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 					lowercase__						(   unittest.TestCase):


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parent
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							batch_size
												SCREAMING_SNAKE_CASE							:    List[str]							=							num_channels
												SCREAMING_SNAKE_CASE							:    str							=							min_resolution
												SCREAMING_SNAKE_CASE							:    Any							=							max_resolution
												SCREAMING_SNAKE_CASE							:    Any							=							do_resize
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							size
												SCREAMING_SNAKE_CASE							:    List[Any]							=							do_normalize
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							image_mean
												SCREAMING_SNAKE_CASE							:    Tuple							=							image_std
												SCREAMING_SNAKE_CASE							:    List[str]							=							do_rescale
												SCREAMING_SNAKE_CASE							:    Any							=							rescale_factor
												SCREAMING_SNAKE_CASE							:    Any							=							do_pad


						def        __A       (       self	:				str						):





												'''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        __A       (       self	:				List[str]    ,		UpperCamelCase__	:				Dict    ,		UpperCamelCase__	:				List[str]=False						):





												'''simple docstring'''





												if not batched:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							image_inputs[0]
																		if isinstance(UpperCamelCase__    ,		Image.Image						):
																								SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							image.size
																		else:
																								SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Dict							=							image.shape[1], image.shape[2]
																		if w < h:
																								SCREAMING_SNAKE_CASE							:    int							=							int(self.size['''shortest_edge'''] * h / w						)
																								SCREAMING_SNAKE_CASE							:    int							=							self.size['''shortest_edge''']
																		elif w > h:
																								SCREAMING_SNAKE_CASE							:    Dict							=							self.size['''shortest_edge''']
																								SCREAMING_SNAKE_CASE							:    Tuple							=							int(self.size['''shortest_edge'''] * w / h						)
																		else:
																								SCREAMING_SNAKE_CASE							:    str							=							self.size['''shortest_edge''']
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.size['''shortest_edge''']

												else:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							[]
																		for image in image_inputs:
																								SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Tuple							=							self.get_expected_values([image]						)
																								expected_values.append((expected_height, expected_width)						)
																		SCREAMING_SNAKE_CASE							:    str							=							max(UpperCamelCase__    ,		key=lambda UpperCamelCase__						: item[0]						)[0]
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							max(UpperCamelCase__    ,		key=lambda UpperCamelCase__						: item[1]						)[1]

												return expected_height, expected_width






@require_torch
@require_vision
class 					lowercase__						(   UpperCamelCase_	,							unittest.TestCase):
						UpperCamelCase_     =		DetaImageProcessor if is_vision_available() else None


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							DetaImageProcessingTester(self						)


						@property
						def        __A       (       self	:				int						):





												'''simple docstring'''





												return self.image_processor_tester.prepare_image_processor_dict()


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							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__    ,		'''do_rescale'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''do_pad'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''size'''						)						)


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    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__						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												pass


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							self.image_processing_class(**self.image_processor_dict						)
												# create random PIL images
												SCREAMING_SNAKE_CASE							:    Optional[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
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							image_processing(image_inputs[0]    ,		return_tensors='''pt'''						).pixel_values

												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[Any]							=							self.image_processor_tester.get_expected_values(UpperCamelCase__    ,		batched=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							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        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.image_processing_class(**self.image_processor_dict						)
												# create random numpy tensors
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							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
												SCREAMING_SNAKE_CASE							:    Any							=							image_processing(image_inputs[0]    ,		return_tensors='''pt'''						).pixel_values

												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Tuple							=							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
												SCREAMING_SNAKE_CASE							:    int							=							image_processing(UpperCamelCase__    ,		return_tensors='''pt'''						).pixel_values

												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Tuple							=							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        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							self.image_processing_class(**self.image_processor_dict						)
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							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
												SCREAMING_SNAKE_CASE							:    int							=							image_processing(image_inputs[0]    ,		return_tensors='''pt'''						).pixel_values

