


import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A      :    Optional[int]		=					logging.get_logger(__name__)  # pylint: disable=invalid-name

def       lowercase_		(		_A			:    Union[List, PIL.Image.Image, torch.Tensor]						):

      """simple docstring"""




      warnings.warn(
          "The preprocess method is deprecated and will be removed in a future version. Please"
          " use VaeImageProcessor.preprocess instead"    ,					_A    ,					)
      if isinstance(_A    ,					torch.Tensor						):
            return image
      elif isinstance(_A    ,					PIL.Image.Image						):
            lowerCamelCase__		:				Optional[Any]    =  [image]

      if isinstance(image[0]    ,					PIL.Image.Image						):
            lowerCamelCase__    ,   lowerCamelCase__		:				int    =  image[0].size
            lowerCamelCase__    ,   lowerCamelCase__		:				int    =  (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

            lowerCamelCase__		:				Tuple    =  [np.array(i.resize((w, h)    ,					resample=PIL_INTERPOLATION["lanczos"]						)						)[None, :] for i in image]
            lowerCamelCase__		:				Optional[int]    =  np.concatenate(_A    ,					axis=0						)
            lowerCamelCase__		:				Union[str, Any]    =  np.array(_A						).astype(np.floataa						) / 255.0
            lowerCamelCase__		:				Dict    =  image.transpose(0    ,					3    ,					1    ,					2						)
            lowerCamelCase__		:				List[str]    =  2.0 * image - 1.0
            lowerCamelCase__		:				str    =  torch.from_numpy(_A						)
      elif isinstance(image[0]    ,					torch.Tensor						):
            lowerCamelCase__		:				Dict    =  torch.cat(_A    ,					dim=0						)
      return image

def       lowercase_		(		_A			:    Union[List, PIL.Image.Image, torch.Tensor]						):

      """simple docstring"""




      if isinstance(_A    ,					torch.Tensor						):
            return mask
      elif isinstance(_A    ,					PIL.Image.Image						):
            lowerCamelCase__		:				str    =  [mask]

      if isinstance(mask[0]    ,					PIL.Image.Image						):
            lowerCamelCase__    ,   lowerCamelCase__		:				Optional[int]    =  mask[0].size
            lowerCamelCase__    ,   lowerCamelCase__		:				Dict    =  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
            lowerCamelCase__		:				Optional[int]    =  [np.array(m.convert("L"						).resize((w, h)    ,					resample=PIL_INTERPOLATION["nearest"]						)						)[None, :] for m in mask]
            lowerCamelCase__		:				str    =  np.concatenate(_A    ,					axis=0						)
            lowerCamelCase__		:				Tuple    =  mask.astype(np.floataa						) / 255.0
            lowerCamelCase__		:				int    =  0
            lowerCamelCase__		:				Optional[Any]    =  1
            lowerCamelCase__		:				str    =  torch.from_numpy(_A						)
      elif isinstance(mask[0]    ,					torch.Tensor						):
            lowerCamelCase__		:				Dict    =  torch.cat(_A    ,					dim=0						)
      return mask







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			42
       A__											=			42





       def __init__(   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			List[Any]       ):


             '''simple docstring'''



             super().__init__()
             self.register_modules(unet=__lowerCamelCase					,      scheduler=__lowerCamelCase       )





       @torch.no_grad()
       def __call__(   self							:			Dict					,      __lowerCamelCase							:			Union[torch.Tensor, PIL.Image.Image]					,      __lowerCamelCase							:			Union[torch.Tensor, PIL.Image.Image]					,      __lowerCamelCase							:			int = 250					,      __lowerCamelCase							:			float = 0.0					,      __lowerCamelCase							:			int = 10					,      __lowerCamelCase							:			int = 10					,      __lowerCamelCase							:			Optional[Union[torch.Generator, List[torch.Generator]]] = None					,      __lowerCamelCase							:			Optional[str] = "pil"					,      __lowerCamelCase							:			bool = True					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  image

             lowerCamelCase__		:				str    =  _preprocess_image(__lowerCamelCase       )
             lowerCamelCase__		:				Dict    =  original_image.to(device=self.device					,      dtype=self.unet.dtype       )
             lowerCamelCase__		:				List[Any]    =  _preprocess_mask(__lowerCamelCase       )
             lowerCamelCase__		:				Tuple    =  mask_image.to(device=self.device					,      dtype=self.unet.dtype       )

             lowerCamelCase__		:				str    =  original_image.shape[0]

