
'''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 DetrImageProcessor

class 						a   (				unittest.TestCase    ):




				def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				str=7,    SCREAMING_SNAKE_CASE_	:				List[str]=3,    SCREAMING_SNAKE_CASE_	:				Optional[int]=30,    SCREAMING_SNAKE_CASE_	:				str=4_00,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=True,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=None,    SCREAMING_SNAKE_CASE_	:				Any=True,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=1 / 2_55,    SCREAMING_SNAKE_CASE_	:				str=True,    SCREAMING_SNAKE_CASE_	:				str=[0.5, 0.5, 0.5],    SCREAMING_SNAKE_CASE_	:				str=[0.5, 0.5, 0.5],    SCREAMING_SNAKE_CASE_	:				Optional[int]=True,    ):
										# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
										snake_case       :     Optional[Any] =    size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
										snake_case       :     int =    parent
										snake_case       :     int =    batch_size
										snake_case       :     Optional[Any] =    num_channels
										snake_case       :     Union[str, Any] =    min_resolution
										snake_case       :     str =    max_resolution
										snake_case       :     Dict =    do_resize
										snake_case       :     Any =    size
										snake_case       :     List[str] =    do_rescale
										snake_case       :     Optional[int] =    rescale_factor
										snake_case       :     Optional[int] =    do_normalize
										snake_case       :     Any =    image_mean
										snake_case       :     Dict =    image_std
										snake_case       :     List[Any] =    do_pad




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

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

										return expected_height, expected_width




@require_torch
@require_vision
class 						a   (				__magic_name__				,unittest.TestCase    ):
				_snake_case		    = DetrImageProcessor if is_vision_available() else None




				def 	__snake_case							(							self	:				int      ):
										snake_case       :     Optional[int] =    DetrImageProcessingTester(self      )




				@property
				def 	__snake_case							(							self	:				Union[str, Any]      ):
										return self.image_processor_tester.prepare_image_processor_dict()




				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     List[str] =    self.image_processing_class(**self.image_processor_dict      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    '''image_mean'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    '''image_std'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    '''do_normalize'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    '''do_rescale'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    '''rescale_factor'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    '''do_resize'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    '''size'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    '''do_pad'''      )      )




				def 	__snake_case							(							self	:				int      ):
										snake_case       :     Any =    self.image_processing_class.from_dict(self.image_processor_dict      )
										self.assertEqual(image_processor.size,    {'''shortest_edge''': 18, '''longest_edge''': 13_33}      )
										self.assertEqual(image_processor.do_pad,    SCREAMING_SNAKE_CASE_      )

										snake_case       :     Union[str, Any] =    self.image_processing_class.from_dict(
										    self.image_processor_dict,    size=42,    max_size=84,    pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE_      )
										self.assertEqual(image_processor.size,    {'''shortest_edge''': 42, '''longest_edge''': 84}      )
										self.assertEqual(image_processor.do_pad,    SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[int]      ):
										pass




				def 	__snake_case							(							self	:				Dict      ):
										# Initialize image_processing
										snake_case       :     List[Any] =    self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										snake_case       :     int =    prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_      )
										for image in image_inputs:
																self.assertIsInstance(SCREAMING_SNAKE_CASE_,    Image.Image      )

										# Test not batched input
										snake_case       :     Optional[Any] =    image_processing(image_inputs[0],    return_tensors='''pt'''      ).pixel_values

										snake_case,					snake_case       :     int =    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_      )

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

										# Test batched
										snake_case,					snake_case       :     List[str] =    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_      )

										snake_case       :     str =    image_processing(SCREAMING_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 	__snake_case							(							self	:				List[str]      ):
										# Initialize image_processing
										snake_case       :     Tuple =    self.image_processing_class(**self.image_processor_dict      )
										# create random numpy tensors
										snake_case       :     Any =    prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_,    numpify=SCREAMING_SNAKE_CASE_      )
										for image in image_inputs:
																self.assertIsInstance(SCREAMING_SNAKE_CASE_,    np.ndarray      )

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

										snake_case,					snake_case       :     Dict =    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_      )

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

										# Test batched
										snake_case       :     List[str] =    image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='''pt'''      ).pixel_values

										snake_case,					snake_case       :     Dict =    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_      )

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




				def 	__snake_case							(							self	:				Dict      ):
										# Initialize image_processing
										snake_case       :     List[Any] =    self.image_processing_class(**self.image_processor_dict      )
										# create random PyTorch tensors
										snake_case       :     str =    prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_,    torchify=SCREAMING_SNAKE_CASE_      )
										for image in image_inputs:
																self.assertIsInstance(SCREAMING_SNAKE_CASE_,    torch.Tensor      )

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

										snake_case,					snake_case       :     Optional[Any] =    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_      )

