamelCase_      : List[str]							,    lowerCamelCase_      : Optional[Any]=13							,    lowerCamelCase_      : List[Any]=7							,    lowerCamelCase_      : Tuple=True							,    lowerCamelCase_      : Optional[int]=True							,    lowerCamelCase_      : List[Any]=True							,    lowerCamelCase_      : str=99							,    lowerCamelCase_      : List[Any]=32							,    lowerCamelCase_      : str=5							,    lowerCamelCase_      : Optional[Any]=4							,    lowerCamelCase_      : int=37							,    lowerCamelCase_      : Any="gelu"							,    lowerCamelCase_      : Union[str, Any]=0.1							,    lowerCamelCase_      : Any=0.1							,    lowerCamelCase_      : int=512							,    lowerCamelCase_      : List[str]=16							,    lowerCamelCase_      : List[str]=2							,    lowerCamelCase_      : Optional[Any]=0.0_2							,    lowerCamelCase_      : int=3							,    lowerCamelCase_      : Dict=4							,    lowerCamelCase_      : Optional[int]=None							,    )    -> Tuple:
								__magic_name__				:						Optional[int]     				=				parent
								__magic_name__				:						int     				=				batch_size
								__magic_name__				:						List[str]     				=				seq_length
								__magic_name__				:						Optional[int]     				=				is_training
								__magic_name__				:						Optional[Any]     				=				use_input_mask
								__magic_name__				:						List[Any]     				=				use_labels
								__magic_name__				:						Optional[int]     				=				vocab_size
								__magic_name__				:						Tuple     				=				hidden_size
								__magic_name__				:						Optional[Any]     				=				num_hidden_layers
								__magic_name__				:						Tuple     				=				num_attention_heads
								__magic_name__				:						Dict     				=				intermediate_size
								__magic_name__				:						int     				=				hidden_act
								__magic_name__				:						Optional[int]     				=				hidden_dropout_prob
								__magic_name__				:						List[Any]     				=				attention_probs_dropout_prob
								__magic_name__				:						Any     				=				max_position_embeddings
								__magic_name__				:						List[str]     				=				type_vocab_size
								__magic_name__				:						Optional[Any]     				=				type_sequence_label_size
								__magic_name__				:						List[Any]     				=				initializer_range
								__magic_name__				:						Tuple     				=				num_labels
								__magic_name__				:						str     				=				num_choices
								__magic_name__				:						Optional[Any]     				=				scope

			def 				UpperCAmelCase__							(			self      : int	)    -> int:
								__magic_name__				:						Optional[int]     				=				ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size	)

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

								__magic_name__				:						Optional[Any]     				=				None
								__magic_name__				:						List[Any]     				=				None
								__magic_name__				:						Optional[int]     				=				None
								if self.use_labels:
													__magic_name__				:						int     				=				ids_tensor([self.batch_size]							,    self.type_sequence_label_size	)
													__magic_name__				:						Optional[int]     				=				ids_tensor([self.batch_size, self.seq_length]							,    self.num_labels	)
													__magic_name__				:						List[str]     				=				ids_tensor([self.batch_size]							,    self.num_choices	)

								__magic_name__				:						str     				=				self.get_config()

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

			def 				UpperCAmelCase__							(			self      : Optional[Any]	)    -> Optional[int]:
								__magic_name__				:						Any     				=				ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size	)

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

								return config, input_ids, input_mask

			def 				UpperCAmelCase__							(			self      : int	)    -> List[str]:
								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 				UpperCAmelCase__							(			self      : Any							,    lowerCamelCase_      : Tuple							,    lowerCamelCase_      : int							,    lowerCamelCase_      : Optional[int]							,    lowerCamelCase_      : Union[str, Any]							,    lowerCamelCase_      : str							,    lowerCamelCase_      : str	)    -> Any:
								__magic_name__				:						Optional[Any]     				=				ErnieMModel(config=lowerCamelCase_	)
								model.to(lowerCamelCase_	)
								model.eval()
								__magic_name__				:						Optional[int]     				=				model(lowerCamelCase_							,    return_dict=lowerCamelCase_	)
								self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)	)

