


'''simple docstring'''
import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    MobileBertForMaskedLM,
							    MobileBertForMultipleChoice,
							    MobileBertForNextSentencePrediction,
							    MobileBertForPreTraining,
							    MobileBertForQuestionAnswering,
							    MobileBertForSequenceClassification,
							    MobileBertForTokenClassification,
							    MobileBertModel,
							)






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      Dict ,  lowerCAmelCase							:      Dict ,  lowerCAmelCase							:      Tuple=13 ,  lowerCAmelCase							:      Optional[Any]=7 ,  lowerCAmelCase							:      Any=True ,  lowerCAmelCase							:      Dict=True ,  lowerCAmelCase							:      Optional[int]=True ,  lowerCAmelCase							:      Dict=True ,  lowerCAmelCase							:      List[str]=99 ,  lowerCAmelCase							:      Tuple=64 ,  lowerCAmelCase							:      Tuple=32 ,  lowerCAmelCase							:      Dict=5 ,  lowerCAmelCase							:      List[str]=4 ,  lowerCAmelCase							:      List[str]=37 ,  lowerCAmelCase							:      List[str]="gelu" ,  lowerCAmelCase							:      str=0.1 ,  lowerCAmelCase							:      Optional[Any]=0.1 ,  lowerCAmelCase							:      Optional[int]=5_12 ,  lowerCAmelCase							:      Optional[Any]=16 ,  lowerCAmelCase							:      Any=2 ,  lowerCAmelCase							:      List[str]=0.0_2 ,  lowerCAmelCase							:      str=3 ,  lowerCAmelCase							:      Optional[Any]=4 ,  lowerCAmelCase							:      int=None ,  ):
													A_			    =		parent
													A_			    =		batch_size
													A_			    =		seq_length
													A_			    =		is_training
													A_			    =		use_input_mask
													A_			    =		use_token_type_ids
													A_			    =		use_labels
													A_			    =		vocab_size
													A_			    =		hidden_size
													A_			    =		embedding_size
													A_			    =		num_hidden_layers
													A_			    =		num_attention_heads
													A_			    =		intermediate_size
													A_			    =		hidden_act
													A_			    =		hidden_dropout_prob
													A_			    =		attention_probs_dropout_prob
													A_			    =		max_position_embeddings
													A_			    =		type_vocab_size
													A_			    =		type_sequence_label_size
													A_			    =		initializer_range
													A_			    =		num_labels
													A_			    =		num_choices
													A_			    =		scope






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size	)

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

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

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

													A_			    =		self.get_config()

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






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													return MobileBertConfig(
													    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 ,  embedding_size=self.embedding_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=lowerCAmelCase ,  initializer_range=self.initializer_range ,  )






						def 				_UpperCAmelCase       (  self							:      Union[str, Any] ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      List[Any] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      List[Any] ,  lowerCAmelCase							:      Optional[int]	):
													A_			    =		MobileBertModel(config=lowerCAmelCase	)
													model.to(lowerCAmelCase	)
													model.eval()
													A_			    =		model(lowerCAmelCase ,  attention_mask=lowerCAmelCase ,  token_type_ids=lowerCAmelCase	)
													A_			    =		model(lowerCAmelCase ,  token_type_ids=lowerCAmelCase	)
													A_			    =		model(lowerCAmelCase	)

													self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)	)
													self.parent.assertEqual(result.pooler_output.shape ,  (self.batch_size, self.hidden_size)	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      List[Any] ,  lowerCAmelCase							:      Union[str, Any] ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Dict ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      Any	):
													A_			    =		MobileBertForMaskedLM(config=lowerCAmelCase	)
													model.to(lowerCAmelCase	)
													model.eval()
													A_			    =		model(lowerCAmelCase ,  attention_mask=lowerCAmelCase ,  token_type_ids=lowerCAmelCase ,  labels=lowerCAmelCase	)
													self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.vocab_size)	)






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      Union[str, Any] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Any	):
													A_			    =		MobileBertForNextSentencePrediction(config=lowerCAmelCase	)
													model.to(lowerCAmelCase	)
													model.eval()
													A_			    =		model(
													    lowerCAmelCase ,  attention_mask=lowerCAmelCase ,  token_type_ids=lowerCAmelCase ,  labels=lowerCAmelCase ,  )
													self.parent.assertEqual(result.logits.shape ,  (self.batch_size, 2)	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any] ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      Union[str, Any] ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      Optional[int]	):
													A_			    =		MobileBertForPreTraining(config=lowerCAmelCase	)
													model.to(lowerCAmelCase	)
													model.eval()
													A_			    =		model(
													    lowerCAmelCase ,  attention_mask=lowerCAmelCase ,  token_type_ids=lowerCAmelCase ,  labels=lowerCAmelCase ,  next_sentence_label=lowerCAmelCase ,  )
													self.parent.assertEqual(result.prediction_logits.shape ,  (self.batch_size, self.seq_length, self.vocab_size)	)
													self.parent.assertEqual(result.seq_relationship_logits.shape ,  (self.batch_size, 2)	)






