
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 DetrImageProcessor







class        lowercase	(      unittest.TestCase	):




							def __init__(       self     ,A__     ,A__=7     ,A__=3     ,A__=3_0     ,A__=4_0_0     ,A__=True     ,A__=None     ,A__=True     ,A__=1 / 2_5_5     ,A__=True     ,A__=[0.5, 0.5, 0.5]     ,A__=[0.5, 0.5, 0.5]     ,A__=True     ,):
												# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
												lowercase					=		size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
												lowercase					=		parent
												lowercase					=		batch_size
												lowercase					=		num_channels
												lowercase					=		min_resolution
												lowercase					=		max_resolution
												lowercase					=		do_resize
												lowercase					=		size
												lowercase					=		do_rescale
												lowercase					=		rescale_factor
												lowercase					=		do_normalize
												lowercase					=		image_mean
												lowercase					=		image_std
												lowercase					=		do_pad




							def 				A__   (       self):
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_pad": self.do_pad,
												}





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

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

												return expected_height, expected_width







@require_torch
@require_vision
class        lowercase	(      SCREAMING_SNAKE_CASE__ ,						unittest.TestCase	):
							lowercase_ : Tuple       =DetrImageProcessor if is_vision_available() else None




							def 				A__   (       self):
												lowercase					=		DetrImageProcessingTester(self)




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




							def 				A__   (       self):
												lowercase					=		self.image_processing_class(**self.image_processor_dict)
												self.assertTrue(hasattr(A__     ,'''image_mean'''))
												self.assertTrue(hasattr(A__     ,'''image_std'''))
												self.assertTrue(hasattr(A__     ,'''do_normalize'''))
												self.assertTrue(hasattr(A__     ,'''do_rescale'''))
												self.assertTrue(hasattr(A__     ,'''rescale_factor'''))
												self.assertTrue(hasattr(A__     ,'''do_resize'''))
												self.assertTrue(hasattr(A__     ,'''size'''))
												self.assertTrue(hasattr(A__     ,'''do_pad'''))




							def 				A__   (       self):
												lowercase					=		self.image_processing_class.from_dict(self.image_processor_dict)
												self.assertEqual(image_processor.size     ,{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3})
												self.assertEqual(image_processor.do_pad     ,A__)

												lowercase					=		self.image_processing_class.from_dict(
												    self.image_processor_dict     ,size=4_2     ,max_size=8_4     ,pad_and_return_pixel_mask=A__)
												self.assertEqual(image_processor.size     ,{'''shortest_edge''': 4_2, '''longest_edge''': 8_4})
												self.assertEqual(image_processor.do_pad     ,A__)




							def 				A__   (       self):
												pass




							def 				A__   (       self):
												# Initialize image_processing
												lowercase					=		self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												lowercase					=		prepare_image_inputs(self.image_processor_tester     ,equal_resolution=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__     ,Image.Image)

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

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

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

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

												lowercase					=		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,
												        expected_height,
												        expected_width,
												    )     ,)




							def 				A__   (       self):
												# Initialize image_processing
												lowercase					=		self.image_processing_class(**self.image_processor_dict)
												# create random numpy tensors
												lowercase					=		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
												lowercase					=		image_processing(image_inputs[0]     ,return_tensors='''pt''').pixel_values

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

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

												# Test batched
												lowercase					=		image_processing(A__     ,return_tensors='''pt''').pixel_values

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

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




							def 				A__   (       self):
												# Initialize image_processing
												lowercase					=		self.image_processing_class(**self.image_processor_dict)
												# create random PyTorch tensors
												lowercase					=		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
												lowercase					=		image_processing(image_inputs[0]     ,return_tensors='''pt''').pixel_values

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

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

												# Test batched
												lowercase					=		image_processing(A__     ,return_tensors='''pt''').pixel_values

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

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




							@slow
							def 				A__   (       self):
												# prepare image and target
												lowercase					=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
												with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''     ,'''r''') as f:
																	lowercase					=		json.loads(f.read())

												lowercase					=		{'''image_id''': 3_9_7_6_9, '''annotations''': target}

												# encode them
												lowercase					=		DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50''')
												lowercase					=		image_processing(images=A__     ,annotations=A__     ,return_tensors='''pt''')

												# verify pixel values
												lowercase					=		torch.Size([1, 3, 8_0_0, 1_0_6_6])
												self.assertEqual(encoding['''pixel_values'''].shape     ,A__)

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

												# verify area
												lowercase					=		torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,A__))
												# verify boxes
												lowercase					=		torch.Size([6, 4])
												self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,A__)
												lowercase					=		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,A__     ,atol=1E-3))
												# verify image_id
												lowercase					=		torch.tensor([3_9_7_6_9])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,A__))
												# verify is_crowd
												lowercase					=		torch.tensor([0, 0, 0, 0, 0, 0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,A__))
												# verify class_labels
												lowercase					=		torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,A__))
												# verify orig_size
												lowercase					=		torch.tensor([4_8_0, 6_4_0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,A__))
												# verify size
												lowercase					=		torch.tensor([8_0_0, 1_0_6_6])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,A__))





							@slow
							def 				A__   (       self):
												# prepare image, target and masks_path
												lowercase					=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
												with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''     ,'''r''') as f:
																	lowercase					=		json.loads(f.read())

												lowercase					=		{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

												lowercase					=		pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

												# encode them
												lowercase					=		DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic''')
												lowercase					=		image_processing(images=A__     ,annotations=A__     ,masks_path=A__     ,return_tensors='''pt''')

												# verify pixel values
												lowercase					=		torch.Size([1, 3, 8_0_0, 1_0_6_6])
												self.assertEqual(encoding['''pixel_values'''].shape     ,A__)

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

												# verify area
												lowercase					=		torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,A__))
												# verify boxes
												lowercase					=		torch.Size([6, 4])
												self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,A__)
												lowercase					=		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,A__     ,atol=1E-3))
												# verify image_id
												lowercase					=		torch.tensor([3_9_7_6_9])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,A__))
												# verify is_crowd
												lowercase					=		torch.tensor([0, 0, 0, 0, 0, 0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,A__))
												# verify class_labels
												lowercase					=		torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,A__))
												# verify masks
												lowercase					=		8_2_2_8_7_3
												self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()     ,A__)
												# verify orig_size
												lowercase					=		torch.tensor([4_8_0, 6_4_0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,A__))
												# verify size
												lowercase					=		torch.tensor([8_0_0, 1_0_6_6])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,A__))






from __future__ import annotations



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) < 2:
										raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''	)
					if any(i <= 0 for i in nums	):
										raise ValueError('''All values must be greater than 0'''	)
					lowercase					=		nums.copy()
					copy_nums.sort()
					return copy_nums[-1] < sum(copy_nums[:-1]	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




