




import unittest

from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
 import torch

 from transformers import (
     MraForMaskedLM,
     MraForMultipleChoice,
     MraForQuestionAnswering,
     MraForSequenceClassification,
     MraForTokenClassification,
     MraModel,
 )
 from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Union[str, Any] ,       snake_case					: Any ,       snake_case					: Optional[Any]=2 ,       snake_case					: Dict=8 ,       snake_case					: Any=True ,       snake_case					: Dict=True ,       snake_case					: List[Any]=True ,       snake_case					: List[Any]=True ,       snake_case					: Tuple=99 ,       snake_case					: int=16 ,       snake_case					: Optional[int]=5 ,       snake_case					: Optional[int]=2 ,       snake_case					: Dict=36 ,       snake_case					: List[Any]="gelu" ,       snake_case					: Any=0.0 ,       snake_case					: Union[str, Any]=0.0 ,       snake_case					: Any=512 ,       snake_case					: Any=16 ,       snake_case					: Dict=2 ,       snake_case					: Dict=0.02 ,       snake_case					: str=3 ,       snake_case					: Any=4 ,       snake_case					: List[str]=None ,       ):
          __UpperCamelCase  		=						parent
          __UpperCamelCase  		=						batch_size
          __UpperCamelCase  		=						seq_length
          __UpperCamelCase  		=						is_training
          __UpperCamelCase  		=						use_input_mask
          __UpperCamelCase  		=						use_token_type_ids
          __UpperCamelCase  		=						use_labels
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						type_vocab_size
          __UpperCamelCase  		=						type_sequence_label_size
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						num_labels
          __UpperCamelCase  		=						num_choices
          __UpperCamelCase  		=						scope







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size				)

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

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

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

          __UpperCamelCase  		=						self.get_config()

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







      def       snake_case	(     self					: Optional[Any]				):
          return MraConfig(
              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 ,       is_decoder=snake_case ,       initializer_range=self.initializer_range ,       )







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.get_config()
          __UpperCamelCase  		=						300
          return config







      def       snake_case	(     self					: List[Any]				):
          (
              (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      
          )  		=						self.prepare_config_and_inputs()

          __UpperCamelCase  		=						True
          __UpperCamelCase  		=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
          __UpperCamelCase  		=						ids_tensor([self.batch_size, self.seq_length] ,       vocab_size=2				)

          return (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
              encoder_hidden_states,
              encoder_attention_mask,
          )







      def       snake_case	(     self					: Dict ,       snake_case					: Optional[int] ,       snake_case					: List[Any] ,       snake_case					: Any ,       snake_case					: Tuple ,       snake_case					: Optional[int] ,       snake_case					: int ,       snake_case					: List[Any]				):
          __UpperCamelCase  		=						MraModel(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case				)
          __UpperCamelCase  		=						model(snake_case ,       token_type_ids=snake_case				)
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size)				)







      def       snake_case	(     self					: List[str] ,       snake_case					: Optional[int] ,       snake_case					: List[str] ,       snake_case					: Union[str, Any] ,       snake_case					: List[Any] ,       snake_case					: List[Any] ,       snake_case					: Optional[int] ,       snake_case					: Any ,       snake_case					: List[Any] ,       snake_case					: Optional[int] ,       ):
          __UpperCamelCase  		=						True
          __UpperCamelCase  		=						MraModel(snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(
              snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case ,       encoder_hidden_states=snake_case ,       encoder_attention_mask=snake_case ,       )
          __UpperCamelCase  		=						model(
              snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case ,       encoder_hidden_states=snake_case ,       )
          __UpperCamelCase  		=						model(snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case				)
          self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size)				)







      def       snake_case	(     self					: Dict ,       snake_case					: str ,       snake_case					: Optional[Any] ,       snake_case					: List[Any] ,       snake_case					: List[Any] ,       snake_case					: List[str] ,       snake_case					: Any ,       snake_case					: Optional[int]				):
          __UpperCamelCase  		=						MraForMaskedLM(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case ,       labels=snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.vocab_size)				)







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: str ,       snake_case					: Dict ,       snake_case					: List[str] ,       snake_case					: Tuple ,       snake_case					: Any ,       snake_case					: List[Any] ,       snake_case					: Any				):
          __UpperCamelCase  		=						MraForQuestionAnswering(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(
              snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case ,       start_positions=snake_case ,       end_positions=snake_case ,       )
          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					: Any ,       snake_case					: int ,       snake_case					: Tuple ,       snake_case					: int ,       snake_case					: List[Any] ,       snake_case					: Union[str, Any] ,       snake_case					: int ,       snake_case					: Optional[int]				):
          __UpperCamelCase  		=						self.num_labels
          __UpperCamelCase  		=						MraForSequenceClassification(snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case ,       labels=snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels)				)







      def       snake_case	(     self					: Tuple ,       snake_case					: Union[str, Any] ,       snake_case					: Optional[int] ,       snake_case					: Dict ,       snake_case					: List[Any] ,       snake_case					: Any ,       snake_case					: Tuple ,       snake_case					: Union[str, Any]				):
          __UpperCamelCase  		=						self.num_labels
          __UpperCamelCase  		=						MraForTokenClassification(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case ,       labels=snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.num_labels)				)







