

import heapq
import sys

import numpy as np

a__												=						tuple[int, int]

class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			)       ->       Dict:
												_a      :       str   =						[]
												_a      :       int   =						set()


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												if not self.empty():
																	return self.elements[0][0]
												else:
																	return float('''inf'''			)


							def 							__lowercase							(     self			)       ->       Tuple:
												return len(self.elements			) == 0


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       Optional[int]:
												if item not in self.set:
																	heapq.heappush(self.elements			,				(priority, item)			)
																	self.set.add(_a			)
												else:
																	# update
																	# print("update", item)
																	_a      :       Optional[Any]   =						[]
																	((_a) ,   (_a))      :       Optional[int]   =						heapq.heappop(self.elements			)
																	while x != item:
																						temp.append((pri, x)			)
																						((_a) ,   (_a))      :       Union[str, Any]   =						heapq.heappop(self.elements			)
																	temp.append((priority, item)			)
																	for pro, xxx in temp:
																						heapq.heappush(self.elements			,				(pro, xxx)			)


							def 							__lowercase							(     self			,				_a			)       ->       List[Any]:
												if item in self.set:
																	self.set.remove(_a			)
																	_a      :       Optional[Any]   =						[]
																	((_a) ,   (_a))      :       Union[str, Any]   =						heapq.heappop(self.elements			)
																	while x != item:
																						temp.append((pro, x)			)
																						((_a) ,   (_a))      :       Optional[int]   =						heapq.heappop(self.elements			)
																	for prito, yyy in temp:
																						heapq.heappush(self.elements			,				(prito, yyy)			)


							def 							__lowercase							(     self			)       ->       Tuple:
												return self.elements[0][1]




							def 							__lowercase							(     self			)       ->       int:
												((_a) ,   (_a))      :       List[Any]   =						heapq.heappop(self.elements			)
												self.set.remove(_a			)
												return (priority, item)



def   __UpperCAmelCase					(      __a :     TPos						,__a :     TPos      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       int   =						np.array(__a      )
					_a      :       int   =						np.array(__a      )
					return np.linalg.norm(a - b      )



def   __UpperCAmelCase					(      __a :     TPos						,__a :     TPos      )     ->	List[Any]:

					"""simple docstring"""

					return consistent_heuristic(__a						,__a      ) // t



def   __UpperCAmelCase					(      __a :     TPos						,__a :     TPos      )     ->	str:

					"""simple docstring"""

					return abs(p[0] - goal[0]      ) + abs(p[1] - goal[1]      )



def   __UpperCAmelCase					(      __a :     TPos						,__a :     int						,__a :     TPos						,__a :     dict[TPos, float]      )     ->	Optional[int]:

					"""simple docstring"""

					_a      :       Optional[int]   =						g_function[start] + Wa * heuristics[i](__a						,__a      )
					return ans



def   __UpperCAmelCase					(      __a :     Any						,__a :     Any						,__a :     Dict      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						np.chararray((n, n)      )
					for i in range(__a      ):
										for j in range(__a      ):
															_a      :       Any   =						'''*'''

					for i in range(__a      ):
										for j in range(__a      ):
															if (j, (n - 1) - i) in blocks:
																				_a      :       Dict   =						'''#'''

					_a      :       Optional[int]   =						'''-'''
					_a      :       List[str]   =						back_pointer[goal]
					while x != start:
										((_a) ,   (_a))      :       int   =						x
										# print(x)
										_a      :       Dict   =						'''-'''
										_a      :       Tuple   =						back_pointer[x]
					_a      :       Optional[int]   =						'''-'''

					for i in range(__a      ):
										for j in range(__a      ):
															if (i, j) == (0, n - 1):
																				print(grid[i][j]						,end=''' '''      )
																				print('''<-- End position'''						,end=''' '''      )
															else:
																				print(grid[i][j]						,end=''' '''      )
										print()
					print('''^'''      )
					print('''Start position'''      )
					print()
					print('''# is an obstacle'''      )
					print('''- is the path taken by algorithm'''      )
					print('''PATH TAKEN BY THE ALGORITHM IS:-'''      )
					_a      :       str   =						back_pointer[goal]
					while x != start:
										print(__a						,end=''' '''      )
										_a      :       str   =						back_pointer[x]
					print(__a      )
					sys.exit()



def   __UpperCAmelCase					(      __a :     TPos      )     ->	List[str]:

					"""simple docstring"""

					if p[0] < 0 or p[0] > n - 1:
										return False
					if p[1] < 0 or p[1] > n - 1:
										return False
					return True



def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     List[str]						,__a :     List[str]						,__a :     Any						,__a :     Dict						,__a :     Optional[Any]						,__a :     List[Any]						,__a :     Any						,)     ->	Optional[Any]:

					"""simple docstring"""

					for itera in range(__a      ):
										open_list[itera].remove_element(__a      )
					# print("s", s)
					# print("j", j)
					((_a) ,   (_a))      :       Optional[Any]   =						s
					_a      :       Any   =						(x - 1, y)
					_a      :       Dict   =						(x + 1, y)
					_a      :       str   =						(x, y + 1)
					_a      :       str   =						(x, y - 1)

					for neighbours in [left, right, up, down]:
										if neighbours not in blocks:
															if valid(__a      ) and neighbours not in visited:
																				# print("neighbour", neighbours)
																				visited.add(__a      )
																				_a      :       Optional[int]   =						-1
																				_a      :       Union[str, Any]   =						float('''inf'''      )

															if valid(__a      ) and g_function[neighbours] > g_function[s] + 1:
																				_a      :       Dict   =						g_function[s] + 1
																				_a      :       Optional[Any]   =						s
																				if neighbours not in close_list_anchor:
																									open_list[0].put(__a						,key(__a						,0						,__a						,__a      )      )
																									if neighbours not in close_list_inad:
																														for var in range(1						,__a      ):
																																			if key(__a						,__a						,__a						,__a      ) <= Wa * key(
																																			    __a						,0						,__a						,__a      ):
																																								open_list[j].put(
																																								    __a						,key(__a						,__a						,__a						,__a      )      )



def   __UpperCAmelCase					(      )     ->	List[str]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						[]
					for x in range(1						,5      ):
										for y in range(1						,6      ):
															some_list.append((x, y)      )

					for x in range(15						,20      ):
										some_list.append((x, 17)      )

					for x in range(10						,19      ):
										for y in range(1						,15      ):
															some_list.append((x, y)      )

    # L block
					for x in range(1						,4      ):
										for y in range(12						,19      ):
															some_list.append((x, y)      )
					for x in range(3						,13      ):
										for y in range(16						,19      ):
															some_list.append((x, y)      )
					return some_list


a__												=						{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a__												=						[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
a__												=						make_common_ground()


a__												=						blocks_blk
# hyper parameters
a__												=						1
a__												=						1
a__												=						20
a__												=						3  # one consistent and two other inconsistent

# start and end destination
a__												=						(0, 0)
a__												=						(n - 1, n - 1)

a__												=						1



def   __UpperCAmelCase					(      __a :     TPos						,__a :     TPos						,__a :     int      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       Optional[int]   =						{start: 0, goal: float('''inf'''      )}
					_a      :       int   =						{start: -1, goal: -1}
					_a      :       Union[str, Any]   =						[]
					_a      :       Optional[int]   =						set()

					for i in range(__a      ):
										open_list.append(PriorityQueue()      )
										open_list[i].put(__a						,key(__a						,__a						,__a						,__a      )      )

