



'''simple docstring'''

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_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


if is_torch_available():
	import torch

	from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin


class 	a__     :


	def __init__(self : Tuple,					__UpperCAmelCase : Dict,					__UpperCAmelCase : Optional[int]=None,					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : str="resnet50",					__UpperCAmelCase : Union[str, Any]=3,					__UpperCAmelCase : Optional[Any]=32,					__UpperCAmelCase : Dict=3,					__UpperCAmelCase : Union[str, Any]=True,					__UpperCAmelCase : Union[str, Any]=True,					)     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= parent
					SCREAMING_SNAKE_CASE  :						Any      					= out_indices if out_indices is not None else [4]
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= stage_names
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= out_features
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= backbone
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= batch_size
					SCREAMING_SNAKE_CASE  :						Any      					= image_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= num_channels
					SCREAMING_SNAKE_CASE  :						Dict      					= use_pretrained_backbone
					SCREAMING_SNAKE_CASE  :						Any      					= is_training


	def 				lowercase__  (self : int      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_config()

					return config, pixel_values


	def 				lowercase__  (self : Any      )     ->			int:

					"""simple docstring"""

					return TimmBackboneConfig(
					    image_size=self.image_size,					num_channels=self.num_channels,					out_features=self.out_features,					out_indices=self.out_indices,					stage_names=self.stage_names,					use_pretrained_backbone=self.use_pretrained_backbone,					backbone=self.backbone,					)


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : Dict,					__UpperCAmelCase : List[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= TimmBackbone(config=__UpperCamelCase      )
					model.to(__UpperCamelCase      )
					model.eval()
					with torch.no_grad():
									SCREAMING_SNAKE_CASE  :						int      					= model(__UpperCamelCase      )
					self.parent.assertEqual(
					    result.feature_map[-1].shape,					(self.batch_size, model.channels[-1], 14, 14),					)






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.prepare_config_and_inputs()
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[Any]      					= config_and_inputs
					SCREAMING_SNAKE_CASE  :						Dict      					= {'''pixel_values''': pixel_values}
					return config, inputs_dict



@require_torch
@require_timm
class 	a__     (				_lowercase,			_lowercase,			_lowercase,			unittest.TestCase	):
	__magic_name__		:    List[str]	    =     (TimmBackbone,) if is_torch_available() else ()
	__magic_name__		:    int	    =     {"feature-extraction": TimmBackbone} if is_torch_available() else {}
	__magic_name__		:    str	    =     False
	__magic_name__		:    int	    =     False
	__magic_name__		:    Optional[Any]	    =     False
	__magic_name__		:    str	    =     False


	def 				lowercase__  (self : str      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= TimmBackboneModelTester(self      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= ConfigTester(self,					config_class=__UpperCamelCase,					has_text_modality=__UpperCamelCase      )


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

					"""simple docstring"""

					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 				lowercase__  (self : Any      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''resnet18'''
					SCREAMING_SNAKE_CASE  :						Any      					= '''microsoft/resnet-18'''

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= AutoBackbone.from_pretrained(__UpperCamelCase,					use_timm_backbone=__UpperCamelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= AutoBackbone.from_pretrained(__UpperCamelCase      )

					self.assertEqual(len(timm_model.out_features      ),					len(transformers_model.out_features      )      )
					self.assertEqual(len(timm_model.stage_names      ),					len(transformers_model.stage_names      )      )
					self.assertEqual(timm_model.channels,					transformers_model.channels      )
					# Out indices are set to the last layer by default. For timm models, we don't know
					# the number of layers in advance, so we set it to (-1,), whereas for transformers
					# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
					self.assertEqual(timm_model.out_indices,					(-1,)      )
					self.assertEqual(transformers_model.out_indices,					[len(timm_model.stage_names      ) - 1]      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= AutoBackbone.from_pretrained(__UpperCamelCase,					use_timm_backbone=__UpperCamelCase,					out_indices=[1, 2, 3]      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= AutoBackbone.from_pretrained(__UpperCamelCase,					out_indices=[1, 2, 3]      )

					self.assertEqual(timm_model.out_indices,					transformers_model.out_indices      )
					self.assertEqual(len(timm_model.out_features      ),					len(transformers_model.out_features      )      )
					self.assertEqual(timm_model.channels,					transformers_model.channels      )


	@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''      )
	def 				lowercase__  (self : Optional[int]      )     ->			Any:

