
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=KandinskyVaaInpaintPipeline
							lowercase_       :       Union[str, Any]       					=['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
							lowercase_       :       Optional[int]       					=[
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							    '''mask_image''',
							]
							lowercase_       :       Union[str, Any]       					=[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_       :       Tuple       					=False




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return self.time_input_dim




							@property
							def  A__      (				self):
												return self.time_input_dim * 4




							@property
							def  A__      (				self):
												return 1_0_0




							@property
							def  A__      (				self):
												torch.manual_seed(0)

												lowercase							=						{
												    '''in_channels''': 9,
												    # Out channels is double in channels because predicts mean and variance
												    '''out_channels''': 8,
												    '''addition_embed_type''': '''image''',
												    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
												    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
												    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
												    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
												    '''layers_per_block''': 1,
												    '''encoder_hid_dim''': self.text_embedder_hidden_size,
												    '''encoder_hid_dim_type''': '''image_proj''',
												    '''cross_attention_dim''': self.cross_attention_dim,
												    '''attention_head_dim''': 4,
												    '''resnet_time_scale_shift''': '''scale_shift''',
												    '''class_embed_type''': None,
												}

												lowercase							=						UNetaDConditionModel(**A__)
												return model




							@property
							def  A__      (				self):
												return {
												    "block_out_channels": [3_2, 6_4],
												    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
												    "in_channels": 3,
												    "latent_channels": 4,
												    "layers_per_block": 1,
												    "norm_num_groups": 8,
												    "norm_type": "spatial",
												    "num_vq_embeddings": 1_2,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						VQModel(**self.dummy_movq_kwargs)
												return model




							def  A__      (				self):
												lowercase							=						self.dummy_unet
												lowercase							=						self.dummy_movq

												lowercase							=						DDIMScheduler(
												    num_train_timesteps=1_0_0_0   ,beta_schedule='''linear'''   ,beta_start=0.00085   ,beta_end=0.012   ,clip_sample=A__   ,set_alpha_to_one=A__   ,steps_offset=1   ,prediction_type='''epsilon'''   ,thresholding=A__   ,)

												lowercase							=						{
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}

												return components




							def  A__      (				self   ,A__   ,A__=0):
												lowercase							=						floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(seed + 1)).to(
												    A__)
												# create init_image
												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						image.cpu().permute(0   ,2   ,3   ,1)[0]
												lowercase							=						Image.fromarray(np.uinta(A__)).convert('''RGB''').resize((2_5_6, 2_5_6))
												# create mask
												lowercase							=						np.ones((6_4, 6_4)   ,dtype=np.floataa)
												lowercase							=						0

												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)
												lowercase							=						{
												    '''image''': init_image,
												    '''mask_image''': mask,
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 4.0,
												    '''output_type''': '''np''',
												}
												return inputs




							def  A__      (				self):
												lowercase							=						'''cpu'''

												lowercase							=						self.get_dummy_components()

												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)

												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						pipe(**self.get_dummy_inputs(A__))
												lowercase							=						output.images

												lowercase							=						pipe(
												    **self.get_dummy_inputs(A__)   ,return_dict=A__   ,)[0]

												lowercase							=						image[0, -3:, -3:, -1]
												lowercase							=						image_from_tuple[0, -3:, -3:, -1]

												print(f'image.shape {image.shape}')

												assert image.shape == (1, 6_4, 6_4, 3)

												lowercase							=						np.array(
												    [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848])

												assert (
												    np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
												), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
												assert (
												    np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
												), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'





							def  A__      (				self):
												super().test_inference_batch_single_identical(expected_max_diff=3E-3)







@slow
@require_torch_gpu
class 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





							def  A__      (				self):
												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy''')

												lowercase							=						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''')
												lowercase							=						np.ones((7_6_8, 7_6_8)   ,dtype=np.floataa)
												lowercase							=						0

												lowercase							=						'''a hat'''

												lowercase							=						KandinskyVaaPriorPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-2-prior'''   ,torch_dtype=torch.floataa)
												pipe_prior.to(A__)

												lowercase							=						KandinskyVaaInpaintPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''   ,torch_dtype=torch.floataa)
												lowercase							=						pipeline.to(A__)
												pipeline.set_progress_bar_config(disable=A__)

