
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor




class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def __init__(    self						,  lowercase						,  lowercase=7						,  lowercase=3						,  lowercase=30						,  lowercase=400						,  lowercase=True						,  lowercase=None						,  lowercase=True						,  lowercase=[0.5, 0.5, 0.5]						,  lowercase=[0.5, 0.5, 0.5]						,  lowercase=True						,  lowercase=1 / 255						,  lowercase=True						,  )				->       Dict:
					# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
					lowerCamelCase_						=			size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
					lowerCamelCase_						=			parent
					lowerCamelCase_						=			batch_size
					lowerCamelCase_						=			num_channels
					lowerCamelCase_						=			min_resolution
					lowerCamelCase_						=			max_resolution
					lowerCamelCase_						=			do_resize
					lowerCamelCase_						=			size
					lowerCamelCase_						=			do_normalize
					lowerCamelCase_						=			image_mean
					lowerCamelCase_						=			image_std
					lowerCamelCase_						=			do_rescale
					lowerCamelCase_						=			rescale_factor
					lowerCamelCase_						=			do_pad



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_rescale": self.do_rescale,
					    "rescale_factor": self.rescale_factor,
					    "do_pad": self.do_pad,
					}



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase=False )				->       str:
					if not batched:
									lowerCamelCase_						=			image_inputs[0]
									if isinstance(lowercase						,  Image.Image ):
													lowerCamelCase_	,    lowerCamelCase_						=			image.size
									else:
													lowerCamelCase_	,    lowerCamelCase_						=			image.shape[1], image.shape[2]
									if w < h:
													lowerCamelCase_						=			int(self.size["shortest_edge"] * h / w )
													lowerCamelCase_						=			self.size["shortest_edge"]
									elif w > h:
													lowerCamelCase_						=			self.size["shortest_edge"]
													lowerCamelCase_						=			int(self.size["shortest_edge"] * w / h )
									else:
													lowerCamelCase_						=			self.size["shortest_edge"]
													lowerCamelCase_						=			self.size["shortest_edge"]

					else:
									lowerCamelCase_						=			[]
									for image in image_inputs:
													lowerCamelCase_	,    lowerCamelCase_						=			self.get_expected_values([image] )
													expected_values.append((expected_height, expected_width) )
									lowerCamelCase_						=			max(lowercase						,  key=lambda lowercase : item[0] )[0]
									lowerCamelCase_						=			max(lowercase						,  key=lambda lowercase : item[1] )[1]

					return expected_height, expected_width




@require_torch
@require_vision
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = DeformableDetrImageProcessor if is_vision_available() else None



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			DeformableDetrImageProcessingTester(self )



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					return self.image_processor_tester.prepare_image_processor_dict()



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(lowercase						,  "image_mean" ) )
					self.assertTrue(hasattr(lowercase						,  "image_std" ) )
					self.assertTrue(hasattr(lowercase						,  "do_normalize" ) )
					self.assertTrue(hasattr(lowercase						,  "do_resize" ) )
					self.assertTrue(hasattr(lowercase						,  "do_rescale" ) )
					self.assertTrue(hasattr(lowercase						,  "do_pad" ) )
					self.assertTrue(hasattr(lowercase						,  "size" ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size						,  {"shortest_edge": 18, "longest_edge": 1333} )
					self.assertEqual(image_processor.do_pad						,  lowercase )

					lowerCamelCase_						=			self.image_processing_class.from_dict(
					    self.image_processor_dict						,  size=42						,  max_size=84						,  pad_and_return_pixel_mask=lowercase )
					self.assertEqual(image_processor.size						,  {"shortest_edge": 42, "longest_edge": 84} )
					self.assertEqual(image_processor.do_pad						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					pass



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					# 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=lowercase )
					for image in image_inputs:
									self.assertIsInstance(lowercase						,  Image.Image )

