




'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=18     ,	__SCREAMING_SNAKE_CASE=30     ,	__SCREAMING_SNAKE_CASE=400     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]     ,	__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]     ,	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	num_channels
            __snake_case            =	image_size
            __snake_case            =	min_resolution
            __snake_case            =	max_resolution
            __snake_case            =	do_resize
            __snake_case            =	size if size is not None else {'''height''': 18, '''width''': 20}
            __snake_case            =	do_thumbnail
            __snake_case            =	do_align_axis
            __snake_case            =	do_pad
            __snake_case            =	do_normalize
            __snake_case            =	image_mean
            __snake_case            =	image_std




       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_thumbnail": self.do_thumbnail,
                "do_align_long_axis": self.do_align_axis,
                "do_pad": self.do_pad,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }





@require_torch
@require_vision
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Optional[Any]										=      DonutImageProcessor if is_vision_available() else None


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	DonutImageProcessingTester(self	)


       @property
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	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_thumbnail'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_align_long_axis'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_pad'''	)	)
            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	)   ->	int:



            '''simple docstring'''




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

            __snake_case            =	self.image_processing_class.from_dict(self.image_processor_dict     ,	size=42	)
            self.assertEqual(image_processor.size     ,	{'''height''': 42, '''width''': 42}	)

            # Previous config had dimensions in (width, height) order
            __snake_case            =	self.image_processing_class.from_dict(self.image_processor_dict     ,	size=(42, 84)	)
            self.assertEqual(image_processor.size     ,	{'''height''': 84, '''width''': 42}	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            pass


       @is_flaky()
       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            __snake_case            =	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
            __snake_case            =	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.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )     ,	)

            # Test batched
            __snake_case            =	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.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )     ,	)


       @is_flaky()
       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            __snake_case            =	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
            __snake_case            =	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.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )     ,	)

            # Test batched
            __snake_case            =	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.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )     ,	)




       @is_flaky()
       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            __snake_case            =	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
            __snake_case            =	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.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )     ,	)

            # Test batched
            __snake_case            =	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.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )     ,	)











import numpy as np


class 	__snake_case   :
		def __init__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  (0, 0)
						lowerCAmelCase__		  =  None
						lowerCAmelCase__		  =  0
						lowerCAmelCase__		  =  0
						lowerCAmelCase__		  =  0
		def __eq__(							self    ,a_						):







						"""simple docstring"""
						return self.position == cell.position



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						print(self.position						)



class 	__snake_case   :
		def __init__(							self    ,a_=(5, 5)						):







						"""simple docstring"""
						lowerCAmelCase__		  =  np.zeros(a_						)
						lowerCAmelCase__		  =  world_size[0]
						lowerCAmelCase__		  =  world_size[1]
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						print(self.w						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [
						    (-1, -1),
						    (-1, 0),
						    (-1, 1),
						    (0, -1),
						    (0, 1),
						    (1, -1),
						    (1, 0),
						    (1, 1),
						]
						lowerCAmelCase__		  =  cell.position[0]
						lowerCAmelCase__		  =  cell.position[1]
						lowerCAmelCase__		  =  []
						for n in neughbour_cord:
										lowerCAmelCase__		  =  current_x + n[0]
										lowerCAmelCase__		  =  current_y + n[1]
										if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
														lowerCAmelCase__		  =  Cell()
														lowerCAmelCase__		  =  (x, y)
														lowerCAmelCase__		  =  cell
														neighbours.append(a_						)
						return neighbours







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     int:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  []
				_open.append(snake_case__					)

				while _open:
								lowerCAmelCase__		  =  np.argmin([n.f for n in _open]					)
								lowerCAmelCase__		  =  _open[min_f]
								_closed.append(_open.pop(snake_case__					)					)
								if current == goal:
												break
								for n in world.get_neigbours(snake_case__					):
												for c in _closed:
																if c == n:
																				continue
												lowerCAmelCase__		  =  current.g + 1
												lowerCAmelCase__  ,   lowerCAmelCase__		  =  n.position
												lowerCAmelCase__  ,   lowerCAmelCase__		  =  goal.position
												lowerCAmelCase__		  =  (ya - ya) ** 2 + (xa - xa) ** 2
												lowerCAmelCase__		  =  n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(snake_case__					)
				lowerCAmelCase__		  =  []
				while current.parent is not None:
								path.append(current.position					)
								lowerCAmelCase__		  =  current.parent
				path.append(current.position					)
				return path[::-1]


if __name__ == "__main__":
	_lowerCAmelCase	:   str    =   Gridworld()
	# Start position and goal
	_lowerCAmelCase	:   List[Any]    =   Cell()
	_lowerCAmelCase	:   List[str]    =   (0, 0)
	_lowerCAmelCase	:   Optional[int]    =   Cell()
	_lowerCAmelCase	:   int    =   (4, 4)
	print(f"""path from {start.position} to {goal.position}""")
	_lowerCAmelCase	:   Optional[int]    =   astar(world, start, goal)
	#   Just for visual reasons.
	for i in s:
		_lowerCAmelCase	:   int    =   1
	print(world.w)