					"""simple docstring"""

					pass


	@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''      )
	def 				lowercase__  (self : List[Any]      )     ->			Any:

					"""simple docstring"""

					pass


	@unittest.skip('''TimmBackbone initialization is managed on the timm side'''      )
	def 				lowercase__  (self : Dict      )     ->			Any:

					"""simple docstring"""

					pass


	@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''      )
	def 				lowercase__  (self : Union[str, Any]      )     ->			str:

					"""simple docstring"""

					pass


	@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''      )
	def 				lowercase__  (self : Optional[Any]      )     ->			Dict:

					"""simple docstring"""

					pass


	@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''      )
	def 				lowercase__  (self : Any      )     ->			List[Any]:

					"""simple docstring"""

					pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''      )
	def 				lowercase__  (self : Any      )     ->			str:

					"""simple docstring"""

					pass


	@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''      )
	def 				lowercase__  (self : Optional[int]      )     ->			Optional[Any]:

					"""simple docstring"""

					pass


	@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''      )
	def 				lowercase__  (self : List[Any]      )     ->			Any:

					"""simple docstring"""

					pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''      )
	def 				lowercase__  (self : str      )     ->			Optional[int]:

					"""simple docstring"""

					pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''      )
	def 				lowercase__  (self : List[Any]      )     ->			Dict:

					"""simple docstring"""

					pass


	@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''      )
	def 				lowercase__  (self : Optional[int]      )     ->			str:

					"""simple docstring"""

					pass


	@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''      )
	def 				lowercase__  (self : List[Any]      )     ->			List[str]:

					"""simple docstring"""

					pass


	@unittest.skip('''Safetensors is not supported by timm.'''      )
	def 				lowercase__  (self : List[Any]      )     ->			Tuple:

					"""simple docstring"""

					pass


	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
	def 				lowercase__  (self : Optional[int]      )     ->			Union[str, Any]:

					"""simple docstring"""

					pass


	def 				lowercase__  (self : str      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						Any      					= model_class(__UpperCamelCase      )
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= inspect.signature(model.forward      )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE  :						Tuple      					= ['''pixel_values''']
									self.assertListEqual(arg_names[:1],					__UpperCamelCase      )


	def 				lowercase__  (self : List[Any]      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= self.model_tester.prepare_config_and_inputs_for_common()
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= True
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.has_attentions

					# no need to test all models as different heads yield the same functionality
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.all_model_classes[0]
					SCREAMING_SNAKE_CASE  :						int      					= model_class(__UpperCamelCase      )
					model.to(__UpperCamelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self._prepare_for_class(__UpperCamelCase,					__UpperCamelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= model(**__UpperCamelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= outputs[0][-1]

					# Encoder-/Decoder-only models
					SCREAMING_SNAKE_CASE  :						str      					= outputs.hidden_states[0]
					hidden_states.retain_grad()

					if self.has_attentions:
									SCREAMING_SNAKE_CASE  :						str      					= outputs.attentions[0]
									attentions.retain_grad()

					output.flatten()[0].backward(retain_graph=__UpperCamelCase      )

					self.assertIsNotNone(hidden_states.grad      )

					if self.has_attentions:
									self.assertIsNotNone(attentions.grad      )






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						str      					= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= model_class(__UpperCamelCase      )
									model.to(__UpperCamelCase      )
									model.eval()
									SCREAMING_SNAKE_CASE  :						int      					= model(**__UpperCamelCase      )

									self.assertEqual(len(result.feature_maps      ),					len(config.out_indices      )      )
									self.assertEqual(len(model.channels      ),					len(config.out_indices      )      )

									# Check output of last stage is taken if out_features=None, out_indices=None
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= copy.deepcopy(__UpperCamelCase      )
									SCREAMING_SNAKE_CASE  :						List[Any]      					= None
									SCREAMING_SNAKE_CASE  :						Dict      					= model_class(__UpperCamelCase      )
									model.to(__UpperCamelCase      )
									model.eval()
									SCREAMING_SNAKE_CASE  :						int      					= model(**__UpperCamelCase      )

									self.assertEqual(len(result.feature_maps      ),					1      )
									self.assertEqual(len(model.channels      ),					1      )

									# Check backbone can be initialized with fresh weights
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= copy.deepcopy(__UpperCamelCase      )
									SCREAMING_SNAKE_CASE  :						Dict      					= False
									SCREAMING_SNAKE_CASE  :						List[Any]      					= model_class(__UpperCamelCase      )
									model.to(__UpperCamelCase      )
									model.eval()
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= model(**__UpperCamelCase      )









'''simple docstring'''

import heapq
import sys

import numpy as np

snake_case_			=     tuple[int, int]


class 	a__     :


	def __init__(self : int      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= []
					SCREAMING_SNAKE_CASE  :						Tuple      					= set()


	def 				lowercase__  (self : Any      )     ->			Dict:

					"""simple docstring"""

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


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

					"""simple docstring"""

					return len(self.elements      ) == 0


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : int,					__UpperCAmelCase : List[Any]      )     ->			Tuple:

					"""simple docstring"""

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


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

					"""simple docstring"""

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


	def 				lowercase__  (self : Tuple      )     ->			Dict:

					"""simple docstring"""

					return self.elements[0][1]






	def 				lowercase__  (self : Optional[Any]      )     ->			Tuple:

					"""simple docstring"""

					((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Optional[int]      					= heapq.heappop(self.elements      )
					self.set.remove(__UpperCAmelCase      )
					return (priority, item)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :TPos       ):
				# euclidean distance
				SCREAMING_SNAKE_CASE  :						str      					= np.array(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= np.array(_SCREAMING_SNAKE_CASE       )
				return np.linalg.norm(a - b       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :TPos       ):
				# integer division by time variable
				return consistent_heuristic(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ) // t






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :TPos       ):
				# manhattan distance
				return abs(p[0] - goal[0]       ) + abs(p[1] - goal[1]       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :dict[TPos, float]       ):
				SCREAMING_SNAKE_CASE  :						List[str]      					= g_function[start] + Wa * heuristics[i](_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				return ans






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Tuple       ):
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= np.chararray((n, n)       )
				for i in range(_SCREAMING_SNAKE_CASE       ):
								for j in range(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						Tuple      					= '''*'''

				for i in range(_SCREAMING_SNAKE_CASE       ):
								for j in range(_SCREAMING_SNAKE_CASE       ):
												if (j, (n - 1) - i) in blocks:
																SCREAMING_SNAKE_CASE  :						Tuple      					= '''#'''

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

				for i in range(_SCREAMING_SNAKE_CASE       ):
								for j in range(_SCREAMING_SNAKE_CASE       ):
												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:-'''       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= back_pointer[goal]
				while x != start:
								print(_SCREAMING_SNAKE_CASE    ,	end=''' '''       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= back_pointer[x]
				print(_SCREAMING_SNAKE_CASE       )
				sys.exit()






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos       ):
				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       __lowercase  (_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Optional[int]    ,	):
				for itera in range(_SCREAMING_SNAKE_CASE       ):
								open_list[itera].remove_element(_SCREAMING_SNAKE_CASE       )
				# print("s", s)
				# print("j", j)
				((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Tuple      					= s
				SCREAMING_SNAKE_CASE  :						List[Any]      					= (x - 1, y)
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= (x + 1, y)
				SCREAMING_SNAKE_CASE  :						List[Any]      					= (x, y + 1)
				SCREAMING_SNAKE_CASE  :						Any      					= (x, y - 1)

				for neighbours in [left, right, up, down]:
								if neighbours not in blocks:
												if valid(_SCREAMING_SNAKE_CASE       ) and neighbours not in visited:
																# print("neighbour", neighbours)
																visited.add(_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= -1
																SCREAMING_SNAKE_CASE  :						List[str]      					= float('''inf'''       )

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






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
				for x in range(1    ,	5       ):
								for y in range(1    ,	6       ):
												some_list.append((x, y)       )

				for x in range(15    ,	20       ):
								some_list.append((x, 17)       )

				for x in range(10    ,	19       ):
								for y in range(1    ,	15       ):
												some_list.append((x, y)       )

    # L block
				for x in range(1    ,	4       ):
								for y in range(12    ,	19       ):
												some_list.append((x, y)       )
				for x in range(3    ,	13       ):
								for y in range(16    ,	19       ):
												some_list.append((x, y)       )
				return some_list


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

snake_case_			=     [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
snake_case_			=     make_common_ground()


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

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

snake_case_			=     1






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :int       ):
				SCREAMING_SNAKE_CASE  :						Any      					= {start: 0, goal: float('''inf'''       )}
				SCREAMING_SNAKE_CASE  :						Tuple      					= {start: -1, goal: -1}
				SCREAMING_SNAKE_CASE  :						Tuple      					= []
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= set()

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

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