					# Test not batched input
					lowerCamelCase_						=			image_processing(image_inputs[0]						,  return_tensors="pt" ).pixel_values

					lowerCamelCase_	,    lowerCamelCase_						=			self.image_processor_tester.get_expected_values(lowercase )

					self.assertEqual(
					    encoded_images.shape						,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,  )

					# Test batched
					lowerCamelCase_	,    lowerCamelCase_						=			self.image_processor_tester.get_expected_values(lowercase						,  batched=lowercase )

					lowerCamelCase_						=			image_processing(lowercase						,  return_tensors="pt" ).pixel_values
					self.assertEqual(
					    encoded_images.shape						,  (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )						,  )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					# 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=lowercase						,  numpify=lowercase )
					for image in image_inputs:
									self.assertIsInstance(lowercase						,  np.ndarray )

					# Test not batched input
					lowerCamelCase_						=			image_processing(image_inputs[0]						,  return_tensors="pt" ).pixel_values

					lowerCamelCase_	,    lowerCamelCase_						=			self.image_processor_tester.get_expected_values(lowercase )

					self.assertEqual(
					    encoded_images.shape						,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,  )

					# Test batched
					lowerCamelCase_						=			image_processing(lowercase						,  return_tensors="pt" ).pixel_values

					lowerCamelCase_	,    lowerCamelCase_						=			self.image_processor_tester.get_expected_values(lowercase						,  batched=lowercase )

					self.assertEqual(
					    encoded_images.shape						,  (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )						,  )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					# 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=lowercase						,  torchify=lowercase )
					for image in image_inputs:
									self.assertIsInstance(lowercase						,  torch.Tensor )

					# Test not batched input
					lowerCamelCase_						=			image_processing(image_inputs[0]						,  return_tensors="pt" ).pixel_values

					lowerCamelCase_	,    lowerCamelCase_						=			self.image_processor_tester.get_expected_values(lowercase )

					self.assertEqual(
					    encoded_images.shape						,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,  )

					# Test batched
					lowerCamelCase_						=			image_processing(lowercase						,  return_tensors="pt" ).pixel_values

					lowerCamelCase_	,    lowerCamelCase_						=			self.image_processor_tester.get_expected_values(lowercase						,  batched=lowercase )

					self.assertEqual(
					    encoded_images.shape						,  (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )						,  )



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					# prepare image and target
					lowerCamelCase_						=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
					with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"						,  "r" ) as f:
									lowerCamelCase_						=			json.loads(f.read() )

					lowerCamelCase_						=			{"image_id": 39769, "annotations": target}

					# encode them
					lowerCamelCase_						=			DeformableDetrImageProcessor()
					lowerCamelCase_						=			image_processing(images=lowercase						,  annotations=lowercase						,  return_tensors="pt" )

					# verify pixel values
					lowerCamelCase_						=			torch.Size([1, 3, 800, 1066] )
					self.assertEqual(encoding["pixel_values"].shape						,  lowercase )

					lowerCamelCase_						=			torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
					self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]						,  lowercase						,  atol=1e-4 ) )

					# verify area
					lowerCamelCase_						=			torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["area"]						,  lowercase ) )
					# verify boxes
					lowerCamelCase_						=			torch.Size([6, 4] )
					self.assertEqual(encoding["labels"][0]["boxes"].shape						,  lowercase )
					lowerCamelCase_						=			torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]						,  lowercase						,  atol=1e-3 ) )
					# verify image_id
					lowerCamelCase_						=			torch.tensor([39769] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]						,  lowercase ) )
					# verify is_crowd
					lowerCamelCase_						=			torch.tensor([0, 0, 0, 0, 0, 0] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]						,  lowercase ) )
					# verify class_labels
					lowerCamelCase_						=			torch.tensor([75, 75, 63, 65, 17, 17] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]						,  lowercase ) )
					# verify orig_size
					lowerCamelCase_						=			torch.tensor([480, 640] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]						,  lowercase ) )
					# verify size
					lowerCamelCase_						=			torch.tensor([800, 1066] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["size"]						,  lowercase ) )



