





"""simple docstring"""






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 YolosImageProcessor




class 					__lowercase      (      unittest.TestCase				):







					'''simple docstring'''




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







					def 			_lowerCamelCase		(    self  ):
									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 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase=False  ):
									if not batched:
													__a			:    Dict      							=	image_inputs[0]
													if isinstance(_UpperCAmelCase						,      Image.Image  ):
																	__a							,    __a			:    Union[str, Any]      							=	image.size
													else:
																	__a							,    __a			:    Any      							=	image.shape[1], image.shape[2]
													if w < h:
																	__a			:    List[str]      							=	int(self.size['''shortest_edge'''] * h / w  )
																	__a			:    int      							=	self.size['''shortest_edge''']
													elif w > h:
																	__a			:    Dict      							=	self.size['''shortest_edge''']
																	__a			:    int      							=	int(self.size['''shortest_edge'''] * w / h  )
													else:
																	__a			:    str      							=	self.size['''shortest_edge''']
																	__a			:    str      							=	self.size['''shortest_edge''']

									else:
													__a			:    Optional[int]      							=	[]
													for image in image_inputs:
																	__a							,    __a			:    Optional[Any]      							=	self.get_expected_values([image]  )
																	expected_values.append((expected_height, expected_width)  )
													__a			:    Optional[int]      							=	max(_UpperCAmelCase						,      key=lambda _UpperCAmelCase  : item[0]  )[0]
													__a			:    str      							=	max(_UpperCAmelCase						,      key=lambda _UpperCAmelCase  : item[1]  )[1]

									return expected_height, expected_width






@require_torch
@require_vision
class 					__lowercase      (      _UpperCamelCase ,   unittest.TestCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	YolosImageProcessor if is_vision_available() else None







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[Any]      							=	YolosImageProcessingTester(self  )







					@property
					def 			_lowerCamelCase		(    self  ):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 			_lowerCamelCase		(    self  ):
									__a			:    Any      							=	self.image_processing_class(**self.image_processor_dict  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''image_mean'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''image_std'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''do_normalize'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''do_resize'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''size'''  )  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[int]      							=	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						,      _UpperCAmelCase  )

									__a			:    Optional[Any]      							=	self.image_processing_class.from_dict(
									    self.image_processor_dict						,      size=42						,      max_size=84						,      pad_and_return_pixel_mask=_UpperCAmelCase  )
									self.assertEqual(image_processor.size						,      {'''shortest_edge''': 42, '''longest_edge''': 84}  )
									self.assertEqual(image_processor.do_pad						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									pass







					def 			_lowerCamelCase		(    self  ):
									# Initialize image_processing
									__a			:    Any      							=	self.image_processing_class(**self.image_processor_dict  )
									# create random PIL images
									__a			:    Union[str, Any]      							=	prepare_image_inputs(self.image_processor_tester						,      equal_resolution=_UpperCAmelCase  )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase						,      Image.Image  )

									# Test not batched input
									__a			:    str      							=	image_processing(image_inputs[0]						,      return_tensors='''pt'''  ).pixel_values

									__a							,    __a			:    int      							=	self.image_processor_tester.get_expected_values(_UpperCAmelCase  )

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

									# Test batched
									__a							,    __a			:    int      							=	self.image_processor_tester.get_expected_values(_UpperCAmelCase						,      batched=_UpperCAmelCase  )

									__a			:    List[Any]      							=	image_processing(_UpperCAmelCase						,      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 			_lowerCamelCase		(    self  ):
									# Initialize image_processing
									__a			:    int      							=	self.image_processing_class(**self.image_processor_dict  )
									# create random numpy tensors
									__a			:    Any      							=	prepare_image_inputs(self.image_processor_tester						,      equal_resolution=_UpperCAmelCase						,      numpify=_UpperCAmelCase  )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase						,      np.ndarray  )

