



import heapq
import sys

import numpy as np

__snake_case		:List[Any]   	=  tuple[int, int]

class 					_A :



       def __init__(	self							:  str):


           '''simple docstring'''

           __a		 =       []
           __a		 =       set()



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           return len(self.elements) == 0



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  int):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           return self.elements[0][1]





       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           ((__a) ,     (__a))		 =       heapq.heappop(self.elements)
           self.set.remove(__SCREAMING_SNAKE_CASE)
           return (priority, item)





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):

    '''simple docstring'''





    __a		 =       np.array(_UpperCAmelCase       )
    __a		 =       np.array(_UpperCAmelCase       )
    return np.linalg.norm(a - b       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):

    '''simple docstring'''





    return consistent_heuristic(_UpperCAmelCase     ,				_UpperCAmelCase       ) // t





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):

    '''simple docstring'''





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





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):

    '''simple docstring'''





    __a		 =       g_function[start] + Wa * heuristics[i](_UpperCAmelCase     ,				_UpperCAmelCase       )
    return ans





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):

    '''simple docstring'''





    __a		 =       np.chararray((n, n)       )
    for i in range(_UpperCAmelCase       ):
        for j in range(_UpperCAmelCase       ):
            __a		 =       '''*'''

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

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

    for i in range(_UpperCAmelCase       ):
        for j in range(_UpperCAmelCase       ):
            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		 =       back_pointer[goal]
    while x != start:
        print(_UpperCAmelCase     ,				end=''' '''       )
        __a		 =       back_pointer[x]
    print(_UpperCAmelCase       )
    sys.exit()





def   __snake_case			(    _UpperCAmelCase       ):

    '''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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				):

    '''simple docstring'''





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

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

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





def   __snake_case			(    ):

    '''simple docstring'''





    __a		 =       []
    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		:List[Any]   	=  {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

__snake_case		:str   	=  [
    (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		:Union[str, Any]   	=  make_common_ground()


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

# start and end destination
__snake_case		:Union[str, Any]   	=  (0, 0)
__snake_case		:Tuple   	=  (n - 1, n - 1)

__snake_case		:Optional[int]   	=  1





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):

    '''simple docstring'''





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

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

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

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor

class 					_A (			unittest.TestCase					):



       def __init__(	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  int=7  ,	__SCREAMING_SNAKE_CASE							:  Tuple=3  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=18  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=30  ,	__SCREAMING_SNAKE_CASE							:  int=400  ,	__SCREAMING_SNAKE_CASE							:  Dict=True  ,	__SCREAMING_SNAKE_CASE							:  str=None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=True  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=None  ,	__SCREAMING_SNAKE_CASE							:  str=True  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=[0.5, 0.5, 0.5]  ,	__SCREAMING_SNAKE_CASE							:  Any=[0.5, 0.5, 0.5]  ,	__SCREAMING_SNAKE_CASE							:  List[str]=False  ,	):


           '''simple docstring'''

           __a		 =       size if size is not None else {'''height''': 20, '''width''': 20}
           __a		 =       crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
           __a		 =       parent
           __a		 =       batch_size
           __a		 =       num_channels
           __a		 =       image_size
           __a		 =       min_resolution
           __a		 =       max_resolution
           __a		 =       do_resize
           __a		 =       size
           __a		 =       do_center_crop
           __a		 =       crop_size
           __a		 =       do_normalize
           __a		 =       image_mean
           __a		 =       image_std
           __a		 =       do_reduce_labels





       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_reduce_labels": self.do_reduce_labels,
           }





def   __snake_case			(    ):
    __a		 =       load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,				split='''test'''       )

    __a		 =       Image.open(dataset[0]['''file''']       )
    __a		 =       Image.open(dataset[1]['''file''']       )

    return image, map





def   __snake_case			(    ):
    __a		 =       load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,				split='''test'''       )

    __a		 =       Image.open(ds[0]['''file''']       )
    __a		 =       Image.open(ds[1]['''file''']       )
    __a		 =       Image.open(ds[2]['''file''']       )
    __a		 =       Image.open(ds[3]['''file''']       )

    return [imagea, imagea], [mapa, mapa]

@require_torch
@require_vision
class 					_A (			__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					Union[str, Any]		      =		BeitImageProcessor if is_vision_available() else None



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       BeitImageProcessingTester(self)



       @property
       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''do_resize'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''size'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''do_center_crop'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''center_crop'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''do_normalize'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''image_mean'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''image_std'''))



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           __a		 =       self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size  ,	{'''height''': 20, '''width''': 20})
           self.assertEqual(image_processor.crop_size  ,	{'''height''': 18, '''width''': 18})
           self.assertEqual(image_processor.do_reduce_labels  ,	__SCREAMING_SNAKE_CASE)

           __a		 =       self.image_processing_class.from_dict(
               self.image_processor_dict  ,	size=42  ,	crop_size=84  ,	reduce_labels=__SCREAMING_SNAKE_CASE)
           self.assertEqual(image_processor.size  ,	{'''height''': 42, '''width''': 42})
           self.assertEqual(image_processor.crop_size  ,	{'''height''': 84, '''width''': 84})
           self.assertEqual(image_processor.do_reduce_labels  ,	__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           __a		 =       prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE)
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	Image.Image)

           # Test not batched input
           __a		 =       image_processing(image_inputs[0]  ,	return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape  ,	(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)

           # Test batched
           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape  ,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           __a		 =       prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE  ,	numpify=__SCREAMING_SNAKE_CASE)
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	np.ndarray)

           # Test not batched input
           __a		 =       image_processing(image_inputs[0]  ,	return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape  ,	(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)

           # Test batched
           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape  ,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           __a		 =       prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE  ,	torchify=__SCREAMING_SNAKE_CASE)
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	torch.Tensor)

           # Test not batched input
           __a		 =       image_processing(image_inputs[0]  ,	return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape  ,	(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)

           # Test batched
           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape  ,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           __a		 =       prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE  ,	torchify=__SCREAMING_SNAKE_CASE)
           __a		 =       []
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	torch.Tensor)
               maps.append(torch.zeros(image.shape[-2:]).long())

           # Test not batched input
           __a		 =       image_processing(image_inputs[0]  ,	maps[0]  ,	return_tensors='''pt''')
           self.assertEqual(
               encoding['''pixel_values'''].shape  ,	(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(
               encoding['''labels'''].shape  ,	(
                   1,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(encoding['''labels'''].dtype  ,	torch.long)
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)

           # Test batched
           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''')
           self.assertEqual(
               encoding['''pixel_values'''].shape  ,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(
               encoding['''labels'''].shape  ,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(encoding['''labels'''].dtype  ,	torch.long)
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)

           # Test not batched input (PIL images)
           __a ,     __a		 =       prepare_semantic_single_inputs()

           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''')
           self.assertEqual(
               encoding['''pixel_values'''].shape  ,	(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(
               encoding['''labels'''].shape  ,	(
                   1,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(encoding['''labels'''].dtype  ,	torch.long)
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)

           # Test batched input (PIL images)
           __a ,     __a		 =       prepare_semantic_batch_inputs()

           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''')
           self.assertEqual(
               encoding['''pixel_values'''].shape  ,	(
                   2,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(
               encoding['''labels'''].shape  ,	(
                   2,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )  ,	)
           self.assertEqual(encoding['''labels'''].dtype  ,	torch.long)
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)





       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)

           # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
           __a ,     __a		 =       prepare_semantic_single_inputs()
           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''')
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 150)

           __a		 =       True
           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''')
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)