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					# prepare image, target and masks_path
					lowerCamelCase_						=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
					with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"						,  "r" ) as f:
									lowerCamelCase_						=			json.loads(f.read() )

					lowerCamelCase_						=			{"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

					lowerCamelCase_						=			pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

					# encode them
					lowerCamelCase_						=			DeformableDetrImageProcessor(format="coco_panoptic" )
					lowerCamelCase_						=			image_processing(images=lowercase						,  annotations=lowercase						,  masks_path=lowercase						,  return_tensors="pt" )

					# verify pixel values
					lowerCamelCase_						=			torch.Size([1, 3, 800, 1066] )
					self.assertEqual(encoding["pixel_values"].shape						,  lowercase )

					lowerCamelCase_						=			torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
					self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]						,  lowercase						,  atol=1e-4 ) )

					# verify area
					lowerCamelCase_						=			torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["area"]						,  lowercase ) )
					# verify boxes
					lowerCamelCase_						=			torch.Size([6, 4] )
					self.assertEqual(encoding["labels"][0]["boxes"].shape						,  lowercase )
					lowerCamelCase_						=			torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]						,  lowercase						,  atol=1e-3 ) )
					# verify image_id
					lowerCamelCase_						=			torch.tensor([39769] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]						,  lowercase ) )
					# verify is_crowd
					lowerCamelCase_						=			torch.tensor([0, 0, 0, 0, 0, 0] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]						,  lowercase ) )
					# verify class_labels
					lowerCamelCase_						=			torch.tensor([17, 17, 63, 75, 75, 93] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]						,  lowercase ) )
					# verify masks
					lowerCamelCase_						=			822873
					self.assertEqual(encoding["labels"][0]["masks"].sum().item()						,  lowercase )
					# verify orig_size
					lowerCamelCase_						=			torch.tensor([480, 640] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]						,  lowercase ) )
					# verify size
					lowerCamelCase_						=			torch.tensor([800, 1066] )
					self.assertTrue(torch.allclose(encoding["labels"][0]["size"]						,  lowercase ) )







import numpy as np

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	(0, 0)
					__UpperCAmelCase    :     List[str]													=	None
					__UpperCAmelCase    :     int													=	0
					__UpperCAmelCase    :     Union[str, Any]													=	0
					__UpperCAmelCase    :     List[Any]													=	0




			def __eq__(   self							: Optional[int]       ,       a_							: List[Any] ):
					'''simple docstring'''





					return self.position == cell.position




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					print(self.position )

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: str       ,       a_							: Dict=(5, 5) ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	np.zeros(a_ )
					__UpperCAmelCase    :     List[Any]													=	world_size[0]
					__UpperCAmelCase    :     List[str]													=	world_size[1]




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					print(self.w )




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	[
					    (-1, -1),
					    (-1, 0),
					    (-1, 1),
					    (0, -1),
					    (0, 1),
					    (1, -1),
					    (1, 0),
					    (1, 1),
					]
					__UpperCAmelCase    :     str													=	cell.position[0]
					__UpperCAmelCase    :     List[Any]													=	cell.position[1]
					__UpperCAmelCase    :     str													=	[]
					for n in neughbour_cord:
							__UpperCAmelCase    :     Optional[Any]													=	current_x + n[0]
							__UpperCAmelCase    :     Any													=	current_y + n[1]
							if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
									__UpperCAmelCase    :     int													=	Cell()
									__UpperCAmelCase    :     List[str]													=	(x, y)
									__UpperCAmelCase    :     List[Any]													=	cell
									neighbours.append(a_ )
					return neighbours

def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	[]
		__UpperCAmelCase    :     Optional[Any]													=	[]
		_open.append(_UpperCAmelCase						)

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

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


if __name__ == "__main__":
				__A   			=Gridworld()
				# Start position and goal
				__A   			=Cell()
				__A   			=(0, 0)
				__A   			=Cell()
				__A   			=(4, 4)
				print(f'''path from {start.position} to {goal.position}''')
				__A   			=astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								__A   			=1
				print(world.w)