			def 				UpperCAmelCase__							(			self      : Tuple							,    lowerCamelCase_      : Any							,    lowerCamelCase_      : Tuple							,    lowerCamelCase_      : Union[str, Any]							,    lowerCamelCase_      : Union[str, Any]							,    lowerCamelCase_      : Dict							,    lowerCamelCase_      : str	)    -> Union[str, Any]:
								__magic_name__				:						Tuple     				=				ErnieMForQuestionAnswering(config=lowerCamelCase_	)
								model.to(lowerCamelCase_	)
								model.eval()
								__magic_name__				:						List[str]     				=				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 				UpperCAmelCase__							(			self      : List[Any]							,    lowerCamelCase_      : List[str]							,    lowerCamelCase_      : Any							,    lowerCamelCase_      : Any							,    lowerCamelCase_      : Optional[Any]							,    lowerCamelCase_      : Optional[Any]							,    lowerCamelCase_      : Optional[Any]	)    -> Any:
								__magic_name__				:						Any     				=				ErnieMForInformationExtraction(config=lowerCamelCase_	)
								model.to(lowerCamelCase_	)
								model.eval()
								__magic_name__				:						List[Any]     				=				torch.ones_like(lowerCamelCase_							,    dtype=torch.floataa	)
								__magic_name__				:						List[Any]     				=				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 				UpperCAmelCase__							(			self      : str							,    lowerCamelCase_      : List[str]							,    lowerCamelCase_      : List[str]							,    lowerCamelCase_      : List[Any]							,    lowerCamelCase_      : List[str]							,    lowerCamelCase_      : Any							,    lowerCamelCase_      : Any	)    -> Optional[int]:
								__magic_name__				:						Optional[int]     				=				self.num_labels
								__magic_name__				:						Optional[Any]     				=				ErnieMForSequenceClassification(lowerCamelCase_	)
								model.to(lowerCamelCase_	)
								model.eval()
								__magic_name__				:						Union[str, Any]     				=				model(lowerCamelCase_							,    attention_mask=lowerCamelCase_							,    labels=lowerCamelCase_	)
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)	)

			def 				UpperCAmelCase__							(			self      : Tuple							,    lowerCamelCase_      : Any							,    lowerCamelCase_      : Any							,    lowerCamelCase_      : int							,    lowerCamelCase_      : Optional[Any]							,    lowerCamelCase_      : List[Any]							,    lowerCamelCase_      : int	)    -> List[str]:
								__magic_name__				:						List[Any]     				=				self.num_labels
								__magic_name__				:						int     				=				ErnieMForTokenClassification(config=lowerCamelCase_	)
								model.to(lowerCamelCase_	)
								model.eval()
								input_ids.to(lowerCamelCase_	)
								input_mask.to(lowerCamelCase_	)
								token_labels.to(lowerCamelCase_	)

								__magic_name__				:						List[Any]     				=				model(lowerCamelCase_							,    attention_mask=lowerCamelCase_							,    labels=lowerCamelCase_	)

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

			def 				UpperCAmelCase__							(			self      : List[str]							,    lowerCamelCase_      : Union[str, Any]							,    lowerCamelCase_      : Any							,    lowerCamelCase_      : Union[str, Any]							,    lowerCamelCase_      : List[Any]							,    lowerCamelCase_      : List[Any]							,    lowerCamelCase_      : Any	)    -> Dict:
								__magic_name__				:						str     				=				self.num_choices
								__magic_name__				:						Union[str, Any]     				=				ErnieMForMultipleChoice(config=lowerCamelCase_	)
								model.to(lowerCamelCase_	)
								model.eval()
								__magic_name__				:						Dict     				=				input_ids.unsqueeze(1	).expand(-1							,    self.num_choices							,    -1	).contiguous()
								__magic_name__				:						Optional[Any]     				=				input_mask.unsqueeze(1	).expand(-1							,    self.num_choices							,    -1	).contiguous()
								__magic_name__				:						List[str]     				=				model(
								    lowerCamelCase_							,    attention_mask=lowerCamelCase_							,    labels=lowerCamelCase_							,    )
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_choices)	)


			def 				UpperCAmelCase__							(			self      : Tuple	)    -> Dict:
								__magic_name__				:						Any     				=				self.prepare_config_and_inputs()
								(
								    (
								    __magic_name__
								)				,     (
								    __magic_name__
								)				,     (
								    __magic_name__
								)				,     (
								    __magic_name__
								)				,     (
								    __magic_name__
								)				,     (
								    __magic_name__
								)				,     
								)				:						List[Any]     				=				config_and_inputs
								__magic_name__				:						str     				=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict






@require_torch
class  lowerCamelCase     (		_lowerCamelCase					,_lowerCamelCase					,unittest.TestCase    ):






			'''simple docstring'''





			UpperCamelCase__										=(
			    (
			        ErnieMModel,
			        ErnieMForMultipleChoice,
			        ErnieMForQuestionAnswering,
			        ErnieMForSequenceClassification,
			        ErnieMForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			UpperCamelCase__										=()
			UpperCamelCase__										=(
			    {
			        '''feature-extraction''': ErnieMModel,
			        '''question-answering''': ErnieMForQuestionAnswering,
			        '''text-classification''': ErnieMForSequenceClassification,
			        '''token-classification''': ErnieMForTokenClassification,
			        '''zero-shot''': ErnieMForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase__										=False

			def 				UpperCAmelCase__							(			self      : Optional[int]							,    lowerCamelCase_      : List[Any]							,    lowerCamelCase_      : Optional[int]							,    lowerCamelCase_      : Dict							,    lowerCamelCase_      : Union[str, Any]							,    lowerCamelCase_      : List[Any]	)    -> Optional[Any]:
								if pipeline_test_casse_name == "QAPipelineTests":
													return True

								return False

			def 				UpperCAmelCase__							(			self      : Optional[Any]	)    -> Any:
								__magic_name__				:						Tuple     				=				ErnieMModelTester(self	)
								__magic_name__				:						List[Any]     				=				ConfigTester(self							,    config_class=lowerCamelCase_							,    hidden_size=37	)

			def 				UpperCAmelCase__							(			self      : Dict	)    -> List[Any]:
								self.config_tester.run_common_tests()

			def 				UpperCAmelCase__							(			self      : Dict	)    -> List[Any]:
								__magic_name__				:						Tuple     				=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : Dict	)    -> str:
								__magic_name__				:						Tuple     				=				self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
													__magic_name__				:						Union[str, Any]     				=				type
													self.model_tester.create_and_check_model(*lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : List[str]	)    -> Dict:
								__magic_name__				:						Dict     				=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : Union[str, Any]	)    -> Tuple:
								__magic_name__				:						List[str]     				=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : Union[str, Any]	)    -> str:
								__magic_name__				:						Optional[Any]     				=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : List[Any]	)    -> Any:
								__magic_name__				:						int     				=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_information_extraction(*lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : Dict	)    -> Tuple:
								__magic_name__				:						Dict     				=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_	)


			@slow
			def 				UpperCAmelCase__							(			self      : Tuple	)    -> List[Any]:
								for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__magic_name__				:						str     				=				ErnieMModel.from_pretrained(lowerCamelCase_	)
													self.assertIsNotNone(lowerCamelCase_	)






@require_torch
class  lowerCamelCase     (		unittest.TestCase    ):






			'''simple docstring'''





			@slow
			def 				UpperCAmelCase__							(			self      : Optional[int]	)    -> Union[str, Any]:
								__magic_name__				:						Union[str, Any]     				=				ErnieMModel.from_pretrained('''susnato/ernie-m-base_pytorch'''	)
								model.eval()
								__magic_name__				:						Optional[Any]     				=				torch.tensor([[0, 1, 2, 3, 4, 5]]	)
								__magic_name__				:						Dict     				=				model(lowerCamelCase_	)[0]

								# TODO Replace vocab size
								__magic_name__				:						str     				=				768

								__magic_name__				:						List[str]     				=				torch.Size((1, 6, hidden_size)	)
								self.assertEqual(output.shape							,    lowerCamelCase_	)

