



import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import MaskFormerSwinBackbone
       from transformers.models.maskformer import MaskFormerSwinModel



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Optional[int]			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			Optional[int]=13			,				lowerCAmelCase  :			str=32			,				lowerCAmelCase  :			Any=2			,				lowerCAmelCase  :			Dict=3			,				lowerCAmelCase  :			str=16			,				lowerCAmelCase  :			Union[str, Any]=[1, 2, 1]			,				lowerCAmelCase  :			List[str]=[2, 2, 4]			,				lowerCAmelCase  :			Tuple=2			,				lowerCAmelCase  :			Tuple=2.0			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			Dict=0.0			,				lowerCAmelCase  :			Tuple=0.0			,				lowerCAmelCase  :			List[str]=0.1			,				lowerCAmelCase  :			str="gelu"			,				lowerCAmelCase  :			Dict=False			,				lowerCAmelCase  :			Tuple=True			,				lowerCAmelCase  :			str=0.02			,				lowerCAmelCase  :			List[str]=1E-5			,				lowerCAmelCase  :			Any=True			,				lowerCAmelCase  :			Optional[Any]=None			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			Dict=10			,				lowerCAmelCase  :			Dict=8			,				lowerCAmelCase  :			str=["stage1", "stage2", "stage3"]			,				lowerCAmelCase  :			List[str]=[1, 2, 3]			,				)	->				Any:




         """simple docstring"""

         lowercase__     =  parent
         lowercase__     =  batch_size
         lowercase__     =  image_size
         lowercase__     =  patch_size
         lowercase__     =  num_channels
         lowercase__     =  embed_dim
         lowercase__     =  depths
         lowercase__     =  num_heads
         lowercase__     =  window_size
         lowercase__     =  mlp_ratio
         lowercase__     =  qkv_bias
         lowercase__     =  hidden_dropout_prob
         lowercase__     =  attention_probs_dropout_prob
         lowercase__     =  drop_path_rate
         lowercase__     =  hidden_act
         lowercase__     =  use_absolute_embeddings
         lowercase__     =  patch_norm
         lowercase__     =  layer_norm_eps
         lowercase__     =  initializer_range
         lowercase__     =  is_training
         lowercase__     =  scope
         lowercase__     =  use_labels
         lowercase__     =  type_sequence_label_size
         lowercase__     =  encoder_stride
         lowercase__     =  out_features
         lowercase__     =  out_indices


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




         """simple docstring"""

         lowercase__     =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

         lowercase__     =  None
         if self.use_labels:
               lowercase__     =  ids_tensor([self.batch_size]			,				self.type_sequence_label_size)

         lowercase__     =  self.get_config()

         return config, pixel_values, labels


   def 			UpperCAmelCase		(						self  :			Any)	->				Optional[Any]:




         """simple docstring"""

         return MaskFormerSwinConfig(
             image_size=self.image_size			,				patch_size=self.patch_size			,				num_channels=self.num_channels			,				embed_dim=self.embed_dim			,				depths=self.depths			,				num_heads=self.num_heads			,				window_size=self.window_size			,				mlp_ratio=self.mlp_ratio			,				qkv_bias=self.qkv_bias			,				hidden_dropout_prob=self.hidden_dropout_prob			,				attention_probs_dropout_prob=self.attention_probs_dropout_prob			,				drop_path_rate=self.drop_path_rate			,				hidden_act=self.hidden_act			,				use_absolute_embeddings=self.use_absolute_embeddings			,				path_norm=self.patch_norm			,				layer_norm_eps=self.layer_norm_eps			,				initializer_range=self.initializer_range			,				encoder_stride=self.encoder_stride			,				out_features=self.out_features			,				out_indices=self.out_indices			,				)


   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			Optional[int]			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Any)	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  MaskFormerSwinModel(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__     =  model(lowerCAmelCase)

         lowercase__     =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
         lowercase__     =  int(config.embed_dim * 2 ** (len(config.depths) - 1))

         self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, expected_seq_len, expected_dim))