												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							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
												SCREAMING_SNAKE_CASE							:    Any							=							image_processing(UpperCamelCase__    ,		return_tensors='''pt'''						).pixel_values

												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[Any]							=							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,
												    )    ,		)


						@slow
						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
												with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,		'''r'''						) as f:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							json.loads(f.read()						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							{'''image_id''': 3_9769, '''annotations''': target}

												# encode them
												SCREAMING_SNAKE_CASE							:    int							=							DetaImageProcessor()
												SCREAMING_SNAKE_CASE							:    int							=							image_processing(images=UpperCamelCase__    ,		annotations=UpperCamelCase__    ,		return_tensors='''pt'''						)

												# verify pixel values
												SCREAMING_SNAKE_CASE							:    int							=							torch.Size([1, 3, 800, 1066]						)
												self.assertEqual(encoding['''pixel_values'''].shape    ,		UpperCamelCase__						)

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

												# verify area
												SCREAMING_SNAKE_CASE							:    int							=							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''']    ,		UpperCamelCase__						)						)
												# verify boxes
												SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.Size([6, 4]						)
												self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,		UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,		UpperCamelCase__    ,		atol=1E-3						)						)
												# verify image_id
												SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.tensor([3_9769]						)
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,		UpperCamelCase__						)						)
												# verify is_crowd
												SCREAMING_SNAKE_CASE							:    Dict							=							torch.tensor([0, 0, 0, 0, 0, 0]						)
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,		UpperCamelCase__						)						)
												# verify class_labels
												SCREAMING_SNAKE_CASE							:    str							=							torch.tensor([75, 75, 63, 65, 17, 17]						)
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,		UpperCamelCase__						)						)
												# verify orig_size
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							torch.tensor([480, 640]						)
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,		UpperCamelCase__						)						)
												# verify size
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.tensor([800, 1066]						)
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,		UpperCamelCase__						)						)



						@slow
						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
												with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,		'''r'''						) as f:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							json.loads(f.read()						)

												SCREAMING_SNAKE_CASE							:    Any							=							{'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

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

												# encode them
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							DetaImageProcessor(format='''coco_panoptic'''						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							image_processing(images=UpperCamelCase__    ,		annotations=UpperCamelCase__    ,		masks_path=UpperCamelCase__    ,		return_tensors='''pt'''						)

												# verify pixel values
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.Size([1, 3, 800, 1066]						)
												self.assertEqual(encoding['''pixel_values'''].shape    ,		UpperCamelCase__						)

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

												# verify area
												SCREAMING_SNAKE_CASE							:    List[str]							=							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''']    ,		UpperCamelCase__						)						)
												# verify boxes
												SCREAMING_SNAKE_CASE							:    int							=							torch.Size([6, 4]						)
												self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,		UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,		UpperCamelCase__    ,		atol=1E-3						)						)
												# verify image_id
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							torch.tensor([3_9769]						)
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,		UpperCamelCase__						)						)
												# verify is_crowd
												SCREAMING_SNAKE_CASE							:    Tuple							=							torch.tensor([0, 0, 0, 0, 0, 0]						)
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,		UpperCamelCase__						)						)
												# verify class_labels
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.tensor([17, 17, 63, 75, 75, 93]						)
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,		UpperCamelCase__						)						)
												# verify masks
												SCREAMING_SNAKE_CASE							:    int							=							82_2873
												self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,		UpperCamelCase__						)
												# verify orig_size
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.tensor([480, 640]						)
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,		UpperCamelCase__						)						)
												# verify size
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.tensor([800, 1066]						)
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,		UpperCamelCase__						)						)






import math
def       A	(    _lowercase						):
						return math.sqrt(_lowercase						) * math.sqrt(_lowercase						) == num
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Dict							=							0
						SCREAMING_SNAKE_CASE							:    Tuple							=							n
						while left <= right:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(left + right) // 2
												if mid**2 == n:
																		return True
												elif mid**2 > n:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							mid - 1
												else:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							mid + 1
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()