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)
												lowercase	,						lowercase							=						pipe_prior(
												    A__   ,generator=A__   ,num_inference_steps=5   ,negative_prompt=''''''   ,).to_tuple()

												lowercase							=						pipeline(
												    image=A__   ,mask_image=A__   ,image_embeds=A__   ,negative_image_embeds=A__   ,generator=A__   ,num_inference_steps=1_0_0   ,height=7_6_8   ,width=7_6_8   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (7_6_8, 7_6_8, 3)

												assert_mean_pixel_difference(A__   ,A__)





"""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 YolosImageProcessor


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=3    ,			_UpperCAmelCase=30    ,			_UpperCAmelCase=400    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=[0.5, 0.5, 0.5]    ,			_UpperCAmelCase=[0.5, 0.5, 0.5]    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=1 / 255    ,			_UpperCAmelCase=True    ,			):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       lowercase__:   str					=      size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
       lowercase__:   Optional[Any]					=      parent
       lowercase__:   List[Any]					=      batch_size
       lowercase__:   Tuple					=      num_channels
       lowercase__:   Optional[Any]					=      min_resolution
       lowercase__:   Dict					=      max_resolution
       lowercase__:   Optional[int]					=      do_resize
       lowercase__:   Any					=      size
       lowercase__:   Optional[Any]					=      do_normalize
       lowercase__:   Union[str, Any]					=      image_mean
       lowercase__:   Tuple					=      image_std
       lowercase__:   str					=      do_rescale
       lowercase__:   Any					=      rescale_factor
       lowercase__:   List[Any]					=      do_pad






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

       else:
          lowercase__:   Optional[int]					=      []
          for image in image_inputs:
             lowercase__, lowercase__:   int					=      self.get_expected_values([image]							)
             expected_values.append((expected_height, expected_width)							)
          lowercase__:   Union[str, Any]					=      max(_UpperCAmelCase    ,			key=lambda _UpperCAmelCase							: item[0]							)[0]
          lowercase__:   Dict					=      max(_UpperCAmelCase    ,			key=lambda _UpperCAmelCase							: item[1]							)[1]

       return expected_height, expected_width



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



    """simple docstring"""

    _UpperCAmelCase	:Optional[int]						    = YolosImageProcessor if is_vision_available() else None






    def _snake_case			(   self							):
       lowercase__:   int					=      YolosImageProcessingTester(self							)






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






    def _snake_case			(   self							):
       lowercase__:   List[str]					=      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    ,			'''size'''							)							)






    def _snake_case			(   self							):
       lowercase__:   Any					=      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							)

       lowercase__:   Any					=      self.image_processing_class.from_dict(
           self.image_processor_dict    ,			size=42    ,			max_size=84    ,			pad_and_return_pixel_mask=_UpperCAmelCase							)
       self.assertEqual(image_processor.size    ,			{'''shortest_edge''': 42, '''longest_edge''': 84}							)
       self.assertEqual(image_processor.do_pad    ,			_UpperCAmelCase							)






    def _snake_case			(   self							):
       pass






    def _snake_case			(   self							):
       # Initialize image_processing
       lowercase__:   Union[str, Any]					=      self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       lowercase__:   Dict					=      prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_UpperCAmelCase							)
       for image in image_inputs:
          self.assertIsInstance(_UpperCAmelCase    ,			Image.Image							)

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

       lowercase__, lowercase__:   Optional[int]					=      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
       lowercase__, lowercase__:   Any					=      self.image_processor_tester.get_expected_values(_UpperCAmelCase    ,			batched=_UpperCAmelCase							)

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

       lowercase__, lowercase__:   str					=      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
       lowercase__:   Dict					=      image_processing(_UpperCAmelCase    ,			return_tensors='''pt'''							).pixel_values

