

import heapq
import sys

import numpy as np

a__												=						tuple[int, int]

class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			)       ->       Dict:
												_a      :       str   =						[]
												_a      :       int   =						set()


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												if not self.empty():
																	return self.elements[0][0]
												else:
																	return float('''inf'''			)


							def 							__lowercase							(     self			)       ->       Tuple:
												return len(self.elements			) == 0


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


							def 							__lowercase							(     self			,				_a			)       ->       List[Any]:
												if item in self.set:
																	self.set.remove(_a			)
																	_a      :       Optional[Any]   =						[]
																	((_a) ,   (_a))      :       Union[str, Any]   =						heapq.heappop(self.elements			)
																	while x != item:
																						temp.append((pro, x)			)
																						((_a) ,   (_a))      :       Optional[int]   =						heapq.heappop(self.elements			)
																	for prito, yyy in temp:
																						heapq.heappush(self.elements			,				(prito, yyy)			)


							def 							__lowercase							(     self			)       ->       Tuple:
												return self.elements[0][1]




							def 							__lowercase							(     self			)       ->       int:
												((_a) ,   (_a))      :       List[Any]   =						heapq.heappop(self.elements			)
												self.set.remove(_a			)
												return (priority, item)



def   __UpperCAmelCase					(      __a :     TPos						,__a :     TPos      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       int   =						np.array(__a      )
					_a      :       int   =						np.array(__a      )
					return np.linalg.norm(a - b      )



def   __UpperCAmelCase					(      __a :     TPos						,__a :     TPos      )     ->	List[Any]:

					"""simple docstring"""

					return consistent_heuristic(__a						,__a      ) // t



def   __UpperCAmelCase					(      __a :     TPos						,__a :     TPos      )     ->	str:

					"""simple docstring"""

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



def   __UpperCAmelCase					(      __a :     TPos						,__a :     int						,__a :     TPos						,__a :     dict[TPos, float]      )     ->	Optional[int]:

					"""simple docstring"""

					_a      :       Optional[int]   =						g_function[start] + Wa * heuristics[i](__a						,__a      )
					return ans



def   __UpperCAmelCase					(      __a :     Any						,__a :     Any						,__a :     Dict      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						np.chararray((n, n)      )
					for i in range(__a      ):
										for j in range(__a      ):
															_a      :       Any   =						'''*'''

					for i in range(__a      ):
										for j in range(__a      ):
															if (j, (n - 1) - i) in blocks:
																				_a      :       Dict   =						'''#'''

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

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



def   __UpperCAmelCase					(      __a :     TPos      )     ->	List[str]:

					"""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   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     List[str]						,__a :     List[str]						,__a :     Any						,__a :     Dict						,__a :     Optional[Any]						,__a :     List[Any]						,__a :     Any						,)     ->	Optional[Any]:

					"""simple docstring"""

					for itera in range(__a      ):
										open_list[itera].remove_element(__a      )
					# print("s", s)
					# print("j", j)
					((_a) ,   (_a))      :       Optional[Any]   =						s
					_a      :       Any   =						(x - 1, y)
					_a      :       Dict   =						(x + 1, y)
					_a      :       str   =						(x, y + 1)
					_a      :       str   =						(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      )
																				_a      :       Optional[int]   =						-1
																				_a      :       Union[str, Any]   =						float('''inf'''      )

															if valid(__a      ) and g_function[neighbours] > g_function[s] + 1:
																				_a      :       Dict   =						g_function[s] + 1
																				_a      :       Optional[Any]   =						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   __UpperCAmelCase					(      )     ->	List[str]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						[]
					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   __UpperCAmelCase					(      __a :     TPos						,__a :     TPos						,__a :     int      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       Optional[int]   =						{start: 0, goal: float('''inf'''      )}
					_a      :       int   =						{start: -1, goal: -1}
					_a      :       Union[str, Any]   =						[]
					_a      :       Optional[int]   =						set()

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

					_a      :       list[int]   =						[]
					_a      :       list[int]   =						[]
					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:
																									_a ,   _a      :       Any   =						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:
																									_a      :       Tuple   =						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)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Tuple									= ['''RoFormerTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Any									= [
						    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RoFormerForCausalLM''',
						    '''RoFormerForMaskedLM''',
						    '''RoFormerForMultipleChoice''',
						    '''RoFormerForQuestionAnswering''',
						    '''RoFormerForSequenceClassification''',
						    '''RoFormerForTokenClassification''',
						    '''RoFormerLayer''',
						    '''RoFormerModel''',
						    '''RoFormerPreTrainedModel''',
						    '''load_tf_weights_in_roformer''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Dict									= [
						    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFRoFormerForCausalLM''',
						    '''TFRoFormerForMaskedLM''',
						    '''TFRoFormerForMultipleChoice''',
						    '''TFRoFormerForQuestionAnswering''',
						    '''TFRoFormerForSequenceClassification''',
						    '''TFRoFormerForTokenClassification''',
						    '''TFRoFormerLayer''',
						    '''TFRoFormerModel''',
						    '''TFRoFormerPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     str									= [
						    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaxRoFormerForMaskedLM''',
						    '''FlaxRoFormerForMultipleChoice''',
						    '''FlaxRoFormerForQuestionAnswering''',
						    '''FlaxRoFormerForSequenceClassification''',
						    '''FlaxRoFormerForTokenClassification''',
						    '''FlaxRoFormerModel''',
						    '''FlaxRoFormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
						from .tokenization_roformer import RoFormerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roformer_fast import RoFormerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roformer import (
												    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoFormerForCausalLM,
												    RoFormerForMaskedLM,
												    RoFormerForMultipleChoice,
												    RoFormerForQuestionAnswering,
												    RoFormerForSequenceClassification,
												    RoFormerForTokenClassification,
												    RoFormerLayer,
												    RoFormerModel,
												    RoFormerPreTrainedModel,
												    load_tf_weights_in_roformer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roformer import (
												    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRoFormerForCausalLM,
												    TFRoFormerForMaskedLM,
												    TFRoFormerForMultipleChoice,
												    TFRoFormerForQuestionAnswering,
												    TFRoFormerForSequenceClassification,
												    TFRoFormerForTokenClassification,
												    TFRoFormerLayer,
												    TFRoFormerModel,
												    TFRoFormerPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roformer import (
												    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxRoFormerForMaskedLM,
												    FlaxRoFormerForMultipleChoice,
												    FlaxRoFormerForQuestionAnswering,
												    FlaxRoFormerForSequenceClassification,
												    FlaxRoFormerForTokenClassification,
												    FlaxRoFormerModel,
												    FlaxRoFormerPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE:     Any									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)