					_a      :       list[int]   =						[]
					_a      :       list[int]   =						[]
					while open_list[0].minkey() < float('''inf'''      ):
										for i in range(1						,__a      ):
															# print(open_list[0].minkey(), open_list[i].minkey())
															if open_list[i].minkey() <= Wa * open_list[0].minkey():
																				global t
																				t += 1
																				if g_function[goal] <= open_list[i].minkey():
																									if g_function[goal] < float('''inf'''      ):
																														do_something(__a						,__a						,__a      )
																				else:
																									_a ,   _a      :       Any   =						open_list[i].top_show()
																									visited.add(__a      )
																									expand_state(
																									    __a						,__a						,__a						,__a						,__a						,__a						,__a						,__a						,)
																									close_list_inad.append(__a      )
															else:
																				if g_function[goal] <= open_list[0].minkey():
																									if g_function[goal] < float('''inf'''      ):
																														do_something(__a						,__a						,__a      )
																				else:
																									_a      :       Tuple   =						open_list[0].top_show()
																									visited.add(__a      )
																									expand_state(
																									    __a						,0						,__a						,__a						,__a						,__a						,__a						,__a						,)
																									close_list_anchor.append(__a      )
					print('''No path found to goal'''      )
					print()
					for i in range(n - 1						,-1						,-1      ):
										for j in range(__a      ):
															if (j, i) in blocks:
																				print('''#'''						,end=''' '''      )
															elif (j, i) in back_pointer:
																				if (j, i) == (n - 1, n - 1):
																									print('''*'''						,end=''' '''      )
																				else:
																									print('''-'''						,end=''' '''      )
															else:
																				print('''*'''						,end=''' '''      )
															if (j, i) == (n - 1, n - 1):
																				print('''<-- End position'''						,end=''' '''      )
										print()
					print('''^'''      )
					print('''Start position'''      )
					print()
					print('''# is an obstacle'''      )
					print('''- is the path taken by algorithm'''      )


if __name__ == "__main__":
			multi_a_star(start, goal, n_heuristic)









import unittest

from transformers import DebertaConfig, is_torch_available
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		)
		from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	Dict			,      __lowercase				:	Any			,      __lowercase				:	str=13			,      __lowercase				:	Optional[Any]=7			,      __lowercase				:	Tuple=True			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	List[str]=True			,      __lowercase				:	Dict=True			,      __lowercase				:	Any=99			,      __lowercase				:	Any=32			,      __lowercase				:	List[Any]=5			,      __lowercase				:	Any=4			,      __lowercase				:	List[str]=37			,      __lowercase				:	Union[str, Any]="gelu"			,      __lowercase				:	Union[str, Any]=0.1			,      __lowercase				:	str=0.1			,      __lowercase				:	List[str]=5_12			,      __lowercase				:	Tuple=16			,      __lowercase				:	List[str]=2			,      __lowercase				:	Tuple=0.02			,      __lowercase				:	Optional[Any]=False			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	Union[str, Any]="None"			,      __lowercase				:	Union[str, Any]=3			,      __lowercase				:	int=4			,      __lowercase				:	Tuple=None			,      ):






							"""simple docstring"""






							snake_case_						       =	parent
							snake_case_						       =	batch_size
							snake_case_						       =	seq_length
							snake_case_						       =	is_training
							snake_case_						       =	use_input_mask
							snake_case_						       =	use_token_type_ids
							snake_case_						       =	use_labels
							snake_case_						       =	vocab_size
							snake_case_						       =	hidden_size
							snake_case_						       =	num_hidden_layers
							snake_case_						       =	num_attention_heads
							snake_case_						       =	intermediate_size
							snake_case_						       =	hidden_act
							snake_case_						       =	hidden_dropout_prob
							snake_case_						       =	attention_probs_dropout_prob
							snake_case_						       =	max_position_embeddings
							snake_case_						       =	type_vocab_size
							snake_case_						       =	type_sequence_label_size
							snake_case_						       =	initializer_range
							snake_case_						       =	num_labels
							snake_case_						       =	num_choices
							snake_case_						       =	relative_attention
							snake_case_						       =	position_biased_input
							snake_case_						       =	pos_att_type
							snake_case_						       =	scope