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      List[Any] ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      Union[str, Any] ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      int	):
													A_			    =		MobileBertForQuestionAnswering(config=lowerCAmelCase	)
													model.to(lowerCAmelCase	)
													model.eval()
													A_			    =		model(
													    lowerCAmelCase ,  attention_mask=lowerCAmelCase ,  token_type_ids=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							:      int ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      int	):
													A_			    =		self.num_labels
													A_			    =		MobileBertForSequenceClassification(lowerCAmelCase	)
													model.to(lowerCAmelCase	)
													model.eval()
													A_			    =		model(lowerCAmelCase ,  attention_mask=lowerCAmelCase ,  token_type_ids=lowerCAmelCase ,  labels=lowerCAmelCase	)
													self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)	)






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      List[Any] ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      Union[str, Any] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      List[Any]	):
													A_			    =		self.num_labels
													A_			    =		MobileBertForTokenClassification(config=lowerCAmelCase	)
													model.to(lowerCAmelCase	)
													model.eval()
													A_			    =		model(lowerCAmelCase ,  attention_mask=lowerCAmelCase ,  token_type_ids=lowerCAmelCase ,  labels=lowerCAmelCase	)
													self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.num_labels)	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      Dict ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      str	):
													A_			    =		self.num_choices
													A_			    =		MobileBertForMultipleChoice(config=lowerCAmelCase	)
													model.to(lowerCAmelCase	)
													model.eval()
													A_			    =		input_ids.unsqueeze(1	).expand(-1 ,  self.num_choices ,  -1	).contiguous()
													A_			    =		token_type_ids.unsqueeze(1	).expand(-1 ,  self.num_choices ,  -1	).contiguous()
													A_			    =		input_mask.unsqueeze(1	).expand(-1 ,  self.num_choices ,  -1	).contiguous()
													A_			    =		model(
													    lowerCAmelCase ,  attention_mask=lowerCAmelCase ,  token_type_ids=lowerCAmelCase ,  labels=lowerCAmelCase ,  )
													self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_choices)	)






						def 				_UpperCAmelCase       (  self							:      Dict	):
													A_			    =		self.prepare_config_and_inputs()
													(
													    (
													    A_
													)    ,					(
													    A_
													)    ,					(
													    A_
													)    ,					(
													    A_
													)    ,					(
													    A_
													)    ,					(
													    A_
													)    ,					(
													    A_
													)    ,					
													)			    =		config_and_inputs
													A_			    =		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
													return config, inputs_dict






@require_torch
class 					__lowerCAmelCase   (   lowercase				,			lowercase				,			unittest.TestCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Union[str, Any]   						=(
						    (
						        MobileBertModel,
						        MobileBertForMaskedLM,
						        MobileBertForMultipleChoice,
						        MobileBertForNextSentencePrediction,
						        MobileBertForPreTraining,
						        MobileBertForQuestionAnswering,
						        MobileBertForSequenceClassification,
						        MobileBertForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						_UpperCAmelCase		:       Tuple   						=(
						    {
						        "feature-extraction": MobileBertModel,
						        "fill-mask": MobileBertForMaskedLM,
						        "question-answering": MobileBertForQuestionAnswering,
						        "text-classification": MobileBertForSequenceClassification,
						        "token-classification": MobileBertForTokenClassification,
						        "zero-shot": MobileBertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_UpperCAmelCase		:       Any   						=True






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      Optional[Any]=False	):
													A_			    =		super()._prepare_for_class(lowerCAmelCase ,  lowerCAmelCase ,  return_labels=lowerCAmelCase	)

													if return_labels:
																				if model_class in get_values(lowerCAmelCase	):
																											A_			    =		torch.zeros(
																											    (self.model_tester.batch_size, self.model_tester.seq_length) ,  dtype=torch.long ,  device=lowerCAmelCase	)
																											A_			    =		torch.zeros(
																											    self.model_tester.batch_size ,  dtype=torch.long ,  device=lowerCAmelCase	)
													return inputs_dict






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		MobileBertModelTester(self	)
													A_			    =		ConfigTester(self ,  config_class=lowerCAmelCase ,  hidden_size=37	)






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													self.config_tester.run_common_tests()






						def 				_UpperCAmelCase       (  self							:      Dict	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_model(*lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Dict	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCAmelCase	)







def       a_				(	UpperCamelCase_ ):
							return torch.tensor(
							    UpperCamelCase_						,  dtype=torch.long						,  device=UpperCamelCase_						,  )


