



"""simple docstring"""





import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
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 (
					    MODEL_FOR_PRETRAINING_MAPPING,
					    AlbertForMaskedLM,
					    AlbertForMultipleChoice,
					    AlbertForPreTraining,
					    AlbertForQuestionAnswering,
					    AlbertForSequenceClassification,
					    AlbertForTokenClassification,
					    AlbertModel,
					)
					from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class  _lowerCAmelCase     :





		"""simple docstring"""






		def __init__(       self				,     _lowercase				,     _lowercase=1_3				,     _lowercase=7				,     _lowercase=True				,     _lowercase=True				,     _lowercase=True				,     _lowercase=True				,     _lowercase=9_9				,     _lowercase=1_6				,     _lowercase=3_6				,     _lowercase=6				,     _lowercase=6				,     _lowercase=6				,     _lowercase=3_7				,     _lowercase="gelu"				,     _lowercase=0.1				,     _lowercase=0.1				,     _lowercase=5_1_2				,     _lowercase=1_6				,     _lowercase=2				,     _lowercase=0.02				,     _lowercase=3				,     _lowercase=4				,     _lowercase=None				,     )			->    int:







								'''simple docstring'''

								snake_case_ :			Dict            =      parent
								snake_case_ :			Dict            =      batch_size
								snake_case_ :			str            =      seq_length
								snake_case_ :			List[str]            =      is_training
								snake_case_ :			Tuple            =      use_input_mask
								snake_case_ :			Dict            =      use_token_type_ids
								snake_case_ :			List[Any]            =      use_labels
								snake_case_ :			Optional[Any]            =      vocab_size
								snake_case_ :			List[Any]            =      embedding_size
								snake_case_ :			List[str]            =      hidden_size
								snake_case_ :			str            =      num_hidden_layers
								snake_case_ :			Any            =      num_hidden_groups
								snake_case_ :			Union[str, Any]            =      num_attention_heads
								snake_case_ :			Optional[int]            =      intermediate_size
								snake_case_ :			Tuple            =      hidden_act
								snake_case_ :			str            =      hidden_dropout_prob
								snake_case_ :			Optional[int]            =      attention_probs_dropout_prob
								snake_case_ :			Tuple            =      max_position_embeddings
								snake_case_ :			List[str]            =      type_vocab_size
								snake_case_ :			Tuple            =      type_sequence_label_size
								snake_case_ :			Dict            =      initializer_range
								snake_case_ :			Dict            =      num_labels
								snake_case_ :			Dict            =      num_choices
								snake_case_ :			Tuple            =      scope





		def  UpperCAmelCase__				(       self )			->    Optional[int]:







								'''simple docstring'''

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

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

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

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

								snake_case_ :			List[str]            =      self.get_config()

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





		def  UpperCAmelCase__				(       self )			->    Union[str, Any]:







								'''simple docstring'''

								return AlbertConfig(
								    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				,     num_hidden_groups=self.num_hidden_groups				,     )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    List[str]:







								'''simple docstring'''

								snake_case_ :			Any            =      AlbertModel(config=_lowercase )
								model.to(_lowercase )
								model.eval()
								snake_case_ :			str            =      model(_lowercase				,     attention_mask=_lowercase				,     token_type_ids=_lowercase )
								snake_case_ :			List[str]            =      model(_lowercase				,     token_type_ids=_lowercase )
								snake_case_ :			Dict            =      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  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    List[Any]:







								'''simple docstring'''

								snake_case_ :			List[str]            =      AlbertForPreTraining(config=_lowercase )
								model.to(_lowercase )
								model.eval()
								snake_case_ :			Tuple            =      model(
								    _lowercase				,     attention_mask=_lowercase				,     token_type_ids=_lowercase				,     labels=_lowercase				,     sentence_order_label=_lowercase				,     )
								self.parent.assertEqual(result.prediction_logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size) )
								self.parent.assertEqual(result.sop_logits.shape				,     (self.batch_size, config.num_labels) )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    int:







								'''simple docstring'''

								snake_case_ :			List[str]            =      AlbertForMaskedLM(config=_lowercase )
								model.to(_lowercase )
								model.eval()
								snake_case_ :			List[Any]            =      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  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    str:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      AlbertForQuestionAnswering(config=_lowercase )
								model.to(_lowercase )
								model.eval()
								snake_case_ :			Tuple            =      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  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    Any:







								'''simple docstring'''

								snake_case_ :			int            =      self.num_labels
								snake_case_ :			Any            =      AlbertForSequenceClassification(_lowercase )
								model.to(_lowercase )
								model.eval()
								snake_case_ :			Dict            =      model(_lowercase				,     attention_mask=_lowercase				,     token_type_ids=_lowercase				,     labels=_lowercase )
								self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels) )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    Any:







