






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 DetaImageProcessor
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def __init__(	self		:       Dict,    lowerCamelCase		:       str,    lowerCamelCase		:       Tuple=7,    lowerCamelCase		:       int=3,    lowerCamelCase		:       Any=30,    lowerCamelCase		:       Dict=400,    lowerCamelCase		:       Union[str, Any]=True,    lowerCamelCase		:       int=None,    lowerCamelCase		:       Any=True,    lowerCamelCase		:       Optional[Any]=[0.5, 0.5, 0.5],    lowerCamelCase		:       str=[0.5, 0.5, 0.5],    lowerCamelCase		:       int=True,    lowerCamelCase		:       List[str]=1 / 255,    lowerCamelCase		:       List[str]=True,    ):

								'''simple docstring'''
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								lowercase__  					=     size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
								lowercase__  					=     parent
								lowercase__  					=     batch_size
								lowercase__  					=     num_channels
								lowercase__  					=     min_resolution
								lowercase__  					=     max_resolution
								lowercase__  					=     do_resize
								lowercase__  					=     size
								lowercase__  					=     do_normalize
								lowercase__  					=     image_mean
								lowercase__  					=     image_std
								lowercase__  					=     do_rescale
								lowercase__  					=     rescale_factor
								lowercase__  					=     do_pad




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								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 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       List[str]=False    ):

								'''simple docstring'''
								if not batched:
												lowercase__  					=     image_inputs[0]
												if isinstance(lowerCamelCase,    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(lowerCamelCase,    key=lambda lowerCamelCase    : item[0]    )[0]
												lowercase__  					=     max(lowerCamelCase,    key=lambda lowerCamelCase    : item[1]    )[1]

								return expected_height, expected_width



@require_torch
@require_vision
class  _UpperCAmelCase   (			A__						,unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			DetaImageProcessor if is_vision_available() else None




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     DetaImageProcessingTester(self    )




				@property
				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(lowerCamelCase,    '''image_mean'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''image_std'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''do_normalize'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''do_resize'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''do_rescale'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''do_pad'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''size'''    )    )




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__  					=     self.image_processing_class.from_dict(self.image_processor_dict    )
								self.assertEqual(image_processor.size,    {'''shortest_edge''': 18, '''longest_edge''': 1_333}    )
								self.assertEqual(image_processor.do_pad,    lowerCamelCase    )




				def 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								# 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=lowerCamelCase    )
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,    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(lowerCamelCase    )

								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(lowerCamelCase,    batched=lowerCamelCase    )

								lowercase__  					=     image_processing(lowerCamelCase,    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 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								# 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=lowerCamelCase,    numpify=lowerCamelCase    )
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,    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(lowerCamelCase    )

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

								# Test batched
								lowercase__  					=     image_processing(lowerCamelCase,    return_tensors='''pt'''    ).pixel_values

								lowercase__					,		lowercase__  					=     self.image_processor_tester.get_expected_values(lowerCamelCase,    batched=lowerCamelCase    )

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




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								# 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=lowerCamelCase,    torchify=lowerCamelCase    )
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,    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(lowerCamelCase    )

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

								# Test batched
								lowercase__  					=     image_processing(lowerCamelCase,    return_tensors='''pt'''    ).pixel_values

								lowercase__					,		lowercase__  					=     self.image_processor_tester.get_expected_values(lowerCamelCase,    batched=lowerCamelCase    )

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




				@slow
				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								# 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''': 39_769, '''annotations''': target}

								# encode them
								lowercase__  					=     DetaImageProcessor()
								lowercase__  					=     image_processing(images=lowerCamelCase,    annotations=lowerCamelCase,    return_tensors='''pt'''    )

								# verify pixel values
								lowercase__  					=     torch.Size([1, 3, 800, 1_066]    )
								self.assertEqual(encoding['''pixel_values'''].shape,    lowerCamelCase    )

								lowercase__  					=     torch.tensor([0.2796, 0.3138, 0.3481]    )
								self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3],    lowerCamelCase,    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'''],    lowerCamelCase    )    )
								# verify boxes
								lowercase__  					=     torch.Size([6, 4]    )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,    lowerCamelCase    )
								lowercase__  					=     torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],    lowerCamelCase,    atol=1E-3    )    )
								# verify image_id
								lowercase__  					=     torch.tensor([39_769]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],    lowerCamelCase    )    )
								# verify is_crowd
								lowercase__  					=     torch.tensor([0, 0, 0, 0, 0, 0]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],    lowerCamelCase    )    )
								# verify class_labels
								lowercase__  					=     torch.tensor([75, 75, 63, 65, 17, 17]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],    lowerCamelCase    )    )
								# verify orig_size
								lowercase__  					=     torch.tensor([480, 640]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],    lowerCamelCase    )    )
								# verify size
								lowercase__  					=     torch.tensor([800, 1_066]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],    lowerCamelCase    )    )




				@slow
				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								# 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''': 39_769, '''segments_info''': target}

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

								# encode them
								lowercase__  					=     DetaImageProcessor(format='''coco_panoptic'''    )
								lowercase__  					=     image_processing(images=lowerCamelCase,    annotations=lowerCamelCase,    masks_path=lowerCamelCase,    return_tensors='''pt'''    )

								# verify pixel values
								lowercase__  					=     torch.Size([1, 3, 800, 1_066]    )
								self.assertEqual(encoding['''pixel_values'''].shape,    lowerCamelCase    )

								lowercase__  					=     torch.tensor([0.2796, 0.3138, 0.3481]    )
								self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3],    lowerCamelCase,    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'''],    lowerCamelCase    )    )
								# verify boxes
								lowercase__  					=     torch.Size([6, 4]    )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,    lowerCamelCase    )
								lowercase__  					=     torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],    lowerCamelCase,    atol=1E-3    )    )
								# verify image_id
								lowercase__  					=     torch.tensor([39_769]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],    lowerCamelCase    )    )
								# verify is_crowd
								lowercase__  					=     torch.tensor([0, 0, 0, 0, 0, 0]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],    lowerCamelCase    )    )
								# verify class_labels
								lowercase__  					=     torch.tensor([17, 17, 63, 75, 75, 93]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],    lowerCamelCase    )    )
								# verify masks
								lowercase__  					=     822_873
								self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item(),    lowerCamelCase    )
								# verify orig_size
								lowercase__  					=     torch.tensor([480, 640]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],    lowerCamelCase    )    )
								# verify size
								lowercase__  					=     torch.tensor([800, 1_066]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],    lowerCamelCase    )    )












def     a     (		lowerCamelCase_							):






				'''simple docstring'''

				if p < 2:
								raise ValueError('''p should not be less than 2!'''							)
				elif p == 2:
								return True

				lowercase__  					=     4
				lowercase__  					=     (1 << p) - 1
				for _ in range(p - 2							):
								lowercase__  					=     ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
							print(lucas_lehmer_test(7))
							print(lucas_lehmer_test(11))




