from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TFMobileBertForMaskedLM,
          TFMobileBertForMultipleChoice,
          TFMobileBertForNextSentencePrediction,
          TFMobileBertForPreTraining,
          TFMobileBertForQuestionAnswering,
          TFMobileBertForSequenceClassification,
          TFMobileBertForTokenClassification,
          TFMobileBertModel,
      )


@require_tf
class 	lowerCamelCase       (UpperCAmelCase__	,   UpperCAmelCase__	,   unittest.TestCase       ):
  """simple docstring"""







  lowerCamelCase__  =    (
      (
          TFMobileBertModel,
          TFMobileBertForMaskedLM,
          TFMobileBertForNextSentencePrediction,
          TFMobileBertForPreTraining,
          TFMobileBertForQuestionAnswering,
          TFMobileBertForSequenceClassification,
          TFMobileBertForTokenClassification,
          TFMobileBertForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  lowerCamelCase__  =    (
      {
          '''feature-extraction''': TFMobileBertModel,
          '''fill-mask''': TFMobileBertForMaskedLM,
          '''question-answering''': TFMobileBertForQuestionAnswering,
          '''text-classification''': TFMobileBertForSequenceClassification,
          '''token-classification''': TFMobileBertForTokenClassification,
          '''zero-shot''': TFMobileBertForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  lowerCamelCase__  =    False
  lowerCamelCase__  =    False







  def  __A  (				self			:							Optional[Any]	,  __magic_name__			:							Dict	,  __magic_name__			:							Tuple	,  __magic_name__			:							str=False )	->						Optional[Any]:
        SCREAMING_SNAKE_CASE_    =	super()._prepare_for_class(UpperCAmelCase__	,  UpperCAmelCase__	,  return_labels=UpperCAmelCase__ )

        if return_labels:
              if model_class in get_values(UpperCAmelCase__ ):
                    SCREAMING_SNAKE_CASE_    =	tf.zeros(self.model_tester.batch_size	,  dtype=tf.intaa )

        return inputs_dict


  class 	lowerCamelCase       (UpperCAmelCase__       ):
    """simple docstring"""







    def __init__(				self			:							List[Any]	,  __magic_name__			:							str	,  __magic_name__			:							int=13	,  __magic_name__			:							str=7	,  __magic_name__			:							Optional[Any]=True	,  __magic_name__			:							Optional[int]=True	,  __magic_name__			:							Union[str, Any]=True	,  __magic_name__			:							Optional[int]=True	,  __magic_name__			:							str=99	,  __magic_name__			:							List[str]=32	,  __magic_name__			:							Optional[int]=32	,  __magic_name__			:							Any=2	,  __magic_name__			:							List[str]=4	,  __magic_name__			:							Optional[Any]=37	,  __magic_name__			:							Optional[int]="gelu"	,  __magic_name__			:							Any=0.1	,  __magic_name__			:							Optional[Any]=0.1	,  __magic_name__			:							List[Any]=512	,  __magic_name__			:							Tuple=16	,  __magic_name__			:							Any=2	,  __magic_name__			:							Dict=0.02	,  __magic_name__			:							int=3	,  __magic_name__			:							List[str]=4	,  __magic_name__			:							Tuple=None	,  )	->						Any:
          SCREAMING_SNAKE_CASE_    =	parent
          SCREAMING_SNAKE_CASE_    =	batch_size
          SCREAMING_SNAKE_CASE_    =	seq_length
          SCREAMING_SNAKE_CASE_    =	is_training
          SCREAMING_SNAKE_CASE_    =	use_input_mask
          SCREAMING_SNAKE_CASE_    =	use_token_type_ids
          SCREAMING_SNAKE_CASE_    =	use_labels
          SCREAMING_SNAKE_CASE_    =	vocab_size
          SCREAMING_SNAKE_CASE_    =	hidden_size
          SCREAMING_SNAKE_CASE_    =	num_hidden_layers
          SCREAMING_SNAKE_CASE_    =	num_attention_heads
          SCREAMING_SNAKE_CASE_    =	intermediate_size
          SCREAMING_SNAKE_CASE_    =	hidden_act
          SCREAMING_SNAKE_CASE_    =	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_    =	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_    =	max_position_embeddings
          SCREAMING_SNAKE_CASE_    =	type_vocab_size
          SCREAMING_SNAKE_CASE_    =	type_sequence_label_size
          SCREAMING_SNAKE_CASE_    =	initializer_range
          SCREAMING_SNAKE_CASE_    =	num_labels
          SCREAMING_SNAKE_CASE_    =	num_choices
          SCREAMING_SNAKE_CASE_    =	scope
          SCREAMING_SNAKE_CASE_    =	embedding_size







    def  __A  (				self			:							Optional[Any] )	->						Tuple:
          SCREAMING_SNAKE_CASE_    =	ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size )

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

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

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

          SCREAMING_SNAKE_CASE_    =	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	,  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	,  embedding_size=self.embedding_size	,  )

