





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   if "cls_token" in name:
      lowerCamelCase__       :						Any        =							name.replace('''cls_token'''      ,  '''vit.embeddings.cls_token'''					)
   if "mask_token" in name:
      lowerCamelCase__       :						Dict        =							name.replace('''mask_token'''      ,  '''decoder.mask_token'''					)
   if "decoder_pos_embed" in name:
      lowerCamelCase__       :						Any        =							name.replace('''decoder_pos_embed'''      ,  '''decoder.decoder_pos_embed'''					)
   if "pos_embed" in name and "decoder" not in name:
      lowerCamelCase__       :						int        =							name.replace('''pos_embed'''      ,  '''vit.embeddings.position_embeddings'''					)
   if "patch_embed.proj" in name:
      lowerCamelCase__       :						Optional[Any]        =							name.replace('''patch_embed.proj'''      ,  '''vit.embeddings.patch_embeddings.projection'''					)
   if "patch_embed.norm" in name:
      lowerCamelCase__       :						Optional[Any]        =							name.replace('''patch_embed.norm'''      ,  '''vit.embeddings.norm'''					)
   if "decoder_blocks" in name:
      lowerCamelCase__       :						Dict        =							name.replace('''decoder_blocks'''      ,  '''decoder.decoder_layers'''					)
   if "blocks" in name:
      lowerCamelCase__       :						List[str]        =							name.replace('''blocks'''      ,  '''vit.encoder.layer'''					)
   if "attn.proj" in name:
      lowerCamelCase__       :						List[Any]        =							name.replace('''attn.proj'''      ,  '''attention.output.dense'''					)
   if "attn" in name:
      lowerCamelCase__       :						List[str]        =							name.replace('''attn'''      ,  '''attention.self'''					)
   if "norm1" in name:
      lowerCamelCase__       :						Optional[Any]        =							name.replace('''norm1'''      ,  '''layernorm_before'''					)
   if "norm2" in name:
      lowerCamelCase__       :						Tuple        =							name.replace('''norm2'''      ,  '''layernorm_after'''					)
   if "mlp.fc1" in name:
      lowerCamelCase__       :						Union[str, Any]        =							name.replace('''mlp.fc1'''      ,  '''intermediate.dense'''					)
   if "mlp.fc2" in name:
      lowerCamelCase__       :						Tuple        =							name.replace('''mlp.fc2'''      ,  '''output.dense'''					)
   if "decoder_embed" in name:
      lowerCamelCase__       :						Any        =							name.replace('''decoder_embed'''      ,  '''decoder.decoder_embed'''					)
   if "decoder_norm" in name:
      lowerCamelCase__       :						int        =							name.replace('''decoder_norm'''      ,  '''decoder.decoder_norm'''					)
   if "decoder_pred" in name:
      lowerCamelCase__       :						Optional[Any]        =							name.replace('''decoder_pred'''      ,  '''decoder.decoder_pred'''					)
   if "norm.weight" in name and "decoder" not in name:
      lowerCamelCase__       :						List[str]        =							name.replace('''norm.weight'''      ,  '''vit.layernorm.weight'''					)
   if "norm.bias" in name and "decoder" not in name:
      lowerCamelCase__       :						Optional[Any]        =							name.replace('''norm.bias'''      ,  '''vit.layernorm.bias'''					)

   return name



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[str]:







   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      lowerCamelCase__       :						Tuple        =							orig_state_dict.pop(UpperCAmelCase					)

      if "qkv" in key:
         lowerCamelCase__       :						Optional[int]        =							key.split('''.'''					)
         lowerCamelCase__       :						Any        =							int(key_split[1]					)
         if "decoder_blocks" in key:
            lowerCamelCase__       :						Optional[int]        =							config.decoder_hidden_size
            lowerCamelCase__       :						str        =							'''decoder.decoder_layers.'''
            if "weight" in key:
               lowerCamelCase__       :						int        =							val[:dim, :]
               lowerCamelCase__       :						str        =							val[dim : dim * 2, :]
               lowerCamelCase__       :						Tuple        =							val[-dim:, :]
            elif "bias" in key:
               lowerCamelCase__       :						Tuple        =							val[:dim]
               lowerCamelCase__       :						Any        =							val[dim : dim * 2]
               lowerCamelCase__       :						Optional[Any]        =							val[-dim:]
         else:
            lowerCamelCase__       :						str        =							config.hidden_size
            lowerCamelCase__       :						Dict        =							'''vit.encoder.layer.'''
            if "weight" in key:
               lowerCamelCase__       :						int        =							val[:dim, :]
               lowerCamelCase__       :						Any        =							val[dim : dim * 2, :]
               lowerCamelCase__       :						Any        =							val[-dim:, :]
            elif "bias" in key:
               lowerCamelCase__       :						Any        =							val[:dim]
               lowerCamelCase__       :						List[str]        =							val[dim : dim * 2]
               lowerCamelCase__       :						List[str]        =							val[-dim:]

      else:
         lowerCamelCase__       :						Dict        =							val

   return orig_state_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							ViTMAEConfig()
   if "large" in checkpoint_url:
      lowerCamelCase__       :						Dict        =							1024
      lowerCamelCase__       :						int        =							4096
      lowerCamelCase__       :						Optional[Any]        =							24
      lowerCamelCase__       :						Dict        =							16
   elif "huge" in checkpoint_url:
      lowerCamelCase__       :						Optional[Any]        =							14
      lowerCamelCase__       :						Optional[int]        =							1280
      lowerCamelCase__       :						Any        =							5120
      lowerCamelCase__       :						Optional[Any]        =							32
      lowerCamelCase__       :						Any        =							16