__SCREAMING_SNAKE_CASE					:				int									=		1E-3







@require_torch
@require_sentencepiece
@require_tokenizers
class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						@slow
						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		MobileBertModel.from_pretrained("google/mobilebert-uncased"	).to(lowerCAmelCase	)
													A_			    =		_long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]	)
													with torch.no_grad():
																				A_			    =		model(lowerCAmelCase	)[0]
													A_			    =		torch.Size((1, 9, 5_12)	)
													self.assertEqual(output.shape ,  lowerCAmelCase	)
													A_			    =		torch.tensor(
													    [
													        [
													            [-2.4_736_526e07, 8.2_691_656e04, 1.6_521_838e05],
													            [-5.7_541_704e-01, 3.9_056_022e00, 4.4_011_507e00],
													            [2.6_047_359e00, 1.5_677_652e00, -1.7_324_188e-01],
													        ]
													    ] ,  device=lowerCAmelCase ,  )

													# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
													# ~1 difference, it's therefore not a good idea to measure using addition.
													# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
													# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
													A_			    =		torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE	)
													A_			    =		torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE	)

													self.assertTrue(lower_bound and upper_bound	)









'''simple docstring'''
from __future__ import annotations

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		list[tuple[int, int]]

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      List[str] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      float ,  lowerCAmelCase							:      Node | None ,  ):
													A_			    =		pos_x
													A_			    =		pos_y
													A_			    =		(pos_y, pos_x)
													A_			    =		goal_x
													A_			    =		goal_y
													A_			    =		g_cost
													A_			    =		parent
													A_			    =		self.calculate_heuristic()






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		abs(self.pos_x - self.goal_x	)
													A_			    =		abs(self.pos_y - self.goal_y	)
													return dx + dy






						def __lt__(  self							:      Tuple ,  lowerCAmelCase							:      Any	):
													return self.f_cost < other.f_cost






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      Union[str, Any] ,  lowerCAmelCase							:      tuple[int, int] ,  lowerCAmelCase							:      tuple[int, int]	):
													A_			    =		Node(start[1] ,  start[0] ,  goal[1] ,  goal[0] ,  0 ,  lowerCAmelCase	)
													A_			    =		Node(goal[1] ,  goal[0] ,  goal[1] ,  goal[0] ,  9_99_99 ,  lowerCAmelCase	)

													A_			    =		[self.start]
													A_			    =		[]

													A_			    =		False






						def 				_UpperCAmelCase       (  self							:      str	):
													while self.open_nodes:
																				# Open Nodes are sorted using __lt__
																				self.open_nodes.sort()
																				A_			    =		self.open_nodes.pop(0	)

																				if current_node.pos == self.target.pos:
																											A_			    =		True
																											return self.retrace_path(lowerCAmelCase	)

																				self.closed_nodes.append(lowerCAmelCase	)
																				A_			    =		self.get_successors(lowerCAmelCase	)

																				for child_node in successors:
																											if child_node in self.closed_nodes:
																																		continue

																											if child_node not in self.open_nodes:
																																		self.open_nodes.append(lowerCAmelCase	)
																											else:
																																		# retrieve the best current path
																																		A_			    =		self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase	)	)

																																		if child_node.g_cost < better_node.g_cost:
																																									self.open_nodes.append(lowerCAmelCase	)
																																		else:
																																									self.open_nodes.append(lowerCAmelCase	)

													if not self.reached:
																				return [self.start.pos]
													return None






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      Node	):
													A_			    =		[]
													for action in delta:
																				A_			    =		parent.pos_x + action[1]
																				A_			    =		parent.pos_y + action[0]

																				if not (0 <= pos_x <= len(grid[0]	) - 1 and 0 <= pos_y <= len(lowerCAmelCase	) - 1):
																											continue

																				if grid[pos_y][pos_x] != 0:
																											continue

																				successors.append(
																				    Node(
																				        lowerCAmelCase ,  lowerCAmelCase ,  self.target.pos_y ,  self.target.pos_x ,  parent.g_cost + 1 ,  lowerCAmelCase ,  )	)
													return successors






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Node | None	):
													A_			    =		node
													A_			    =		[]
													while current_node is not None:
																				path.append((current_node.pos_y, current_node.pos_x)	)
																				A_			    =		current_node.parent
													path.reverse()
													return path


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		(0, 0)
							__SCREAMING_SNAKE_CASE					:				Dict									=		(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							print('''------''')

							__SCREAMING_SNAKE_CASE					:				int									=		GreedyBestFirst(init, goal)
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		greedy_bf.search()
							if path:
														for pos_x, pos_y in path:
																					__SCREAMING_SNAKE_CASE					:				List[Any]									=		2

														for elem in grid:
																					print(elem)