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







    def  __A  (				self			:							Union[str, Any]	,  __magic_name__			:							List[str]	,  __magic_name__			:							str	,  __magic_name__			:							str	,  __magic_name__			:							List[Any]	,  __magic_name__			:							Any	,  __magic_name__			:							Union[str, Any]	,  __magic_name__			:							Optional[Any] )	->						Any:
          SCREAMING_SNAKE_CASE_    =	TFMobileBertModel(config=UpperCAmelCase__ )
          SCREAMING_SNAKE_CASE_    =	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          SCREAMING_SNAKE_CASE_    =	model(UpperCAmelCase__ )

          SCREAMING_SNAKE_CASE_    =	[input_ids, input_mask]
          SCREAMING_SNAKE_CASE_    =	model(UpperCAmelCase__ )

          SCREAMING_SNAKE_CASE_    =	model(UpperCAmelCase__ )

          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			:							List[str]	,  __magic_name__			:							int	,  __magic_name__			:							str	,  __magic_name__			:							Any	,  __magic_name__			:							int	,  __magic_name__			:							Optional[int]	,  __magic_name__			:							Any	,  __magic_name__			:							Tuple )	->						Optional[Any]:
          SCREAMING_SNAKE_CASE_    =	TFMobileBertForMaskedLM(config=UpperCAmelCase__ )
          SCREAMING_SNAKE_CASE_    =	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          SCREAMING_SNAKE_CASE_    =	model(UpperCAmelCase__ )
          self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.vocab_size) )







    def  __A  (				self			:							Dict	,  __magic_name__			:							List[Any]	,  __magic_name__			:							Optional[Any]	,  __magic_name__			:							List[Any]	,  __magic_name__			:							int	,  __magic_name__			:							int	,  __magic_name__			:							List[Any]	,  __magic_name__			:							Optional[Any] )	->						int:
          SCREAMING_SNAKE_CASE_    =	TFMobileBertForNextSentencePrediction(config=UpperCAmelCase__ )
          SCREAMING_SNAKE_CASE_    =	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          SCREAMING_SNAKE_CASE_    =	model(UpperCAmelCase__ )
          self.parent.assertEqual(result.logits.shape	,  (self.batch_size, 2) )







    def  __A  (				self			:							Dict	,  __magic_name__			:							Dict	,  __magic_name__			:							List[Any]	,  __magic_name__			:							Union[str, Any]	,  __magic_name__			:							Dict	,  __magic_name__			:							Dict	,  __magic_name__			:							List[Any]	,  __magic_name__			:							int )	->						List[Any]:
          SCREAMING_SNAKE_CASE_    =	TFMobileBertForPreTraining(config=UpperCAmelCase__ )
          SCREAMING_SNAKE_CASE_    =	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          SCREAMING_SNAKE_CASE_    =	model(UpperCAmelCase__ )
          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  __A  (				self			:							Tuple	,  __magic_name__			:							List[Any]	,  __magic_name__			:							Union[str, Any]	,  __magic_name__			:							Any	,  __magic_name__			:							Any	,  __magic_name__			:							int	,  __magic_name__			:							List[str]	,  __magic_name__			:							Tuple )	->						Dict:
          SCREAMING_SNAKE_CASE_    =	self.num_labels
          SCREAMING_SNAKE_CASE_    =	TFMobileBertForSequenceClassification(config=UpperCAmelCase__ )
          SCREAMING_SNAKE_CASE_    =	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          SCREAMING_SNAKE_CASE_    =	model(UpperCAmelCase__ )
          self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels) )







    def  __A  (				self			:							Any	,  __magic_name__			:							List[Any]	,  __magic_name__			:							int	,  __magic_name__			:							str	,  __magic_name__			:							Any	,  __magic_name__			:							Optional[Any]	,  __magic_name__			:							str	,  __magic_name__			:							int )	->						Dict:
          SCREAMING_SNAKE_CASE_    =	self.num_choices
          SCREAMING_SNAKE_CASE_    =	TFMobileBertForMultipleChoice(config=UpperCAmelCase__ )
          SCREAMING_SNAKE_CASE_    =	tf.tile(tf.expand_dims(UpperCAmelCase__	,  1 )	,  (1, self.num_choices, 1) )
          SCREAMING_SNAKE_CASE_    =	tf.tile(tf.expand_dims(UpperCAmelCase__	,  1 )	,  (1, self.num_choices, 1) )
          SCREAMING_SNAKE_CASE_    =	tf.tile(tf.expand_dims(UpperCAmelCase__	,  1 )	,  (1, self.num_choices, 1) )
          SCREAMING_SNAKE_CASE_    =	{
              "input_ids": multiple_choice_inputs_ids,
              "attention_mask": multiple_choice_input_mask,
              "token_type_ids": multiple_choice_token_type_ids,
          }
          SCREAMING_SNAKE_CASE_    =	model(UpperCAmelCase__ )
          self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_choices) )







    def  __A  (				self			:							Dict	,  __magic_name__			:							Union[str, Any]	,  __magic_name__			:							int	,  __magic_name__			:							Optional[int]	,  __magic_name__			:							List[Any]	,  __magic_name__			:							Any	,  __magic_name__			:							Dict	,  __magic_name__			:							Optional[int] )	->						int:
          SCREAMING_SNAKE_CASE_    =	self.num_labels
          SCREAMING_SNAKE_CASE_    =	TFMobileBertForTokenClassification(config=UpperCAmelCase__ )
          SCREAMING_SNAKE_CASE_    =	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          SCREAMING_SNAKE_CASE_    =	model(UpperCAmelCase__ )
          self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.num_labels) )







    def  __A  (				self			:							List[Any]	,  __magic_name__			:							Optional[Any]	,  __magic_name__			:							Dict	,  __magic_name__			:							List[Any]	,  __magic_name__			:							Tuple	,  __magic_name__			:							Union[str, Any]	,  __magic_name__			:							Any	,  __magic_name__			:							Union[str, Any] )	->						Union[str, Any]:
          SCREAMING_SNAKE_CASE_    =	TFMobileBertForQuestionAnswering(config=UpperCAmelCase__ )
          SCREAMING_SNAKE_CASE_    =	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          SCREAMING_SNAKE_CASE_    =	model(UpperCAmelCase__ )
          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			:							Any )	->						str:
          SCREAMING_SNAKE_CASE_    =	self.prepare_config_and_inputs()
          (
              (
              SCREAMING_SNAKE_CASE_				
          )  ,			(
              SCREAMING_SNAKE_CASE_				
          )  ,			(
              SCREAMING_SNAKE_CASE_				
          )  ,			(
              SCREAMING_SNAKE_CASE_				
          )  ,			(
              SCREAMING_SNAKE_CASE_				
          )  ,			(
              SCREAMING_SNAKE_CASE_				
          )  ,			(
              SCREAMING_SNAKE_CASE_				
          )  ,							
          )    =	config_and_inputs
          SCREAMING_SNAKE_CASE_    =	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict







  def  __A  (				self			:							Any )	->						Union[str, Any]:
        SCREAMING_SNAKE_CASE_    =	TFMobileBertModelTest.TFMobileBertModelTester(self )
        SCREAMING_SNAKE_CASE_    =	ConfigTester(self	,  config_class=UpperCAmelCase__	,  hidden_size=37 )







  def  __A  (				self			:							Tuple )	->						Optional[int]:
        self.config_tester.run_common_tests()







  def  __A  (				self			:							Optional[Any] )	->						Dict:
        SCREAMING_SNAKE_CASE_    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_model(*UpperCAmelCase__ )







  def  __A  (				self			:							List[str] )	->						Tuple:
        SCREAMING_SNAKE_CASE_    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_masked_lm(*UpperCAmelCase__ )







  def  __A  (				self			:							Union[str, Any] )	->						Dict:
        SCREAMING_SNAKE_CASE_    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_multiple_choice(*UpperCAmelCase__ )







  def  __A  (				self			:							Tuple )	->						Dict:
        SCREAMING_SNAKE_CASE_    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*UpperCAmelCase__ )







  def  __A  (				self			:							List[Any] )	->						Union[str, Any]:
        SCREAMING_SNAKE_CASE_    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_pretraining(*UpperCAmelCase__ )







  def  __A  (				self			:							int )	->						Optional[int]:
        SCREAMING_SNAKE_CASE_    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_question_answering(*UpperCAmelCase__ )







  def  __A  (				self			:							Tuple )	->						Optional[int]:
        SCREAMING_SNAKE_CASE_    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_sequence_classification(*UpperCAmelCase__ )







  def  __A  (				self			:							Tuple )	->						List[str]:
        SCREAMING_SNAKE_CASE_    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_token_classification(*UpperCAmelCase__ )







  @slow
  def  __A  (				self			:							str )	->						List[Any]:

        for model_name in ["google/mobilebert-uncased"]:
              SCREAMING_SNAKE_CASE_    =	TFMobileBertModel.from_pretrained(UpperCAmelCase__ )
              self.assertIsNotNone(UpperCAmelCase__ )



@require_tf
class 	lowerCamelCase       (unittest.TestCase       ):
  """simple docstring"""







  @slow
  def  __A  (				self			:							Optional[Any] )	->						Any:
        SCREAMING_SNAKE_CASE_    =	TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased" )
        SCREAMING_SNAKE_CASE_    =	tf.constant([[0, 1, 2, 3, 4, 5]] )
        SCREAMING_SNAKE_CASE_    =	model(UpperCAmelCase__ )[0]

        SCREAMING_SNAKE_CASE_    =	[1, 6, 30_522]
        self.assertEqual(output.shape	,  UpperCAmelCase__ )

        SCREAMING_SNAKE_CASE_    =	tf.constant(
            [
                [
                    [-4.591_9547, -9.24_8295, -9.64_5256],
                    [-6.730_6175, -6.44_0284, -6.605_2837],
                    [-7.274_3506, -6.784_7915, -6.02_4673],
                ]
            ] )
        tf.debugging.assert_near(output[:, :3, :3]	,  UpperCAmelCase__	,  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)