								__magic_name__				:						List[str]     				=				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	)	)









import math


class  lowerCamelCase     :






			'''simple docstring'''





			def __init__(			self      : Dict							,    lowerCamelCase_      : int							,    lowerCamelCase_      : int							,    lowerCamelCase_      : int							,    lowerCamelCase_      : Union[str, Any]	)    -> Optional[Any]:
								__magic_name__				:						Union[str, Any]     				=				x
								__magic_name__				:						Optional[int]     				=				y
								__magic_name__				:						List[str]     				=				step_size
								__magic_name__				:						Union[str, Any]     				=				function_to_optimize

			def 				UpperCAmelCase__							(			self      : int	)    -> int:
								return self.function(self.x							,    self.y	)

			def 				UpperCAmelCase__							(			self      : Dict	)    -> Any:
								__magic_name__				:						Optional[int]     				=				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      : Dict	)    -> Tuple:
								return hash(str(self	)	)

			def __eq__(			self      : List[str]							,    lowerCamelCase_      : Dict	)    -> Union[str, Any]:
								if isinstance(lowerCamelCase_							,    lowerCamelCase_	):
													return hash(str(self	)	) == hash(str(lowerCamelCase_	)	)
								return False


			def __str__(			self      : Tuple	)    -> Tuple:
								return F'''x: {self.x} y: {self.y}'''







def 		lowercase__ (		__A:						int				,__A:						bool = True				,__A:						float = math.inf				,__A:						float = -math.inf				,__A:						float = math.inf				,__A:						float = -math.inf				,__A:						bool = False				,__A:						int = 1_0_0_0_0				,):





					'''simple docstring'''






					__magic_name__				:						Union[str, Any]     				=				search_prob
					__magic_name__				:						int     				=				[]  # list to store the current score at each iteration
					__magic_name__				:						Any     				=				0
					__magic_name__				:						Optional[Any]     				=				False
					__magic_name__				:						Dict     				=				set()
					while not solution_found and iterations < max_iter:
										visited.add(__A					)
										iterations += 1
										__magic_name__				:						int     				=				current_state.score()
										scores.append(__A					)
										__magic_name__				:						Union[str, Any]     				=				current_state.get_neighbors()
										__magic_name__				:						str     				=				-math.inf
										__magic_name__				:						Optional[Any]     				=				math.inf
										__magic_name__				:						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
															__magic_name__				:						Tuple     				=				neighbor.score() - current_score
															if find_max:  # finding max
																				# going to direction with greatest ascent
																				if change > max_change and change > 0:
																									__magic_name__				:						List[str]     				=				change
																									__magic_name__				:						List[str]     				=				neighbor
															else:  # finding min
																				# to direction with greatest descent
																				if change < min_change and change < 0:
																									__magic_name__				:						List[Any]     				=				change
																									__magic_name__				:						Optional[int]     				=				neighbor
										if next_state is not None:
															# we found at least one neighbor which improved the current state
															__magic_name__				:						Any     				=				next_state
										else:
															# since we have no neighbor that improves the solution we stop the search
															__magic_name__				:						List[str]     				=				True

					if visualization:
										from matplotlib import pyplot as plt

										plt.plot(range(__A					)				,__A					)
										plt.xlabel('''Iterations'''					)
										plt.ylabel('''Function values'''					)
										plt.show()

					return current_state


if __name__ == "__main__":
	import doctest

	doctest.testmod()







	def 		lowercase__ (		__A:						Optional[Any]				,__A:						str					):





						'''simple docstring'''






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

	# starting the problem with initial coordinates (3, 4)
	__lowerCamelCase :    Any      						=      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	__lowerCamelCase :    Any      						=      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)
	__lowerCamelCase :    Optional[int]      						=      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	__lowerCamelCase :    Union[str, Any]      						=      hill_climbing(
	    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()}"""
	)







	def 		lowercase__ (		__A:						List[Any]				,__A:						int					):





						'''simple docstring'''






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

	__lowerCamelCase :    List[str]      						=      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	__lowerCamelCase :    Optional[int]      						=      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()}"""
	)



