



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 ConditionalDetrImageProcessor


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


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



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				Union[str, 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       lowerCamelCase_				(  self    :				List[str]       ,	snake_case    :				List[str]       ,	snake_case    :				str=False	):



											'''simple docstring'''


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

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

											return expected_height, expected_width


@require_torch
@require_vision
class 						lowercase    (							SCREAMING_SNAKE_CASE_      ,    unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		Optional[Any]		=		ConditionalDetrImageProcessor if is_vision_available() else None







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				ConditionalDetrImageProcessingTester(self	)







						@property
						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											return self.image_processor_tester.prepare_image_processor_dict()







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[str]						=				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       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[str]						=				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	)

											SCREAMING_SNAKE_CASE							:     int						=				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       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											pass







						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.image_processing_class(**self.image_processor_dict	)
											# create random PIL images
											SCREAMING_SNAKE_CASE							:     str						=				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
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				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(snake_case	)

											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(snake_case       ,	batched=snake_case	)

											SCREAMING_SNAKE_CASE							:     str						=				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       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


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

											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				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
											SCREAMING_SNAKE_CASE							:     Any						=				image_processing(snake_case       ,	return_tensors='pt'	).pixel_values

											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[int]						=				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       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


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

											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				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
											SCREAMING_SNAKE_CASE							:     str						=				image_processing(snake_case       ,	return_tensors='pt'	).pixel_values

											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				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,
											    )       ,	)







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



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				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							:     Union[str, Any]						=				json.loads(f.read()	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				{'image_id': 39769, 'annotations': target}

											# encode them
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'	)
											SCREAMING_SNAKE_CASE							:     int						=				image_processing(images=snake_case       ,	annotations=snake_case       ,	return_tensors='pt'	)

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

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				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
											SCREAMING_SNAKE_CASE							:     str						=				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,	snake_case	)	)
											# verify boxes
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.Size([6, 4]	)
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				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
											SCREAMING_SNAKE_CASE							:     Tuple						=				torch.tensor([39769]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,	snake_case	)	)
											# verify is_crowd
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.tensor([0, 0, 0, 0, 0, 0]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,	snake_case	)	)
											# verify class_labels
											SCREAMING_SNAKE_CASE							:     int						=				torch.tensor([75, 75, 63, 65, 17, 17]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,	snake_case	)	)
											# verify orig_size
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.tensor([480, 640]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,	snake_case	)	)
											# verify size
											SCREAMING_SNAKE_CASE							:     Tuple						=				torch.tensor([800, 1066]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,	snake_case	)	)







						@slow
						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				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							:     Tuple						=				json.loads(f.read()	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

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

											# encode them
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				ConditionalDetrImageProcessor(format='coco_panoptic'	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				image_processing(images=snake_case       ,	annotations=snake_case       ,	masks_path=snake_case       ,	return_tensors='pt'	)

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

											SCREAMING_SNAKE_CASE							:     int						=				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
											SCREAMING_SNAKE_CASE							:     str						=				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,	snake_case	)	)
											# verify boxes
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.Size([6, 4]	)
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     Any						=				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
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.tensor([39769]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,	snake_case	)	)
											# verify is_crowd
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.tensor([0, 0, 0, 0, 0, 0]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,	snake_case	)	)
											# verify class_labels
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.tensor([17, 17, 63, 75, 75, 93]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,	snake_case	)	)
											# verify masks
											SCREAMING_SNAKE_CASE							:     str						=				822873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,	snake_case	)
											# verify orig_size
											SCREAMING_SNAKE_CASE							:     str						=				torch.tensor([480, 640]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,	snake_case	)	)
											# verify size
											SCREAMING_SNAKE_CASE							:     Dict						=				torch.tensor([800, 1066]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,	snake_case	)	)




from __future__ import annotations

import numpy as np

def        __a			(   __lowerCAmelCase   )     ->     Optional[Any]:
					return np.maximum(0      ,	__lowerCAmelCase   )


if __name__ == "__main__":
			print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]