




import heapq
import sys

import numpy as np

_snake_case										=       tuple[int, int]







class 						lowercase							:

     def __init__(  self				)						->							int:
           _A							:     List[Any]			=						[]
           _A							:     Optional[Any]			=						set()

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

     def 					a__	(  self				)						->							int:
           return len(self.elements				) == 0

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

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

     def 					a__	(  self				)						->							Optional[Any]:
           return self.elements[0][1]



     def 					a__	(  self				)						->							Optional[Any]:
           ((_A)			,   (_A))							:     Tuple			=						heapq.heappop(self.elements				)
           self.set.remove(lowercase_				)
           return (priority, item)







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     int			=						np.array(__UpperCAmelCase     )
      _A							:     List[Any]			=						np.array(__UpperCAmelCase     )
      return np.linalg.norm(a - b     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      return consistent_heuristic(__UpperCAmelCase,__UpperCAmelCase     ) // t







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      return abs(p[0] - goal[0]     ) + abs(p[1] - goal[1]     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_     ):
      _A							:     Tuple			=						g_function[start] + Wa * heuristics[i](__UpperCAmelCase,__UpperCAmelCase     )
      return ans







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     int			=						np.chararray((n, n)     )
      for i in range(__UpperCAmelCase     ):
            for j in range(__UpperCAmelCase     ):
                  _A							:     Union[str, Any]			=						"""*"""

      for i in range(__UpperCAmelCase     ):
            for j in range(__UpperCAmelCase     ):
                  if (j, (n - 1) - i) in blocks:
                        _A							:     List[str]			=						"""#"""

      _A							:     Dict			=						"""-"""
      _A							:     Optional[int]			=						back_pointer[goal]
      while x != start:
            ((_A)			,   (_A))							:     Optional[int]			=						x
            # print(x)
            _A							:     Any			=						"""-"""
            _A							:     int			=						back_pointer[x]
      _A							:     Optional[Any]			=						"""-"""

      for i in range(__UpperCAmelCase     ):
            for j in range(__UpperCAmelCase     ):
                  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							:     Dict			=						back_pointer[goal]
      while x != start:
            print(__UpperCAmelCase,end=""" """     )
            _A							:     Tuple			=						back_pointer[x]
      print(__UpperCAmelCase     )
      sys.exit()







def 				lowerCAmelCase_	(     snake_case_     ):

      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 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_,snake_case_,snake_case_,snake_case_,snake_case_,):

      for itera in range(__UpperCAmelCase     ):
            open_list[itera].remove_element(__UpperCAmelCase     )
      # print("s", s)
      # print("j", j)
      ((_A)			,   (_A))							:     Dict			=						s
      _A							:     Dict			=						(x - 1, y)
      _A							:     Union[str, Any]			=						(x + 1, y)
      _A							:     Dict			=						(x, y + 1)
      _A							:     List[Any]			=						(x, y - 1)

      for neighbours in [left, right, up, down]:
            if neighbours not in blocks:
                  if valid(__UpperCAmelCase     ) and neighbours not in visited:
                        # print("neighbour", neighbours)
                        visited.add(__UpperCAmelCase     )
                        _A							:     Tuple			=						-1
                        _A							:     Optional[Any]			=						float("""inf"""     )

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







def 				lowerCAmelCase_	(     ):
      _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


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

_snake_case										=       [
    (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),
]
_snake_case										=       make_common_ground()


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

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

_snake_case										=       1







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Tuple			=						{start: 0, goal: float("""inf"""     )}
      _A							:     Union[str, Any]			=						{start: -1, goal: -1}
      _A							:     Optional[Any]			=						[]
      _A							:     List[Any]			=						set()

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

      _A							:     int			=						[]
      _A							:     str			=						[]
      while open_list[0].minkey() < float("""inf"""     ):
            for i in range(1,__UpperCAmelCase     ):
                  # 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(__UpperCAmelCase,__UpperCAmelCase,__UpperCAmelCase     )
                        else:
                              _A			,   _A							:     Optional[int]			=						open_list[i].top_show()
                              visited.add(__UpperCAmelCase     )
                              expand_state(
                                  __UpperCAmelCase,__UpperCAmelCase,__UpperCAmelCase,__UpperCAmelCase,__UpperCAmelCase,__UpperCAmelCase,__UpperCAmelCase,__UpperCAmelCase,)
                              close_list_inad.append(__UpperCAmelCase     )
                  else:
                        if g_function[goal] <= open_list[0].minkey():
                              if g_function[goal] < float("""inf"""     ):
                                    do_something(__UpperCAmelCase,__UpperCAmelCase,__UpperCAmelCase     )
                        else:
                              _A							:     Any			=						open_list[0].top_show()
                              visited.add(__UpperCAmelCase     )
                              expand_state(
                                  __UpperCAmelCase,0,__UpperCAmelCase,__UpperCAmelCase,__UpperCAmelCase,__UpperCAmelCase,__UpperCAmelCase,__UpperCAmelCase,)
                              close_list_anchor.append(__UpperCAmelCase     )
      print("""No path found to goal"""     )
      print()
      for i in range(n - 1,-1,-1     ):
            for j in range(__UpperCAmelCase     ):
                  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)