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			Optional[Any])	->				List[str]:




         """simple docstring"""

         lowercase__     =  MaskFormerSwinBackbone(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__     =  model(lowerCAmelCase)

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps)			,				len(config.out_features))
         self.parent.assertListEqual(list(result.feature_maps[0].shape)			,				[13, 16, 16, 16])

         # verify channels
         self.parent.assertEqual(len(model.channels)			,				len(config.out_features))
         self.parent.assertListEqual(model.channels			,				[16, 32, 64])

         # verify ValueError
         with self.parent.assertRaises(lowerCAmelCase):
               lowercase__     =  ['''stem''']
               lowercase__     =  MaskFormerSwinBackbone(config=lowerCAmelCase)



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




         """simple docstring"""

         lowercase__     =  self.prepare_config_and_inputs()
         lowercase__     =  config_and_inputs
         lowercase__     =  {'''pixel_values''': pixel_values}
         return config, inputs_dict



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

   '''simple docstring'''




   A    :  str       				=    (
       (
           MaskFormerSwinModel,
           MaskFormerSwinBackbone,
       )
       if is_torch_available()
       else ()
   )
   A    :  Any       				=    {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
   A    :  Any       				=    False
   A    :  Dict       				=    False
   A    :  Optional[int]       				=    False
   A    :  Dict       				=    False
   A    :  List[Any]       				=    False


   def 			UpperCAmelCase		(						self  :			Any)	->				str:




         """simple docstring"""

         lowercase__     =  MaskFormerSwinModelTester(self)
         lowercase__     =  ConfigTester(self			,				config_class=lowerCAmelCase			,				embed_dim=37)


   @require_torch_multi_gpu
   @unittest.skip(
       reason=(
           '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
           ' `nn.DataParallel`'
       ))
   def 			UpperCAmelCase		(						self  :			Any)	->				str:




         """simple docstring"""

         pass


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




         """simple docstring"""

         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()


   def 			UpperCAmelCase		(						self  :			List[Any])	->				Optional[Any]:




         """simple docstring"""

         return


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Dict:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*lowerCAmelCase)


   @unittest.skip('Swin does not use inputs_embeds')
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Dict:




         """simple docstring"""

         pass


   @unittest.skip('Swin does not support feedforward chunking')
   def 			UpperCAmelCase		(						self  :			Tuple)	->				Dict:




         """simple docstring"""

         pass


   def 			UpperCAmelCase		(						self  :			str)	->				str:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase__     =  model_class(lowerCAmelCase)
               self.assertIsInstance(model.get_input_embeddings()			,				(nn.Module))
               lowercase__     =  model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase			,				nn.Linear))


   def 			UpperCAmelCase		(						self  :			Any)	->				str:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase__     =  model_class(lowerCAmelCase)
               lowercase__     =  inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase__     =  [*signature.parameters.keys()]

               lowercase__     =  ['''pixel_values''']
               self.assertListEqual(arg_names[:1]			,				lowerCAmelCase)


   @unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions')
   def 			UpperCAmelCase		(						self  :			str)	->				Any:




         """simple docstring"""

         pass


   @unittest.skip(reason='MaskFormerSwin is only used as an internal backbone')
   def 			UpperCAmelCase		(						self  :			List[str])	->				Optional[Any]:




         """simple docstring"""

         pass


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




         """simple docstring"""

         lowercase__     =  model_class(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()

         with torch.no_grad():
               lowercase__     =  model(**self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase))

         lowercase__     =  outputs.hidden_states

         lowercase__     =  getattr(
             self.model_tester			,				'expected_num_hidden_layers'			,				len(self.model_tester.depths) + 1)
         self.assertEqual(len(lowerCAmelCase)			,				lowerCAmelCase)