									# Test not batched input
									__a			:    Optional[int]      							=	image_processing(image_inputs[0]						,      return_tensors='''pt'''  ).pixel_values

									__a							,    __a			:    Optional[int]      							=	self.image_processor_tester.get_expected_values(_UpperCAmelCase  )

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

									# Test batched
									__a			:    Tuple      							=	image_processing(_UpperCAmelCase						,      return_tensors='''pt'''  ).pixel_values

									__a							,    __a			:    Optional[int]      							=	self.image_processor_tester.get_expected_values(_UpperCAmelCase						,      batched=_UpperCAmelCase  )

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







					def 			_lowerCamelCase		(    self  ):
									# Initialize image_processing
									__a			:    Dict      							=	self.image_processing_class(**self.image_processor_dict  )
									# create random PyTorch tensors
									__a			:    Union[str, Any]      							=	prepare_image_inputs(self.image_processor_tester						,      equal_resolution=_UpperCAmelCase						,      torchify=_UpperCAmelCase  )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase						,      torch.Tensor  )

									# Test not batched input
									__a			:    Dict      							=	image_processing(image_inputs[0]						,      return_tensors='''pt'''  ).pixel_values

									__a							,    __a			:    int      							=	self.image_processor_tester.get_expected_values(_UpperCAmelCase  )

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

									# Test batched
									__a			:    Any      							=	image_processing(_UpperCAmelCase						,      return_tensors='''pt'''  ).pixel_values

									__a							,    __a			:    List[str]      							=	self.image_processor_tester.get_expected_values(_UpperCAmelCase						,      batched=_UpperCAmelCase  )

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







					def 			_lowerCamelCase		(    self  ):
									# Initialize image_processings
									__a			:    Dict      							=	self.image_processing_class(**self.image_processor_dict  )
									__a			:    Any      							=	self.image_processing_class(do_resize=_UpperCAmelCase						,      do_normalize=_UpperCAmelCase						,      do_rescale=_UpperCAmelCase  )
									# create random PyTorch tensors
									__a			:    Any      							=	prepare_image_inputs(self.image_processor_tester						,      equal_resolution=_UpperCAmelCase						,      torchify=_UpperCAmelCase  )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase						,      torch.Tensor  )

									# Test whether the method "pad" and calling the image processor return the same tensors
									__a			:    List[str]      							=	image_processing_a.pad(_UpperCAmelCase						,      return_tensors='''pt'''  )
									__a			:    Dict      							=	image_processing_a(_UpperCAmelCase						,      return_tensors='''pt'''  )

									self.assertTrue(
									    torch.allclose(encoded_images_with_method['''pixel_values''']						,      encoded_images['''pixel_values''']						,      atol=1e-4  )  )







					@slow
					def 			_lowerCamelCase		(    self  ):
									# prepare image and target
									__a			:    Dict      							=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
									with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''						,      '''r'''  ) as f:
													__a			:    Optional[Any]      							=	json.loads(f.read()  )

									__a			:    Optional[Any]      							=	{'''image_id''': 39769, '''annotations''': target}

									# encode them
									__a			:    Any      							=	YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''  )
									__a			:    List[Any]      							=	image_processing(images=_UpperCAmelCase						,      annotations=_UpperCAmelCase						,      return_tensors='''pt'''  )

									# verify pixel values
									__a			:    List[str]      							=	torch.Size([1, 3, 800, 1066]  )
									self.assertEqual(encoding['''pixel_values'''].shape						,      _UpperCAmelCase  )

									__a			:    Optional[int]      							=	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]						,      _UpperCAmelCase						,      atol=1e-4  )  )

									# verify area
									__a			:    Union[str, Any]      							=	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''']						,      _UpperCAmelCase  )  )
									# verify boxes
									__a			:    Optional[Any]      							=	torch.Size([6, 4]  )
									self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,      _UpperCAmelCase  )
									__a			:    int      							=	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]						,      _UpperCAmelCase						,      atol=1e-3  )  )
									# verify image_id
									__a			:    Union[str, Any]      							=	torch.tensor([39769]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,      _UpperCAmelCase  )  )
									# verify is_crowd
									__a			:    Dict      							=	torch.tensor([0, 0, 0, 0, 0, 0]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,      _UpperCAmelCase  )  )
									# verify class_labels
									__a			:    Tuple      							=	torch.tensor([75, 75, 63, 65, 17, 17]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,      _UpperCAmelCase  )  )
									# verify orig_size
									__a			:    Any      							=	torch.tensor([480, 640]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,      _UpperCAmelCase  )  )
									# verify size
									__a			:    List[str]      							=	torch.tensor([800, 1066]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,      _UpperCAmelCase  )  )







					@slow
					def 			_lowerCamelCase		(    self  ):
									# prepare image, target and masks_path
									__a			:    str      							=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
									with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''						,      '''r'''  ) as f:
													__a			:    Optional[Any]      							=	json.loads(f.read()  )

									__a			:    int      							=	{'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

									__a			:    str      							=	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''  )

									# encode them
									__a			:    List[str]      							=	YolosImageProcessor(format='''coco_panoptic'''  )
									__a			:    Tuple      							=	image_processing(images=_UpperCAmelCase						,      annotations=_UpperCAmelCase						,      masks_path=_UpperCAmelCase						,      return_tensors='''pt'''  )

									# verify pixel values
									__a			:    List[str]      							=	torch.Size([1, 3, 800, 1066]  )
									self.assertEqual(encoding['''pixel_values'''].shape						,      _UpperCAmelCase  )

									__a			:    Dict      							=	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]						,      _UpperCAmelCase						,      atol=1e-4  )  )

									# verify area
									__a			:    List[str]      							=	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''']						,      _UpperCAmelCase  )  )
									# verify boxes
									__a			:    Optional[int]      							=	torch.Size([6, 4]  )
									self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,      _UpperCAmelCase  )
									__a			:    Dict      							=	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]						,      _UpperCAmelCase						,      atol=1e-3  )  )
									# verify image_id
									__a			:    List[Any]      							=	torch.tensor([39769]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,      _UpperCAmelCase  )  )
									# verify is_crowd
									__a			:    List[Any]      							=	torch.tensor([0, 0, 0, 0, 0, 0]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,      _UpperCAmelCase  )  )
									# verify class_labels
									__a			:    Optional[int]      							=	torch.tensor([17, 17, 63, 75, 75, 93]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,      _UpperCAmelCase  )  )
									# verify masks
									__a			:    Any      							=	822873
									self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()						,      _UpperCAmelCase  )
									# verify orig_size
									__a			:    Tuple      							=	torch.tensor([480, 640]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,      _UpperCAmelCase  )  )
									# verify size
									__a			:    int      							=	torch.tensor([800, 1066]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,      _UpperCAmelCase  )  )




SCREAMING_SNAKE_CASE__   			=   {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   list[str]:
			A__	  =     set()
			# keep track of all the paths to be checked
			A__	  =     [[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						A__	  =     queue.pop(0       )
						# get the last node from the path
						A__	  =     path[-1]
						if node not in explored:
									A__	  =     graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												A__	  =     list(__UpperCamelCase       )
												new_path.append(__UpperCamelCase       )
												queue.append(__UpperCamelCase       )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(__UpperCamelCase       )

    # in case there's no path between the 2 nodes
			return []


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			A__	  =     [start]
			A__	  =     set(__UpperCamelCase       )
			# Keep tab on distances from `start` node.
			A__	  =     {start: 0, target: -1}
			while queue:
						A__	  =     queue.pop(0       )
						if node == target:
									A__	  =     (
									    dist[node] if dist[target] == -1 else min(dist[target]			,						dist[node]       )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(__UpperCamelCase       )
												queue.append(__UpperCamelCase       )
												A__	  =     dist[node] + 1
			return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4




