



import unittest

import numpy as np

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 MobileViTImageProcessor


class 	__snake_case   ( unittest.TestCase ):
		def __init__(							self    ,a_    ,a_=7    ,a_=3    ,a_=18    ,a_=30    ,a_=400    ,a_=True    ,a_=None    ,a_=True    ,a_=None    ,a_=True    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  size if size is not None else {'shortest_edge': 20}
						lowerCAmelCase__		  =  crop_size if crop_size is not None else {'height': 18, 'width': 18}
						lowerCAmelCase__		  =  parent
						lowerCAmelCase__		  =  batch_size
						lowerCAmelCase__		  =  num_channels
						lowerCAmelCase__		  =  image_size
						lowerCAmelCase__		  =  min_resolution
						lowerCAmelCase__		  =  max_resolution
						lowerCAmelCase__		  =  do_resize
						lowerCAmelCase__		  =  size
						lowerCAmelCase__		  =  do_center_crop
						lowerCAmelCase__		  =  crop_size
						lowerCAmelCase__		  =  do_flip_channel_order



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_flip_channel_order": self.do_flip_channel_order,
						}



@require_torch
@require_vision
class 	__snake_case   ( SCREAMING_SNAKE_CASE							, unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	MobileViTImageProcessor if is_vision_available() else None
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  MobileViTImageProcessingTester(self						)
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.image_processor_tester.prepare_image_processor_dict()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(a_    ,'do_resize'						)						)
						self.assertTrue(hasattr(a_    ,'size'						)						)
						self.assertTrue(hasattr(a_    ,'do_center_crop'						)						)
						self.assertTrue(hasattr(a_    ,'center_crop'						)						)
						self.assertTrue(hasattr(a_    ,'do_flip_channel_order'						)						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 20}						)
						self.assertEqual(image_processor.crop_size    ,{'height': 18, 'width': 18}						)

						lowerCAmelCase__		  =  self.image_processing_class.from_dict(self.image_processor_dict    ,size=42    ,crop_size=84						)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 42}						)
						self.assertEqual(image_processor.crop_size    ,{'height': 84, 'width': 84}						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						pass
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# Initialize image_processing
						lowerCAmelCase__		  =  self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						lowerCAmelCase__		  =  prepare_image_inputs(self.image_processor_tester    ,equal_resolution=a_						)
						for image in image_inputs:
										self.assertIsInstance(a_    ,Image.Image						)

						# Test not batched input
						lowerCAmelCase__		  =  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
						lowerCAmelCase__		  =  image_processing(a_    ,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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# Initialize image_processing
						lowerCAmelCase__		  =  self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						lowerCAmelCase__		  =  prepare_image_inputs(self.image_processor_tester    ,equal_resolution=a_    ,numpify=a_						)
						for image in image_inputs:
										self.assertIsInstance(a_    ,np.ndarray						)

						# Test not batched input
						lowerCAmelCase__		  =  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
						lowerCAmelCase__		  =  image_processing(a_    ,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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# Initialize image_processing
						lowerCAmelCase__		  =  self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						lowerCAmelCase__		  =  prepare_image_inputs(self.image_processor_tester    ,equal_resolution=a_    ,torchify=a_						)
						for image in image_inputs:
										self.assertIsInstance(a_    ,torch.Tensor						)

						# Test not batched input
						lowerCAmelCase__		  =  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
						lowerCAmelCase__		  =  image_processing(a_    ,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'],
						    )    ,)





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)
