





import heapq
import sys

import numpy as np

_a 						=      tuple[int, int]






class 					__A    :

			'''simple docstring'''







			def __init__(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		[]
				lowerCamelCase__								=		set()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return len(self.elements							) == 0





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self.elements[0][1]





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				(lowerCamelCase__)								=		heapq.heappop(self.elements							)
				self.set.remove(__lowerCamelCase							)
				return (priority, item)





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      int:







	'''simple docstring'''







	lowerCamelCase__								=		np.array(_A							)
	lowerCamelCase__								=		np.array(_A							)
	return np.linalg.norm(a - b							)





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Tuple:







	'''simple docstring'''







	return consistent_heuristic(_A       ,_A							) // t





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Any:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case							)				->      Dict:







	'''simple docstring'''







	lowerCamelCase__								=		g_function[start] + Wa * heuristics[i](_A       ,_A							)
	return ans





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case							)				->      Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__								=		np.chararray((n, n)							)
	for i in range(_A							):
		for j in range(_A							):
			lowerCamelCase__								=		"*"

	for i in range(_A							):
		for j in range(_A							):
			if (j, (n - 1) - i) in blocks:
				lowerCamelCase__								=		"#"

	lowerCamelCase__								=		"-"
	lowerCamelCase__								=		back_pointer[goal]
	while x != start:
		(lowerCamelCase__)								=		x
		# print(x)
		lowerCamelCase__								=		"-"
		lowerCamelCase__								=		back_pointer[x]
	lowerCamelCase__								=		"-"

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





def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''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 lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,)				->      Optional[int]:







	'''simple docstring'''







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

			if valid(_A							) and g_function[neighbours] > g_function[s] + 1:
				lowerCamelCase__								=		g_function[s] + 1
				lowerCamelCase__								=		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__()				->      int:







	'''simple docstring'''







	lowerCamelCase__								=		[]
	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 lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case							)				->      Optional[int]:







	'''simple docstring'''







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

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

	lowerCamelCase__								=		[]
	lowerCamelCase__								=		[]
	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:
					lowerCamelCase__								=		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:
					lowerCamelCase__								=		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)







import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
A      :    Union[str, Any]		=					{"LayoutLMv2Config", "LayoutLMv3Config"}







@is_pipeline_test
class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       A__											=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       A__											=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              A__											=			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              A__											=			{
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }





       @require_torch
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="pt"       )

             lowerCamelCase__		:				Dict    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"					,      top_k=2       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]       )

             lowerCamelCase__		:				Optional[int]    =  text_classifier(["This is great !", "This is bad"]					,      top_k=2       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                 ]					,      )

             lowerCamelCase__		:				Any    =  text_classifier("This is great !"					,      top_k=1       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )

             # Legacy behavior
             lowerCamelCase__		:				Dict    =  text_classifier("This is great !"					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )

             lowerCamelCase__		:				str    =  text_classifier("This is great !"					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]]       )

             lowerCamelCase__		:				Optional[Any]    =  text_classifier(["This is great !", "Something else"]					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                     [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                 ]					,      )

             lowerCamelCase__		:				Any    =  text_classifier(["This is great !", "Something else"]					,      return_all_scores=__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [
                     {"label": "LABEL_0", "score": 0.5_0_4},
                     {"label": "LABEL_0", "score": 0.5_0_4},
                 ]					,      )





       @require_torch
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             import torch

             lowerCamelCase__		:				int    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="pt"					,      device=torch.device("cpu"       )					,      )

             lowerCamelCase__		:				Any    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )





       @require_tf
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  pipeline(
                 task="text-classification"					,      model="hf-internal-testing/tiny-random-distilbert"					,      framework="tf"       )

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "LABEL_0", "score": 0.5_0_4}]       )





       @slow
       @require_torch
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  pipeline("text-classification"       )

             lowerCamelCase__		:				List[str]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Optional[int]    =  text_classifier("This is bad !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "NEGATIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Tuple    =  text_classifier("Birds are a type of animal"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 0.9_8_8}]       )





       @slow
       @require_tf
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  pipeline("text-classification"					,      framework="tf"       )

             lowerCamelCase__		:				Optional[int]    =  text_classifier("This is great !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Optional[Any]    =  text_classifier("This is bad !"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "NEGATIVE", "score": 1.0}]       )
             lowerCamelCase__		:				Dict    =  text_classifier("Birds are a type of animal"       )
             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": "POSITIVE", "score": 0.9_8_8}]       )





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  TextClassificationPipeline(model=__lowerCamelCase					,      tokenizer=__lowerCamelCase       )
             return text_classifier, ["HuggingFace is in", "This is another test"]





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  text_classifier.model
             # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
             lowerCamelCase__		:				List[Any]    =  "HuggingFace is in"
             lowerCamelCase__		:				Tuple    =  text_classifier(__lowerCamelCase       )

             self.assertEqual(nested_simplify(__lowerCamelCase       )					,      [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}]       )
             self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )

             lowerCamelCase__		:				Optional[int]    =  ["HuggingFace is in ", "Paris is in France"]
             lowerCamelCase__		:				Dict    =  text_classifier(__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}, {"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}]					,      )
             self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )
             self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()       )

             # Forcing to get all results with `top_k=None`
             # This is NOT the legacy format
             lowerCamelCase__		:				List[Any]    =  text_classifier(__lowerCamelCase					,      top_k=__lowerCamelCase       )
             lowerCamelCase__		:				Union[str, Any]    =  len(model.config.idalabel.values()       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [[{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}] * N, [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}] * N]					,      )

             lowerCamelCase__		:				Optional[int]    =  {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
             lowerCamelCase__		:				List[Any]    =  text_classifier(__lowerCamelCase       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      {"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}					,      )
             self.assertTrue(outputs["label"] in model.config.idalabel.values()       )

             # This might be used a text pair, but tokenizer + pipe interaction
             # makes it hard to understand that it's not using the pair properly
             # https://github.com/huggingface/transformers/issues/17305
             # We disabled this usage instead as it was outputting wrong outputs.
             lowerCamelCase__		:				Any    =  [["HuggingFace is in ", "Paris is in France"]]
             with self.assertRaises(__lowerCamelCase       ):
                   text_classifier(__lowerCamelCase       )

             # This used to be valid for doing text pairs
             # We're keeping it working because of backward compatibility
             lowerCamelCase__		:				int    =  text_classifier([[["HuggingFace is in ", "Paris is in France"]]]       )
             self.assertEqual(
                 nested_simplify(__lowerCamelCase       )					,      [{"label": ANY(__lowerCamelCase       ), "score": ANY(__lowerCamelCase       )}]					,      )
             self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )