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

       lowercase__, lowercase__:   int					=      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
       lowercase__:   List[str]					=      image_processing(_UpperCAmelCase    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   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,
           )    ,			)






    def _snake_case			(   self							):
       # Initialize image_processings
       lowercase__:   Union[str, Any]					=      self.image_processing_class(**self.image_processor_dict							)
       lowercase__:   Optional[Any]					=      self.image_processing_class(do_resize=_UpperCAmelCase    ,			do_normalize=_UpperCAmelCase    ,			do_rescale=_UpperCAmelCase							)
       # create random PyTorch tensors
       lowercase__:   Optional[Any]					=      prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_UpperCAmelCase    ,			torchify=_UpperCAmelCase							)
       for image in image_inputs:
          self.assertIsInstance(_UpperCAmelCase    ,			torch.Tensor							)

       # Test whether the method "pad" and calling the image processor return the same tensors
       lowercase__:   List[str]					=      image_processing_a.pad(_UpperCAmelCase    ,			return_tensors='''pt'''							)
       lowercase__:   Tuple					=      image_processing_a(_UpperCAmelCase    ,			return_tensors='''pt'''							)

       self.assertTrue(
           torch.allclose(encoded_images_with_method['''pixel_values''']    ,			encoded_images['''pixel_values''']    ,			atol=1e-4							)							)






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

       lowercase__:   Dict					=      {'''image_id''': 39769, '''annotations''': target}

       # encode them
       lowercase__:   Dict					=      YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''							)
       lowercase__:   Any					=      image_processing(images=_UpperCAmelCase    ,			annotations=_UpperCAmelCase    ,			return_tensors='''pt'''							)

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

       lowercase__:   Optional[Any]					=      torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,			_UpperCAmelCase    ,			atol=1e-4							)							)

       # verify area
       lowercase__:   Tuple					=      torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,			_UpperCAmelCase							)							)
       # verify boxes
       lowercase__:   str					=      torch.Size([6, 4]							)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,			_UpperCAmelCase							)
       lowercase__:   List[Any]					=      torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,			_UpperCAmelCase    ,			atol=1e-3							)							)
       # verify image_id
       lowercase__:   Optional[int]					=      torch.tensor([39769]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,			_UpperCAmelCase							)							)
       # verify is_crowd
       lowercase__:   Optional[int]					=      torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,			_UpperCAmelCase							)							)
       # verify class_labels
       lowercase__:   Dict					=      torch.tensor([75, 75, 63, 65, 17, 17]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,			_UpperCAmelCase							)							)
       # verify orig_size
       lowercase__:   List[Any]					=      torch.tensor([480, 640]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,			_UpperCAmelCase							)							)
       # verify size
       lowercase__:   List[Any]					=      torch.tensor([800, 1066]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,			_UpperCAmelCase							)							)






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

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

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

       # encode them
       lowercase__:   Union[str, Any]					=      YolosImageProcessor(format='''coco_panoptic'''							)
       lowercase__:   Optional[Any]					=      image_processing(images=_UpperCAmelCase    ,			annotations=_UpperCAmelCase    ,			masks_path=_UpperCAmelCase    ,			return_tensors='''pt'''							)

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

       lowercase__:   Dict					=      torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,			_UpperCAmelCase    ,			atol=1e-4							)							)

       # verify area
       lowercase__:   str					=      torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,			_UpperCAmelCase							)							)
       # verify boxes
       lowercase__:   List[str]					=      torch.Size([6, 4]							)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,			_UpperCAmelCase							)
       lowercase__:   List[Any]					=      torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,			_UpperCAmelCase    ,			atol=1e-3							)							)
       # verify image_id
       lowercase__:   int					=      torch.tensor([39769]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,			_UpperCAmelCase							)							)
       # verify is_crowd
       lowercase__:   int					=      torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,			_UpperCAmelCase							)							)
       # verify class_labels
       lowercase__:   Dict					=      torch.tensor([17, 17, 63, 75, 75, 93]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,			_UpperCAmelCase							)							)
       # verify masks
       lowercase__:   Union[str, Any]					=      822873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,			_UpperCAmelCase							)
       # verify orig_size
       lowercase__:   List[Any]					=      torch.tensor([480, 640]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,			_UpperCAmelCase							)							)
       # verify size
       lowercase__:   Union[str, Any]					=      torch.tensor([800, 1066]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,			_UpperCAmelCase							)							)
