"""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       a_      (     unittest.TestCase			):




					def __init__(	self						:     Union[str, Any]  ,						snake_case__						:     Dict  ,						snake_case__						:     str=7  ,						snake_case__						:     str=3  ,						snake_case__						:     Optional[Any]=30  ,						snake_case__						:     Any=400  ,						snake_case__						:     Union[str, Any]=True  ,						snake_case__						:     List[str]=None  ,						snake_case__						:     List[str]=True  ,						snake_case__						:     List[str]=[0.5, 0.5, 0.5]  ,						snake_case__						:     Any=[0.5, 0.5, 0.5]  ,						snake_case__						:     Dict=True  ,						snake_case__						:     Any=1 / 255  ,						snake_case__						:     str=True  ,						):
							# 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						:     Optional[int]     ):
							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						:     Any  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     str=False     ):
							if not batched:
									lowerCAmelCase__        =   image_inputs[0]
									if isinstance(snake_case__  ,						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(snake_case__  ,						key=lambda snake_case__     : item[0]     )[0]
									lowerCAmelCase__        =   max(snake_case__  ,						key=lambda snake_case__     : item[1]     )[1]

							return expected_height, expected_width

@require_torch
@require_vision
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Dict			=    YolosImageProcessor if is_vision_available() else None




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   YolosImageProcessingTester(self     )




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   self.image_processing_class(**self.image_processor_dict     )
							self.assertTrue(hasattr(snake_case__  ,						"""image_mean"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""image_std"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""do_normalize"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""do_resize"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""size"""     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     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  ,						snake_case__     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							pass




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							# 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=snake_case__     )
							for image in image_inputs:
									self.assertIsInstance(snake_case__  ,						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(snake_case__     )

							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(snake_case__  ,						batched=snake_case__     )

							lowerCAmelCase__        =   image_processing(snake_case__  ,						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						:     Optional[int]     ):
							# 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=snake_case__  ,						numpify=snake_case__     )
							for image in image_inputs:
									self.assertIsInstance(snake_case__  ,						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(snake_case__     )

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

							# Test batched
							lowerCAmelCase__        =   image_processing(snake_case__  ,						return_tensors="""pt"""     ).pixel_values

							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.image_processor_tester.get_expected_values(snake_case__  ,						batched=snake_case__     )

							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=snake_case__  ,						torchify=snake_case__     )
							for image in image_inputs:
									self.assertIsInstance(snake_case__  ,						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(snake_case__     )

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

							# Test batched
							lowerCAmelCase__        =   image_processing(snake_case__  ,						return_tensors="""pt"""     ).pixel_values

							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.image_processor_tester.get_expected_values(snake_case__  ,						batched=snake_case__     )

							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						:     Union[str, Any]     ):
							# Initialize image_processings
							lowerCAmelCase__        =   self.image_processing_class(**self.image_processor_dict     )
							lowerCAmelCase__        =   self.image_processing_class(do_resize=snake_case__  ,						do_normalize=snake_case__  ,						do_rescale=snake_case__     )
							# create random PyTorch tensors
							lowerCAmelCase__        =   prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=snake_case__  ,						torchify=snake_case__     )
							for image in image_inputs:
									self.assertIsInstance(snake_case__  ,						torch.Tensor     )

							# Test whether the method "pad" and calling the image processor return the same tensors
							lowerCAmelCase__        =   image_processing_a.pad(snake_case__  ,						return_tensors="""pt"""     )
							lowerCAmelCase__        =   image_processing_a(snake_case__  ,						return_tensors="""pt"""     )

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




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							# 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__        =   YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""     )
							lowerCAmelCase__        =   image_processing(images=snake_case__  ,						annotations=snake_case__  ,						return_tensors="""pt"""     )

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

							lowerCAmelCase__        =   torch.tensor([0.2796, 0.3138, 0.3481]     )
							self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]  ,						snake_case__  ,						atol=1E-4     )     )

							# verify area
							lowerCAmelCase__        =   torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,						snake_case__     )     )
							# verify boxes
							lowerCAmelCase__        =   torch.Size([6, 4]     )
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,						snake_case__     )
							lowerCAmelCase__        =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,						snake_case__  ,						atol=1E-3     )     )
							# verify image_id
							lowerCAmelCase__        =   torch.tensor([39769]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,						snake_case__     )     )
							# verify is_crowd
							lowerCAmelCase__        =   torch.tensor([0, 0, 0, 0, 0, 0]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,						snake_case__     )     )
							# verify class_labels
							lowerCAmelCase__        =   torch.tensor([75, 75, 63, 65, 17, 17]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,						snake_case__     )     )
							# verify orig_size
							lowerCAmelCase__        =   torch.tensor([480, 640]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,						snake_case__     )     )
							# verify size
							lowerCAmelCase__        =   torch.tensor([800, 1066]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,						snake_case__     )     )




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							# 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__        =   YolosImageProcessor(format="""coco_panoptic"""     )
							lowerCAmelCase__        =   image_processing(images=snake_case__  ,						annotations=snake_case__  ,						masks_path=snake_case__  ,						return_tensors="""pt"""     )

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

							lowerCAmelCase__        =   torch.tensor([0.2796, 0.3138, 0.3481]     )
							self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]  ,						snake_case__  ,						atol=1E-4     )     )

							# verify area
							lowerCAmelCase__        =   torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,						snake_case__     )     )
							# verify boxes
							lowerCAmelCase__        =   torch.Size([6, 4]     )
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,						snake_case__     )
							lowerCAmelCase__        =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,						snake_case__  ,						atol=1E-3     )     )
							# verify image_id
							lowerCAmelCase__        =   torch.tensor([39769]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,						snake_case__     )     )
							# verify is_crowd
							lowerCAmelCase__        =   torch.tensor([0, 0, 0, 0, 0, 0]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,						snake_case__     )     )
							# verify class_labels
							lowerCAmelCase__        =   torch.tensor([17, 17, 63, 75, 75, 93]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,						snake_case__     )     )
							# verify masks
							lowerCAmelCase__        =   822873
							self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()  ,						snake_case__     )
							# verify orig_size
							lowerCAmelCase__        =   torch.tensor([480, 640]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,						snake_case__     )     )
							# verify size
							lowerCAmelCase__        =   torch.tensor([800, 1066]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,						snake_case__     )     )





"""simple docstring"""




def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		while b:
				lowerCAmelCase__   ,					lowerCAmelCase__        =   b, a % b
		return a







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		return a if b == 0 else euclidean_gcd_recursive(lowerCamelCase__			,  a % b     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3			,  5     )}"""     )
		print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5			,  3     )}"""     )
		print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1			,  3     )}"""     )
		print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3			,  6     )}"""     )
		print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6			,  3     )}"""     )

		print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3			,  5     )}"""     )
		print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5			,  3     )}"""     )
		print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1			,  3     )}"""     )
		print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3			,  6     )}"""     )
		print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6			,  3     )}"""     )


if __name__ == "__main__":
			main()