         # Swin has a different seq_length
         lowercase__     =  (
             config.patch_size
             if isinstance(config.patch_size			,				collections.abc.Iterable)
             else (config.patch_size, config.patch_size)
         )

         lowercase__     =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:])			,				[num_patches, self.model_tester.embed_dim]			,				)


   def 			UpperCAmelCase		(						self  :			List[Any])	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()

         lowercase__     =  (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size			,				collections.abc.Iterable)
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
               lowercase__     =  True
               self.check_hidden_states_output(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               lowercase__     =  True

               self.check_hidden_states_output(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__     =  3

         lowercase__     =  (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size			,				collections.abc.Iterable)
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         lowercase__     =  (
             config.patch_size
             if isinstance(config.patch_size			,				collections.abc.Iterable)
             else (config.patch_size, config.patch_size)
         )

         lowercase__     =  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         lowercase__     =  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
               lowercase__     =  True
               self.check_hidden_states_output(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				(padded_height, padded_width))

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               lowercase__     =  True
               self.check_hidden_states_output(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				(padded_height, padded_width))


   @unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints')
   def 			UpperCAmelCase		(						self  :			str)	->				List[str]:




         """simple docstring"""

         pass


   @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin')
   def 			UpperCAmelCase		(						self  :			int)	->				List[Any]:




         """simple docstring"""

         pass


   @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin')
   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				str:




         """simple docstring"""

         pass



   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Any:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()

         def set_nan_tensor_to_zero(lowerCAmelCase  :			List[str]):
               lowercase__     =  0
               return t

         def check_equivalence(lowerCAmelCase  :			Optional[int]			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			Optional[int]={}):
               with torch.no_grad():
                     lowercase__     =  model(**lowerCAmelCase			,				return_dict=lowerCAmelCase			,				**lowerCAmelCase)
                     lowercase__     =  model(**lowerCAmelCase			,				return_dict=lowerCAmelCase			,				**lowerCAmelCase).to_tuple()

                     def recursive_check(lowerCAmelCase  :			int			,				lowerCAmelCase  :			Tuple):
                           if isinstance(lowerCAmelCase			,				(List, Tuple)):
                                 for tuple_iterable_value, dict_iterable_value in zip(lowerCAmelCase			,				lowerCAmelCase):
                                       recursive_check(lowerCAmelCase			,				lowerCAmelCase)
                           elif isinstance(lowerCAmelCase			,				lowerCAmelCase):
                                 for tuple_iterable_value, dict_iterable_value in zip(
                                     tuple_object.values()			,				dict_object.values()):
                                       recursive_check(lowerCAmelCase			,				lowerCAmelCase)
                           elif tuple_object is None:
                                 return
                           else:
                                 self.assertTrue(
                                     torch.allclose(
                                         set_nan_tensor_to_zero(lowerCAmelCase)			,				set_nan_tensor_to_zero(lowerCAmelCase)			,				atol=1E-5)			,				msg=(
                                         'Tuple and dict output are not equal. Difference:'
                                         f''' {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:'''
                                         f''' {torch.isnan(lowerCAmelCase).any()} and `inf`: {torch.isinf(lowerCAmelCase)}. Dict has'''
                                         f''' `nan`: {torch.isnan(lowerCAmelCase).any()} and `inf`: {torch.isinf(lowerCAmelCase)}.'''
                                     )			,				)

                     recursive_check(lowerCAmelCase			,				lowerCAmelCase)

         for model_class in self.all_model_classes:
               lowercase__     =  model_class(lowerCAmelCase)
               model.to(lowerCAmelCase)
               model.eval()

               lowercase__     =  self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase)
               lowercase__     =  self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase)
               check_equivalence(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)

               lowercase__     =  self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase			,				return_labels=lowerCAmelCase)
               lowercase__     =  self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase			,				return_labels=lowerCAmelCase)
               check_equivalence(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)

               lowercase__     =  self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase)
               lowercase__     =  self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase)
               check_equivalence(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				{'output_hidden_states': True})

               lowercase__     =  self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase			,				return_labels=lowerCAmelCase)
               lowercase__     =  self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase			,				return_labels=lowerCAmelCase)
               check_equivalence(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				{'output_hidden_states': True})