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size		)

							snake_case_						       =	None
							if self.use_input_mask:
								snake_case_						       =	ids_tensor([self.batch_size, self.seq_length]			,      vocab_size=2		)

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

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

							snake_case_						       =	self.get_config()

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






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							return DebertaConfig(
							    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			,      relative_attention=self.relative_attention			,      position_biased_input=self.position_biased_input			,      pos_att_type=self.pos_att_type			,      )






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.get_config()
							snake_case_						       =	3_00
							return config






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							self.parent.assertListEqual(list(result.loss.size()		)			,      []		)






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Tuple			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Tuple			,      __lowercase				:	Dict			,      __lowercase				:	str			,      __lowercase				:	Optional[int]			,      __lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	DebertaModel(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase		)[0]
							snake_case_						       =	model(__lowercase			,      token_type_ids=__lowercase		)[0]
							snake_case_						       =	model(__lowercase		)[0]

							self.parent.assertListEqual(list(sequence_output.size()		)			,      [self.batch_size, self.seq_length, self.hidden_size]		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Any			,      __lowercase				:	Dict			,      __lowercase				:	Any			,      __lowercase				:	Any			,      __lowercase				:	Tuple			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	DebertaForMaskedLM(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase		)

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






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Tuple			,      __lowercase				:	List[str]			,      __lowercase				:	Dict			,      __lowercase				:	str			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	self.num_labels
							snake_case_						       =	DebertaForSequenceClassification(__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase		)
							self.parent.assertListEqual(list(result.logits.size()		)			,      [self.batch_size, self.num_labels]		)
							self.check_loss_output(__lowercase		)






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	List[str]			,      __lowercase				:	Any			,      __lowercase				:	List[Any]			,      __lowercase				:	str			,      __lowercase				:	List[str]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	self.num_labels
							snake_case_						       =	DebertaForTokenClassification(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase		)
							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.num_labels)		)






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	str			,      __lowercase				:	List[str]			,      __lowercase				:	Optional[int]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	DebertaForQuestionAnswering(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(
							    __lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      start_positions=__lowercase			,      end_positions=__lowercase			,      )
							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				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	self.prepare_config_and_inputs()
							(
							    (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , 
							)						       =	config_and_inputs
							snake_case_						       =	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict







@require_torch
class 						UpperCAmelCase  (							UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  (
						    (
						        DebertaModel,
						        DebertaForMaskedLM,
						        DebertaForSequenceClassification,
						        DebertaForTokenClassification,
						        DebertaForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase_							=  (
						    {
						        '''feature-extraction''': DebertaModel,
						        '''fill-mask''': DebertaForMaskedLM,
						        '''question-answering''': DebertaForQuestionAnswering,
						        '''text-classification''': DebertaForSequenceClassification,
						        '''token-classification''': DebertaForTokenClassification,
						        '''zero-shot''': DebertaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase_							=  True
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	DebertaModelTester(self		)
							snake_case_						       =	ConfigTester(self			,      config_class=__lowercase			,      hidden_size=37		)






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*__lowercase		)






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*__lowercase		)






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*__lowercase		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*__lowercase		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*__lowercase		)






						@slow
						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_						       =	DebertaModel.from_pretrained(__lowercase		)
								self.assertIsNotNone(__lowercase		)







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







						'''simple docstring'''





						@unittest.skip(reason="Model not available yet"		)
						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							pass






						@slow
						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	DebertaModel.from_pretrained("microsoft/deberta-base"		)

							snake_case_						       =	torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]		)
							snake_case_						       =	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
							with torch.no_grad():
								snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase		)[0]
							# compare the actual values for a slice.
							snake_case_						       =	torch.tensor(
							    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]		)
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]			,      __lowercase			,      atol=1E-4		)			,      f"{output[:, 1:4, 1:4]}"		)