								'''simple docstring'''

								snake_case_ :			Any            =      self.num_labels
								snake_case_ :			List[str]            =      AlbertForTokenClassification(config=_lowercase )
								model.to(_lowercase )
								model.eval()
								snake_case_ :			Tuple            =      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  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    int:







								'''simple docstring'''

								snake_case_ :			str            =      self.num_choices
								snake_case_ :			Any            =      AlbertForMultipleChoice(config=_lowercase )
								model.to(_lowercase )
								model.eval()
								snake_case_ :			int            =      input_ids.unsqueeze(1 ).expand(-1				,     self.num_choices				,     -1 ).contiguous()
								snake_case_ :			List[str]            =      token_type_ids.unsqueeze(1 ).expand(-1				,     self.num_choices				,     -1 ).contiguous()
								snake_case_ :			List[str]            =      input_mask.unsqueeze(1 ).expand(-1				,     self.num_choices				,     -1 ).contiguous()
								snake_case_ :			Optional[int]            =      model(
								    _lowercase				,     attention_mask=_lowercase				,     token_type_ids=_lowercase				,     labels=_lowercase				,     )
								self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_choices) )





		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								snake_case_ :			Tuple            =      self.prepare_config_and_inputs()
								(
								    (
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		
								) :			Union[str, Any]            =      config_and_inputs
								snake_case_ :			int            =      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_torch
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase      	=							(
		    (
		        AlbertModel,
		        AlbertForPreTraining,
		        AlbertForMaskedLM,
		        AlbertForMultipleChoice,
		        AlbertForSequenceClassification,
		        AlbertForTokenClassification,
		        AlbertForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		_lowerCamelCase      	=							(
		    {
		        '''feature-extraction''': AlbertModel,
		        '''fill-mask''': AlbertForMaskedLM,
		        '''question-answering''': AlbertForQuestionAnswering,
		        '''text-classification''': AlbertForSequenceClassification,
		        '''token-classification''': AlbertForTokenClassification,
		        '''zero-shot''': AlbertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		_lowerCamelCase      	=							True





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase=False )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			List[str]            =      super()._prepare_for_class(_lowercase				,     _lowercase				,     return_labels=_lowercase )

								if return_labels:
														if model_class in get_values(_lowercase ):
																				snake_case_ :			List[str]            =      torch.zeros(
																				    (self.model_tester.batch_size, self.model_tester.seq_length)				,     dtype=torch.long				,     device=_lowercase )
																				snake_case_ :			List[str]            =      torch.zeros(
																				    self.model_tester.batch_size				,     dtype=torch.long				,     device=_lowercase )
								return inputs_dict





		def  UpperCAmelCase__				(       self )			->    List[Any]:







								'''simple docstring'''

								snake_case_ :			Tuple            =      AlbertModelTester(self )
								snake_case_ :			str            =      ConfigTester(self				,     config_class=_lowercase				,     hidden_size=3_7 )





		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								self.config_tester.run_common_tests()





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			Dict            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    Union[str, Any]:







								'''simple docstring'''

								snake_case_ :			Dict            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								snake_case_ :			Any            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    List[str]:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			Dict            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    Optional[int]:







								'''simple docstring'''

								snake_case_ :			Any            =      self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
														snake_case_ :			List[Any]            =      type
														self.model_tester.create_and_check_model(*_lowercase )





		@slow
		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_ :			Union[str, Any]            =      AlbertModel.from_pretrained(_lowercase )
														self.assertIsNotNone(_lowercase )




@require_torch
class  _lowerCAmelCase     (					unittest.TestCase		):





		"""simple docstring"""






		@slow
		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_ :			Any            =      AlbertModel.from_pretrained("""albert-base-v2""" )
								snake_case_ :			str            =      torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
								snake_case_ :			Union[str, Any]            =      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
								with torch.no_grad():
														snake_case_ :			Optional[int]            =      model(_lowercase				,     attention_mask=_lowercase )[0]
								snake_case_ :			int            =      torch.Size((1, 1_1, 7_6_8) )
								self.assertEqual(output.shape				,     _lowercase )
								snake_case_ :			Any            =      torch.tensor(
								    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )

								self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,     _lowercase				,     atol=1E-4 ) )







import heapq
import sys

import numpy as np

_lowerCamelCase      :						Any							=  tuple[int, int]


class 							UpperCamelCase_       :

    '''simple docstring'''






    def __init__(     self :      Any)		->str:





           '''simple docstring'''

           A__ 					=       []
           A__ 					=       set()


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->List[str]:





           '''simple docstring'''

           if not self.empty():
                  return self.elements[0][0]
           else:
                  return float('''inf''')


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->str:





           '''simple docstring'''

           return len(self.elements) == 0


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      Optional[Any])		->List[str]:





           '''simple docstring'''

           if item not in self.set:
                  heapq.heappush(self.elements   ,					(priority, item))
                  self.set.add(UpperCAmelCase__)
           else:
                  # update
                  # print("update", item)
                  A__ 					=       []
                  ((A__)					,				(A__)) 					=       heapq.heappop(self.elements)
                  while x != item:
                         temp.append((pri, x))
                         ((A__)					,				(A__)) 					=       heapq.heappop(self.elements)
                  temp.append((priority, item))
                  for pro, xxx in temp:
                         heapq.heappush(self.elements   ,					(pro, xxx))


    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      List[Any])		->Union[str, Any]:





           '''simple docstring'''

           if item in self.set:
                  self.set.remove(UpperCAmelCase__)
                  A__ 					=       []
                  ((A__)					,				(A__)) 					=       heapq.heappop(self.elements)
                  while x != item:
                         temp.append((pro, x))
                         ((A__)					,				(A__)) 					=       heapq.heappop(self.elements)
                  for prito, yyy in temp:
                         heapq.heappush(self.elements   ,					(prito, yyy))


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->List[str]:





           '''simple docstring'''

           return self.elements[0][1]





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->int:





           '''simple docstring'''

           ((A__)					,				(A__)) 					=       heapq.heappop(self.elements)
           self.set.remove(UpperCAmelCase__)
           return (priority, item)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			str:



       """simple docstring"""

       A__ 					=       np.array(lowercase_		)
       A__ 					=       np.array(lowercase_		)
       return np.linalg.norm(a - b		)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			str:



       """simple docstring"""

       return consistent_heuristic(lowercase_					,							lowercase_		) // t







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Union[str, Any]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)  ->			Optional[int]:



       """simple docstring"""

       A__ 					=       g_function[start] + Wa * heuristics[i](lowercase_					,							lowercase_		)
       return ans







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			str:



       """simple docstring"""

       A__ 					=       np.chararray((n, n)		)
       for i in range(lowercase_		):
              for j in range(lowercase_		):
                     A__ 					=       '''*'''

       for i in range(lowercase_		):
              for j in range(lowercase_		):
                     if (j, (n - 1) - i) in blocks:
                            A__ 					=       '''#'''

       A__ 					=       '''-'''
       A__ 					=       back_pointer[goal]
       while x != start:
              ((A__)					,				(A__)) 					=       x
              # print(x)
              A__ 					=       '''-'''
              A__ 					=       back_pointer[x]
       A__ 					=       '''-'''

       for i in range(lowercase_		):
              for j in range(lowercase_		):
                     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__ 					=       back_pointer[goal]
       while x != start:
              print(lowercase_					,							end=''' '''		)
              A__ 					=       back_pointer[x]
       print(lowercase_		)
       sys.exit()







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Dict:



       """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 SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_					,							lowercase_					,							lowercase_					,							lowercase_					,							lowercase_					,							)  ->			Union[str, Any]:



       """simple docstring"""

       for itera in range(lowercase_		):
              open_list[itera].remove_element(lowercase_		)
       # print("s", s)
       # print("j", j)
       ((A__)					,				(A__)) 					=       s
       A__ 					=       (x - 1, y)
       A__ 					=       (x + 1, y)
       A__ 					=       (x, y + 1)
       A__ 					=       (x, y - 1)

       for neighbours in [left, right, up, down]:
              if neighbours not in blocks:
                     if valid(lowercase_		) and neighbours not in visited:
                            # print("neighbour", neighbours)
                            visited.add(lowercase_		)
                            A__ 					=       -1
                            A__ 					=       float('''inf'''		)

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







def SCREAMING_SNAKE_CASE							(			)  ->			Optional[int]:



       """simple docstring"""

       A__ 					=       []
       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


_lowerCamelCase      :						Dict							=  {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

_lowerCamelCase      :						Optional[Any]							=  [
    (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),
]
_lowerCamelCase      :						Optional[int]							=  make_common_ground()


_lowerCamelCase      :						Optional[Any]							=  blocks_blk
# hyper parameters
_lowerCamelCase      :						Optional[int]							=  1
_lowerCamelCase      :						Optional[int]							=  1
_lowerCamelCase      :						List[Any]							=  20
_lowerCamelCase      :						Any							=  3  # one consistent and two other inconsistent

# start and end destination
_lowerCamelCase      :						str							=  (0, 0)
_lowerCamelCase      :						Tuple							=  (n - 1, n - 1)

_lowerCamelCase      :						int							=  1







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			str:



       """simple docstring"""

       A__ 					=       {start: 0, goal: float('''inf'''		)}
       A__ 					=       {start: -1, goal: -1}
       A__ 					=       []
       A__ 					=       set()

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

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