   lowerCamelCase__       :						List[Any]        =							ViTMAEForPreTraining(UpperCAmelCase					)

   lowerCamelCase__       :						int        =							torch.hub.load_state_dict_from_url(UpperCAmelCase      ,  map_location='''cpu'''					)['''model''']

   lowerCamelCase__       :						List[str]        =							ViTMAEImageProcessor(size=config.image_size					)

   lowerCamelCase__       :						Optional[int]        =							convert_state_dict(UpperCAmelCase      ,  UpperCAmelCase					)

   model.load_state_dict(UpperCAmelCase					)
   model.eval()

   lowerCamelCase__       :						int        =							'''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

   lowerCamelCase__       :						Any        =							Image.open(requests.get(UpperCAmelCase      ,  stream=UpperCAmelCase					).raw					)
   lowerCamelCase__       :						int        =							ViTMAEImageProcessor(size=config.image_size					)
   lowerCamelCase__       :						Optional[int]        =							image_processor(images=UpperCAmelCase      ,  return_tensors='''pt'''					)

   # forward pass
   torch.manual_seed(2					)
   lowerCamelCase__       :						str        =							model(**UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							outputs.logits

   if "large" in checkpoint_url:
      lowerCamelCase__       :						Union[str, Any]        =							torch.tensor(
          [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]					)
   elif "huge" in checkpoint_url:
      lowerCamelCase__       :						List[str]        =							torch.tensor(
          [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]					)
   else:
      lowerCamelCase__       :						Union[str, Any]        =							torch.tensor(
          [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]					)

   # verify logits
   assert torch.allclose(logits[0, :3, :3]      ,  UpperCAmelCase      ,  atol=1E-4					)

   print(f"Saving model to {pytorch_dump_folder_path}"					)
   model.save_pretrained(UpperCAmelCase					)

   print(f"Saving image processor to {pytorch_dump_folder_path}"					)
   image_processor.save_pretrained(UpperCAmelCase					)


if __name__ == "__main__":
       _A :   Dict  			=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--checkpoint_url',
           default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
           type=str,
           help='URL of the checkpoint you\'d like to convert.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )

       _A :   Dict  			=  parser.parse_args()
       convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










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        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def __init__(				self					:     Optional[int]		, A					:     Union[str, Any]		, A					:     Any=7		, A					:     Optional[int]=3		, A					:     Tuple=3_0		, A					:     List[Any]=4_0_0		, A					:     str=True		, A					:     Optional[int]=None		, A					:     Tuple=True		, A					:     Union[str, Any]=1 / 2_5_5		, A					:     Any=True		, A					:     Optional[int]=[0.5, 0.5, 0.5]		, A					:     Optional[int]=[0.5, 0.5, 0.5]		, A					:     str=True		, )       ->List[Any]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          lowerCamelCase__       :						Union[str, Any]        =							size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
          lowerCamelCase__       :						List[str]        =							parent
          lowerCamelCase__       :						Union[str, Any]        =							batch_size
          lowerCamelCase__       :						int        =							num_channels
          lowerCamelCase__       :						Optional[Any]        =							min_resolution
          lowerCamelCase__       :						List[Any]        =							max_resolution
          lowerCamelCase__       :						int        =							do_resize
          lowerCamelCase__       :						List[str]        =							size
          lowerCamelCase__       :						Union[str, Any]        =							do_rescale
          lowerCamelCase__       :						Optional[int]        =							rescale_factor
          lowerCamelCase__       :						List[str]        =							do_normalize
          lowerCamelCase__       :						Tuple        =							image_mean
          lowerCamelCase__       :						str        =							image_std
          lowerCamelCase__       :						List[Any]        =							do_pad







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

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

          return expected_height, expected_width


@require_torch
@require_vision
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[Any]			   =   DetrImageProcessor if is_vision_available() else None







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Dict:
          lowerCamelCase__       :						Optional[int]        =							DetrImageProcessingTester(self      )







       @property
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Any:
          return self.image_processor_tester.prepare_image_processor_dict()







       def 						__lowerCamelCase   (				self					:     int      )       ->Union[str, Any]:
          lowerCamelCase__       :						Optional[Any]        =							self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(A		, '''image_mean'''      )      )
          self.assertTrue(hasattr(A		, '''image_std'''      )      )
          self.assertTrue(hasattr(A		, '''do_normalize'''      )      )
          self.assertTrue(hasattr(A		, '''do_rescale'''      )      )
          self.assertTrue(hasattr(A		, '''rescale_factor'''      )      )
          self.assertTrue(hasattr(A		, '''do_resize'''      )      )
          self.assertTrue(hasattr(A		, '''size'''      )      )
          self.assertTrue(hasattr(A		, '''do_pad'''      )      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Union[str, Any]:
          lowerCamelCase__       :						List[Any]        =							self.image_processing_class.from_dict(self.image_processor_dict      )
          self.assertEqual(image_processor.size		, {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}      )
          self.assertEqual(image_processor.do_pad		, A      )

          lowerCamelCase__       :						str        =							self.image_processing_class.from_dict(
              self.image_processor_dict		, size=4_2		, max_size=8_4		, pad_and_return_pixel_mask=A      )
          self.assertEqual(image_processor.size		, {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}      )
          self.assertEqual(image_processor.do_pad		, A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[Any]:
          pass







       def 						__lowerCamelCase   (				self					:     str      )       ->Optional[Any]:
          # Initialize image_processing
          lowerCamelCase__       :						Any        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          lowerCamelCase__       :						str        =							prepare_image_inputs(self.image_processor_tester		, equal_resolution=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, