import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:
					class     lowerCAmelCase_     :







												@staticmethod
												def  UpperCamelCase_						(   *_A      :     Optional[int] ,			**_A      :     Optional[Any]		):
																	pass
@is_pipeline_test
@require_vision
@require_torch
class     lowerCAmelCase_     (       unittest.TestCase ):
							UpperCAmelCase				   =     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







							def  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     List[Any] ,			_A      :     Optional[Any] ,			_A      :     List[str]		):
												_UpperCamelCase			     =     pipeline(
												    '''zero-shot-object-detection''' ,			model='''hf-internal-testing/tiny-random-owlvit-object-detection'''		)

												_UpperCamelCase			     =     [
												    {
												        '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
												        '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												    }
												]
												return object_detector, examples







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     Tuple ,			_A      :     str		):
												_UpperCamelCase			     =     object_detector(examples[0] ,			threshold=0.0		)

												_UpperCamelCase			     =     len(_A		)
												self.assertGreater(_A ,			0		)
												self.assertEqual(
												    _A ,			[
												        {
												            '''score''': ANY(_A		),
												            '''label''': ANY(_A		),
												            '''box''': {'''xmin''': ANY(_A		), '''ymin''': ANY(_A		), '''xmax''': ANY(_A		), '''ymax''': ANY(_A		)},
												        }
												        for i in range(_A		)
												    ] ,			)







							@require_tf
							@unittest.skip('''Zero Shot Object Detection not implemented in TF'''		)
							def  UpperCamelCase_						(   self      :     List[Any]		):
												pass







							@require_torch
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_UpperCamelCase			     =     pipeline(
												    '''zero-shot-object-detection''' ,			model='''hf-internal-testing/tiny-random-owlvit-object-detection'''		)

												_UpperCamelCase			     =     object_detector(
												    '''./tests/fixtures/tests_samples/COCO/000000039769.png''' ,			candidate_labels=['''cat''', '''remote''', '''couch'''] ,			threshold=0.64 ,			)

												self.assertEqual(
												    nested_simplify(_A ,			decimals=4		) ,			[
												        {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												        {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												        {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												        {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												        {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												        {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												        {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												        {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
												        {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												    ] ,			)

												_UpperCamelCase			     =     object_detector(
												    [
												        {
												            '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
												            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												        }
												    ] ,			threshold=0.64 ,			)

												self.assertEqual(
												    nested_simplify(_A ,			decimals=4		) ,			[
												        [
												            {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												            {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												            {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												            {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												            {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												            {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												            {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												            {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
												            {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												        ]
												    ] ,			)







							@require_torch
							@slow
							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     pipeline('''zero-shot-object-detection'''		)

												_UpperCamelCase			     =     object_detector(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg''' ,			candidate_labels=['''cat''', '''remote''', '''couch'''] ,			)
												self.assertEqual(
												    nested_simplify(_A ,			decimals=4		) ,			[
												        {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												        {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												        {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												        {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
												        {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
												    ] ,			)

												_UpperCamelCase			     =     object_detector(
												    [
												        {
												            '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
												            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												        },
												        {
												            '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
												            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												        },
												    ] ,			)
												self.assertEqual(
												    nested_simplify(_A ,			decimals=4		) ,			[
												        [
												            {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												            {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												            {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												            {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
												            {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
												        ],
												        [
												            {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												            {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												            {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												            {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
												            {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
												        ],
												    ] ,			)







							@require_tf
							@unittest.skip('''Zero Shot Object Detection not implemented in TF'''		)
							def  UpperCamelCase_						(   self      :     List[Any]		):
												pass







							@require_torch
							@slow
							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     0.2
												_UpperCamelCase			     =     pipeline('''zero-shot-object-detection'''		)

												_UpperCamelCase			     =     object_detector(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg''' ,			candidate_labels=['''cat''', '''remote''', '''couch'''] ,			threshold=_A ,			)
												self.assertEqual(
												    nested_simplify(_A ,			decimals=4		) ,			[
												        {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												        {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												        {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												    ] ,			)







							@require_torch
							@slow
							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     2
												_UpperCamelCase			     =     pipeline('''zero-shot-object-detection'''		)

												_UpperCamelCase			     =     object_detector(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg''' ,			candidate_labels=['''cat''', '''remote''', '''couch'''] ,			top_k=_A ,			)
												self.assertEqual(
												    nested_simplify(_A ,			decimals=4		) ,			[
												        {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												        {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												    ] ,			)


from __future__ import annotations

_A  =	[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def   lowerCamelCase__							(							__lowerCAmelCase    :    list[list[int]]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    list[list[int]]       ,				):




			"""simple docstring"""


			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the reference grid
			lowerCAmelCase_         =      1
			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the action grid

			lowerCAmelCase_         =      init[0]
			lowerCAmelCase_         =      init[1]
			lowerCAmelCase_         =      0
			lowerCAmelCase_         =      g + heuristic[x][y]  # cost from starting cell to destination cell
			lowerCAmelCase_         =      [[f, g, x, y]]

			lowerCAmelCase_         =      False  # flag that is set when search is complete
			lowerCAmelCase_         =      False  # flag set if we can't find expand

			while not found and not resign:
						if len(__lowerCAmelCase							) == 0:
									raise ValueError("Algorithm is unable to find solution"							)
						else:  # to choose the least costliest action so as to move closer to the goal
									cell.sort()
									cell.reverse()
									lowerCAmelCase_         =      cell.pop()
									lowerCAmelCase_         =      next_cell[2]
									lowerCAmelCase_         =      next_cell[3]
									lowerCAmelCase_         =      next_cell[1]

									if x == goal[0] and y == goal[1]:
												lowerCAmelCase_         =      True
									else:
												for i in range(len(__lowerCAmelCase							)							):  # to try out different valid actions
															lowerCAmelCase_         =      x + DIRECTIONS[i][0]
															lowerCAmelCase_         =      y + DIRECTIONS[i][1]
															if xa >= 0 and xa < len(__lowerCAmelCase							) and ya >= 0 and ya < len(grid[0]							):
																		if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																					lowerCAmelCase_         =      g + cost
																					lowerCAmelCase_         =      ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya]							)
																					lowerCAmelCase_         =      1
																					lowerCAmelCase_         =      i
			lowerCAmelCase_         =      []
			lowerCAmelCase_         =      goal[0]
			lowerCAmelCase_         =      goal[1]
			invpath.append([x, y]							)  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						lowerCAmelCase_         =      x - DIRECTIONS[action[x][y]][0]
						lowerCAmelCase_         =      y - DIRECTIONS[action[x][y]][1]
						lowerCAmelCase_         =      xa
						lowerCAmelCase_         =      ya
						invpath.append([x, y]							)

			lowerCAmelCase_         =      []
			for i in range(len(__lowerCAmelCase							)							):
						path.append(invpath[len(__lowerCAmelCase							) - 1 - i]							)
			return path, action


if __name__ == "__main__":
			_A  =	[
			    [0, 1, 0, 0, 0, 0],
			    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
			    [0, 1, 0, 0, 0, 0],
			    [0, 1, 0, 0, 1, 0],
			    [0, 0, 0, 0, 1, 0],
			]

			_A  =	[0, 0]
			# all coordinates are given in format [y,x]
			_A  =	[len(grid) - 1, len(grid[0]) - 1]
			_A  =	1

			# the cost map which pushes the path closer to the goal
			_A  =	[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
			for i in range(len(grid)):
						for j in range(len(grid[0])):
									_A  =	abs(i - goal[0]) + abs(j - goal[1])
									if grid[i][j] == 1:
												# added extra penalty in the heuristic map
												_A  =	99

			_A, _A  =	search(grid, init, goal, cost, heuristic)

			print("ACTION MAP")
			for i in range(len(action)):
						print(action[i])

			for i in range(len(path)):
						print(path[i])