'''simple docstring'''

import unittest

from transformers import MPNetConfig, 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 (
							    MPNetForMaskedLM,
							    MPNetForMultipleChoice,
							    MPNetForQuestionAnswering,
							    MPNetForSequenceClassification,
							    MPNetForTokenClassification,
							    MPNetModel,
							)






class 						a				:


				def __init__(		self    :					List[Any]       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					Any=13       ,				lowercase_    :					Optional[Any]=7       ,				lowercase_    :					Optional[Any]=True       ,				lowercase_    :					Dict=True       ,				lowercase_    :					Tuple=False       ,				lowercase_    :					Optional[Any]=True       ,				lowercase_    :					Any=99       ,				lowercase_    :					Union[str, Any]=64       ,				lowercase_    :					str=5       ,				lowercase_    :					int=4       ,				lowercase_    :					List[Any]=64       ,				lowercase_    :					Dict="gelu"       ,				lowercase_    :					Optional[int]=0.1       ,				lowercase_    :					Optional[Any]=0.1       ,				lowercase_    :					Tuple=512       ,				lowercase_    :					List[Any]=16       ,				lowercase_    :					str=2       ,				lowercase_    :					List[str]=0.02       ,				lowercase_    :					Optional[Any]=3       ,				lowercase_    :					Optional[Any]=4       ,				lowercase_    :					List[Any]=None       ,				):
									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_									=				scope


				def   A_ (		self    :					List[str]					):
									return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''					)


				def   A_ (		self    :					str					):
									snake_case_									=				ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size					)

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

									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, input_mask, sequence_labels, token_labels, choice_labels


				def   A_ (		self    :					Tuple					):
									return MPNetConfig(
									    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       ,				initializer_range=self.initializer_range       ,				)


				def   A_ (		self    :					Any       ,				lowercase_    :					Dict       ,				lowercase_    :					str       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Dict       ,				lowercase_    :					Optional[int]					):
									snake_case_									=				MPNetModel(config=lowercase_					)
									model.to(lowercase_					)
									model.eval()
									snake_case_									=				model(lowercase_       ,				lowercase_					)
									snake_case_									=				model(lowercase_					)
									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   A_ (		self    :					str       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					Any       ,				lowercase_    :					str       ,				lowercase_    :					Tuple       ,				lowercase_    :					Tuple       ,				lowercase_    :					Optional[int]					):
									snake_case_									=				MPNetForQuestionAnswering(config=lowercase_					)
									model.to(lowercase_					)
									model.eval()
									snake_case_									=				model(
									    lowercase_       ,				attention_mask=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   A_ (		self    :					Tuple       ,				lowercase_    :					Tuple       ,				lowercase_    :					List[str]       ,				lowercase_    :					str       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					Any					):
									snake_case_									=				self.num_labels
									snake_case_									=				MPNetForSequenceClassification(lowercase_					)
									model.to(lowercase_					)
									model.eval()
									snake_case_									=				model(lowercase_       ,				attention_mask=lowercase_       ,				labels=lowercase_					)
									self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)					)


				def   A_ (		self    :					Any       ,				lowercase_    :					Any       ,				lowercase_    :					str       ,				lowercase_    :					Tuple       ,				lowercase_    :					List[str]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Dict					):
									snake_case_									=				self.num_choices
									snake_case_									=				MPNetForMultipleChoice(config=lowercase_					)
									model.to(lowercase_					)
									model.eval()
									snake_case_									=				input_ids.unsqueeze(1					).expand(-1       ,				self.num_choices       ,				-1					).contiguous()
									snake_case_									=				input_mask.unsqueeze(1					).expand(-1       ,				self.num_choices       ,				-1					).contiguous()
									snake_case_									=				model(
									    lowercase_       ,				attention_mask=lowercase_       ,				labels=lowercase_       ,				)
									self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_choices)					)


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


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				self.prepare_config_and_inputs()
									((snake_case_)			,(snake_case_)			,(snake_case_)			,(snake_case_)			,(snake_case_)			,(snake_case_))									=				config_and_inputs
									snake_case_									=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
									return config, inputs_dict







@require_torch
class 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		(
				    (
				        MPNetForMaskedLM,
				        MPNetForMultipleChoice,
				        MPNetForQuestionAnswering,
				        MPNetForSequenceClassification,
				        MPNetForTokenClassification,
				        MPNetModel,
				    )
				    if is_torch_available()
				    else ()
				)
				snake_case_      =		(
				    {
				        "feature-extraction": MPNetModel,
				        "fill-mask": MPNetForMaskedLM,
				        "question-answering": MPNetForQuestionAnswering,
				        "text-classification": MPNetForSequenceClassification,
				        "token-classification": MPNetForTokenClassification,
				        "zero-shot": MPNetForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				snake_case_      =		False
				snake_case_      =		True


				def   A_ (		self    :					Tuple					):
									snake_case_									=				MPNetModelTester(self					)
									snake_case_									=				ConfigTester(self       ,				config_class=lowercase_       ,				hidden_size=37					)


				def   A_ (		self    :					Union[str, Any]					):
									self.config_tester.run_common_tests()


				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_model(*lowercase_					)


				def   A_ (		self    :					List[Any]					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_					)


				def   A_ (		self    :					List[Any]					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_					)


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_					)


				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_					)







@require_torch
class 						a				(   unittest.TestCase     ):


				@slow
				def   A_ (		self    :					List[Any]					):
									snake_case_									=				MPNetModel.from_pretrained('''microsoft/mpnet-base'''					)
									snake_case_									=				torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]					)
									snake_case_									=				model(lowercase_					)[0]
									snake_case_									=				torch.Size((1, 11, 768)					)
									self.assertEqual(output.shape       ,				lowercase_					)
									snake_case_									=				torch.tensor(
									    [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]					)
									# compare the actual values for a slice.
									self.assertTrue(torch.allclose(output[:, :3, :3]       ,				lowercase_       ,				atol=1e-4					)					)