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

   '''simple docstring'''




   A    :  Tuple       				=    (MaskFormerSwinBackbone,) if is_torch_available() else ()
   A    :  int       				=    MaskFormerSwinConfig


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Dict:




         """simple docstring"""

         lowercase__     =  MaskFormerSwinModelTester(self)



   def 			UpperCAmelCase		(						self  :			List[str])	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__     =  inputs_dict['''pixel_values'''].shape[0]

         for backbone_class in self.all_model_classes:
               lowercase__     =  backbone_class(lowerCAmelCase)
               backbone.to(lowerCAmelCase)
               backbone.eval()

               lowercase__     =  backbone(**lowerCAmelCase)

               # Test default outputs and verify feature maps
               self.assertIsInstance(outputs.feature_maps			,				lowerCAmelCase)
               self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
               for feature_map, n_channels in zip(outputs.feature_maps			,				backbone.channels):
                     self.assertTrue(feature_map.shape[:2]			,				(batch_size, n_channels))
               self.assertIsNone(outputs.hidden_states)
               self.assertIsNone(outputs.attentions)

               # Test output_hidden_states=True
               lowercase__     =  backbone(**lowerCAmelCase			,				output_hidden_states=lowerCAmelCase)
               self.assertIsNotNone(outputs.hidden_states)
               self.assertTrue(len(outputs.hidden_states)			,				len(backbone.stage_names))
               # We skip the stem layer
               for hidden_states, n_channels in zip(outputs.hidden_states[1:]			,				backbone.channels):
                     for hidden_state in hidden_states:
                           # Hidden states are in the format (batch_size, (height * width), n_channels)
                           lowercase__     =  hidden_state.shape
                           self.assertTrue((h_batch_size, h_n_channels)			,				(batch_size, n_channels))

                             # Test output_attentions=True
               if self.has_attentions:
                     lowercase__     =  backbone(**lowerCAmelCase			,				output_attentions=lowerCAmelCase)
                     self.assertIsNotNone(outputs.attentions)





import heapq
import sys

import numpy as np

a__      :				Dict   	=			tuple[int, int]



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			List[str])	->				Any:




         """simple docstring"""

         lowercase__     =  []
         lowercase__     =  set()


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




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			int)	->				str:




         """simple docstring"""

         return len(self.elements) == 0


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			List[str])	->				List[str]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			int)	->				Tuple:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Dict)	->				List[Any]:




         """simple docstring"""

         return self.elements[0][1]



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




         """simple docstring"""

         ((lowercase__),	(lowercase__))     =  heapq.heappop(self.elements)
         self.set.remove(lowerCAmelCase)
         return (priority, item)

def   _lowerCAmelCase   (						A__						,  A__	):
      # euclidean distance
      lowercase__     =  np.array(A__	)
      lowercase__     =  np.array(A__	)
      return np.linalg.norm(a - b	)

def   _lowerCAmelCase   (						A__						,  A__	):
      # integer division by time variable
      return consistent_heuristic(A__						,  A__	) // t

def   _lowerCAmelCase   (						A__						,  A__	):
      # manhattan distance
      return abs(p[0] - goal[0]	) + abs(p[1] - goal[1]	)

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__	):
      lowercase__     =  g_function[start] + Wa * heuristics[i](A__						,  A__	)
      return ans

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

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

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

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

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

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

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

def   _lowerCAmelCase   (						):
      lowercase__     =  []
      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__      :				str   	=			{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a__      :				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),
]
a__      :				Any   	=			make_common_ground()


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

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

a__      :				Optional[Any]   	=			1

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

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

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


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