             # sample gaussian noise to begin the loop
             if isinstance(__lowerCamelCase					,      __lowerCamelCase       ) and len(__lowerCamelCase       ) != batch_size:
                   raise ValueError(
                       f"You have passed a list of generators of length {len(__lowerCamelCase       )}, but requested an effective batch"
                       f" size of {batch_size}. Make sure the batch size matches the length of the generators."       )

             lowerCamelCase__		:				str    =  original_image.shape
             lowerCamelCase__		:				Optional[int]    =  randn_tensor(__lowerCamelCase					,      generator=__lowerCamelCase					,      device=self.device					,      dtype=self.unet.dtype       )

             # set step values
             self.scheduler.set_timesteps(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      self.device       )
             lowerCamelCase__		:				Any    =  eta

             lowerCamelCase__		:				Optional[int]    =  self.scheduler.timesteps[0] + 1
             lowerCamelCase__		:				Optional[Any]    =  generator[0] if isinstance(__lowerCamelCase					,      __lowerCamelCase       ) else generator
             for i, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
                   if t < t_last:
                         # predict the noise residual
                         lowerCamelCase__		:				Dict    =  self.unet(__lowerCamelCase					,      __lowerCamelCase       ).sample
                         # compute previous image: x_t -> x_t-1
                         lowerCamelCase__		:				Dict    =  self.scheduler.step(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       ).prev_sample

                   else:
                         # compute the reverse: x_t-1 -> x_t
                         lowerCamelCase__		:				Optional[int]    =  self.scheduler.undo_step(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )
                   lowerCamelCase__		:				Any    =  t

             lowerCamelCase__		:				str    =  (image / 2 + 0.5).clamp(0					,      1       )
             lowerCamelCase__		:				int    =  image.cpu().permute(0					,      2					,      3					,      1       ).numpy()
             if output_type == "pil":
                   lowerCamelCase__		:				int    =  self.numpy_to_pil(__lowerCamelCase       )

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=__lowerCamelCase       )






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):


       """simple docstring"""




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


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			List[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 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			List[str]=False       ):


             '''simple docstring'''



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

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

             return expected_height, expected_width







@require_torch
@require_vision
class 			_lowercase (							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			DetaImageProcessor if is_vision_available() else None





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  DetaImageProcessingTester(self       )





       @property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  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 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				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					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             lowerCamelCase__		:				List[str]    =  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase       )
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase					,      Image.Image       )

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

             lowerCamelCase__    ,   lowerCamelCase__		:				Optional[Any]    =  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
             lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase       )

             lowerCamelCase__		:				List[Any]    =  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 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



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

             lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  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
             lowerCamelCase__		:				Optional[Any]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Union[str, Any]    =  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 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             lowerCamelCase__		:				Dict    =  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
             lowerCamelCase__		:				Dict    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  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
             lowerCamelCase__		:				Union[str, Any]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Dict    =  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 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
             with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"					,      "r"       ) as f:
                   lowerCamelCase__		:				Dict    =  json.loads(f.read()       )

             lowerCamelCase__		:				Any    =  {"image_id": 39769, "annotations": target}

             # encode them
             lowerCamelCase__		:				Union[str, Any]    =  DetaImageProcessor()
             lowerCamelCase__		:				List[str]    =  image_processing(images=__lowerCamelCase					,      annotations=__lowerCamelCase					,      return_tensors="pt"       )

             # verify pixel values
             lowerCamelCase__		:				List[str]    =  torch.Size([1, 3, 800, 1066]       )
             self.assertEqual(encoding["pixel_values"].shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				Union[str, Any]    =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
             self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,      __lowerCamelCase					,      atol=1E-4       )       )

             # verify area
             lowerCamelCase__		:				Tuple    =  torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,      __lowerCamelCase       )       )
             # verify boxes
             lowerCamelCase__		:				Union[str, Any]    =  torch.Size([6, 4]       )
             self.assertEqual(encoding["labels"][0]["boxes"].shape					,      __lowerCamelCase       )
             lowerCamelCase__		:				List[Any]    =  torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,      __lowerCamelCase					,      atol=1E-3       )       )
             # verify image_id
             lowerCamelCase__		:				Optional[Any]    =  torch.tensor([39769]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,      __lowerCamelCase       )       )
             # verify is_crowd
             lowerCamelCase__		:				Dict    =  torch.tensor([0, 0, 0, 0, 0, 0]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,      __lowerCamelCase       )       )
             # verify class_labels
             lowerCamelCase__		:				int    =  torch.tensor([75, 75, 63, 65, 17, 17]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,      __lowerCamelCase       )       )
             # verify orig_size
             lowerCamelCase__		:				int    =  torch.tensor([480, 640]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,      __lowerCamelCase       )       )
             # verify size
             lowerCamelCase__		:				Optional[int]    =  torch.tensor([800, 1066]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,      __lowerCamelCase       )       )





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


             '''simple docstring'''



             lowerCamelCase__		:				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:
                   lowerCamelCase__		:				Tuple    =  json.loads(f.read()       )

             lowerCamelCase__		:				List[str]    =  {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

             lowerCamelCase__		:				Union[str, Any]    =  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"       )

             # encode them
             lowerCamelCase__		:				Tuple    =  DetaImageProcessor(format="coco_panoptic"       )
             lowerCamelCase__		:				Dict    =  image_processing(images=__lowerCamelCase					,      annotations=__lowerCamelCase					,      masks_path=__lowerCamelCase					,      return_tensors="pt"       )

             # verify pixel values
             lowerCamelCase__		:				List[Any]    =  torch.Size([1, 3, 800, 1066]       )
             self.assertEqual(encoding["pixel_values"].shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				Dict    =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
             self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,      __lowerCamelCase					,      atol=1E-4       )       )

             # verify area
             lowerCamelCase__		:				List[Any]    =  torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,      __lowerCamelCase       )       )
             # verify boxes
             lowerCamelCase__		:				Any    =  torch.Size([6, 4]       )
             self.assertEqual(encoding["labels"][0]["boxes"].shape					,      __lowerCamelCase       )
             lowerCamelCase__		:				int    =  torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,      __lowerCamelCase					,      atol=1E-3       )       )
             # verify image_id
             lowerCamelCase__		:				Optional[int]    =  torch.tensor([39769]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,      __lowerCamelCase       )       )
             # verify is_crowd
             lowerCamelCase__		:				Dict    =  torch.tensor([0, 0, 0, 0, 0, 0]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,      __lowerCamelCase       )       )
             # verify class_labels
             lowerCamelCase__		:				Tuple    =  torch.tensor([17, 17, 63, 75, 75, 93]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,      __lowerCamelCase       )       )
             # verify masks
             lowerCamelCase__		:				Union[str, Any]    =  822873
             self.assertEqual(encoding["labels"][0]["masks"].sum().item()					,      __lowerCamelCase       )
             # verify orig_size
             lowerCamelCase__		:				Optional[Any]    =  torch.tensor([480, 640]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,      __lowerCamelCase       )       )
             # verify size
             lowerCamelCase__		:				Union[str, Any]    =  torch.tensor([800, 1066]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,      __lowerCamelCase       )       )