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







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()
          (
              (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      
          )  		=						config_and_inputs
          __UpperCamelCase  		=						{'''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							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[Any]    =					(
          (
              MraModel,
              MraForMaskedLM,
              MraForMultipleChoice,
              MraForQuestionAnswering,
              MraForSequenceClassification,
              MraForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__			: List[Any]    =					False
      lowerCAmelCase__			: Union[str, Any]    =					False
      lowerCAmelCase__			: List[str]    =					False
      lowerCAmelCase__			: Union[str, Any]    =					False

      lowerCAmelCase__			: str    =					()







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						MraModelTester(self				)
          __UpperCamelCase  		=						ConfigTester(self ,       config_class=snake_case ,       hidden_size=37				)







      def       snake_case	(     self					: int				):
          self.config_tester.run_common_tests()







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case				)







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              __UpperCamelCase  		=						type
              self.model_tester.create_and_check_model(*snake_case				)







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case				)







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*snake_case				)







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*snake_case				)







      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*snake_case				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*snake_case				)







      @slow
      def       snake_case	(     self					: List[Any]				):
          for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase  		=						MraModel.from_pretrained(snake_case				)
              self.assertIsNotNone(snake_case				)







      @unittest.skip(reason='''MRA does not output attentions'''				)
      def       snake_case	(     self					: List[str]				):
          return







@require_torch
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      @slow
      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						MraModel.from_pretrained('''uw-madison/mra-base-512-4'''				)
          __UpperCamelCase  		=						torch.arange(256				).unsqueeze(0				)

          with torch.no_grad():
              __UpperCamelCase  		=						model(snake_case				)[0]

          __UpperCamelCase  		=						torch.Size((1, 256, 768)				)
          self.assertEqual(output.shape ,       snake_case				)

          __UpperCamelCase  		=						torch.tensor(
              [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]]				)

          self.assertTrue(torch.allclose(output[:, :3, :3] ,       snake_case ,       atol=1E-4				)				)







      @slow
      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''				)
          __UpperCamelCase  		=						torch.arange(256				).unsqueeze(0				)

          with torch.no_grad():
              __UpperCamelCase  		=						model(snake_case				)[0]

          __UpperCamelCase  		=						50265

          __UpperCamelCase  		=						torch.Size((1, 256, vocab_size)				)
          self.assertEqual(output.shape ,       snake_case				)

          __UpperCamelCase  		=						torch.tensor(
              [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]]				)

          self.assertTrue(torch.allclose(output[:, :3, :3] ,       snake_case ,       atol=1E-4				)				)







      @slow
      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''				)
          __UpperCamelCase  		=						torch.arange(4096				).unsqueeze(0				)

          with torch.no_grad():
              __UpperCamelCase  		=						model(snake_case				)[0]

          __UpperCamelCase  		=						50265

          __UpperCamelCase  		=						torch.Size((1, 4096, vocab_size)				)
          self.assertEqual(output.shape ,       snake_case				)

          __UpperCamelCase  		=						torch.tensor(
              [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]]				)

          self.assertTrue(torch.allclose(output[:, :3, :3] ,       snake_case ,       atol=1E-4				)				)












import heapq
import sys

import numpy as np

a_   		=     tuple[int, int]

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Dict				):
          __UpperCamelCase  		=						[]
          __UpperCamelCase  		=						set()







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







      def       snake_case	(     self					: Optional[Any]				):
          return len(self.elements				) == 0







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







      def       snake_case	(     self					: Any ,       snake_case					: int				):
          if item in self.set:
              self.set.remove(snake_case				)
              __UpperCamelCase  		=						[]
              ((__UpperCamelCase)		,      (__UpperCamelCase))  		=						heapq.heappop(self.elements				)
              while x != item:
                  temp.append((pro, x)				)
                  ((__UpperCamelCase)		,      (__UpperCamelCase))  		=						heapq.heappop(self.elements				)
              for prito, yyy in temp:
                  heapq.heappush(self.elements ,       (prito, yyy)				)







      def       snake_case	(     self					: Tuple				):
          return self.elements[0][1]







      def       snake_case	(     self					: List[str]				):
          ((__UpperCamelCase)		,      (__UpperCamelCase))  		=						heapq.heappop(self.elements				)
          self.set.remove(snake_case				)
          return (priority, item)







def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    return consistent_heuristic(lowercase_					,     lowercase_      ) // t




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


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




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

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


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

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

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

    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:-'''      )
    __UpperCamelCase  		=						back_pointer[goal]
    while x != start:
        print(lowercase_					,     end=''' '''      )
        __UpperCamelCase  		=						back_pointer[x]
    print(lowercase_      )
    sys.exit()




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	List[Any]:

    """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_					,     )       ->	Tuple:

    """simple docstring"""


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

            if valid(lowercase_      ) and g_function[neighbours] > g_function[s] + 1:
                __UpperCamelCase  		=						g_function[s] + 1
                __UpperCamelCase  		=						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   (  )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						[]
    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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	str:

    """simple docstring"""


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

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

    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						[]
    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:
                    __UpperCamelCase		,      __UpperCamelCase  		=						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:
                    __UpperCamelCase  		=						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)