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

										# Test batched
										snake_case       :     List[str] =    image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='''pt'''      ).pixel_values

										snake_case,					snake_case       :     int =    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_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 	__snake_case							(							self	:				Any      ):
										# prepare image and target
										snake_case       :     List[str] =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
										with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''',    '''r'''      ) as f:
																snake_case       :     Union[str, Any] =    json.loads(f.read()      )

										snake_case       :     List[str] =    {'''image_id''': 3_97_69, '''annotations''': target}

										# encode them
										snake_case       :     Tuple =    DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''      )
										snake_case       :     int =    image_processing(images=SCREAMING_SNAKE_CASE_,    annotations=SCREAMING_SNAKE_CASE_,    return_tensors='''pt'''      )

										# verify pixel values
										snake_case       :     Dict =    torch.Size([1, 3, 8_00, 10_66]      )
										self.assertEqual(encoding['''pixel_values'''].shape,    SCREAMING_SNAKE_CASE_      )

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

										# verify area
										snake_case       :     List[Any] =    torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''],    SCREAMING_SNAKE_CASE_      )      )
										# verify boxes
										snake_case       :     Union[str, Any] =    torch.Size([6, 4]      )
										self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,    SCREAMING_SNAKE_CASE_      )
										snake_case       :     Any =    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],    SCREAMING_SNAKE_CASE_,    atol=1e-3      )      )
										# verify image_id
										snake_case       :     str =    torch.tensor([3_97_69]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],    SCREAMING_SNAKE_CASE_      )      )
										# verify is_crowd
										snake_case       :     List[Any] =    torch.tensor([0, 0, 0, 0, 0, 0]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],    SCREAMING_SNAKE_CASE_      )      )
										# verify class_labels
										snake_case       :     Any =    torch.tensor([75, 75, 63, 65, 17, 17]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],    SCREAMING_SNAKE_CASE_      )      )
										# verify orig_size
										snake_case       :     Union[str, Any] =    torch.tensor([4_80, 6_40]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],    SCREAMING_SNAKE_CASE_      )      )
										# verify size
										snake_case       :     Tuple =    torch.tensor([8_00, 10_66]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],    SCREAMING_SNAKE_CASE_      )      )




				@slow
				def 	__snake_case							(							self	:				Union[str, Any]      ):
										# prepare image, target and masks_path
										snake_case       :     Union[str, Any] =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
										with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''',    '''r'''      ) as f:
																snake_case       :     str =    json.loads(f.read()      )

										snake_case       :     Optional[Any] =    {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

										snake_case       :     Optional[Any] =    pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

										# encode them
										snake_case       :     Tuple =    DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''      )
										snake_case       :     List[str] =    image_processing(images=SCREAMING_SNAKE_CASE_,    annotations=SCREAMING_SNAKE_CASE_,    masks_path=SCREAMING_SNAKE_CASE_,    return_tensors='''pt'''      )

										# verify pixel values
										snake_case       :     str =    torch.Size([1, 3, 8_00, 10_66]      )
										self.assertEqual(encoding['''pixel_values'''].shape,    SCREAMING_SNAKE_CASE_      )

										snake_case       :     List[Any] =    torch.tensor([0.2796, 0.3138, 0.3481]      )
										self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3],    SCREAMING_SNAKE_CASE_,    atol=1e-4      )      )

										# verify area
										snake_case       :     List[Any] =    torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''],    SCREAMING_SNAKE_CASE_      )      )
										# verify boxes
										snake_case       :     List[Any] =    torch.Size([6, 4]      )
										self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,    SCREAMING_SNAKE_CASE_      )
										snake_case       :     Any =    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],    SCREAMING_SNAKE_CASE_,    atol=1e-3      )      )
										# verify image_id
										snake_case       :     List[Any] =    torch.tensor([3_97_69]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],    SCREAMING_SNAKE_CASE_      )      )
										# verify is_crowd
										snake_case       :     Any =    torch.tensor([0, 0, 0, 0, 0, 0]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],    SCREAMING_SNAKE_CASE_      )      )
										# verify class_labels
										snake_case       :     Union[str, Any] =    torch.tensor([17, 17, 63, 75, 75, 93]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],    SCREAMING_SNAKE_CASE_      )      )
										# verify masks
										snake_case       :     Any =    82_28_73
										self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item(),    SCREAMING_SNAKE_CASE_      )
										# verify orig_size
										snake_case       :     Optional[int] =    torch.tensor([4_80, 6_40]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],    SCREAMING_SNAKE_CASE_      )      )
										# verify size
										snake_case       :     Dict =    torch.tensor([8_00, 10_66]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],    SCREAMING_SNAKE_CASE_      )      )



'''simple docstring'''





from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip

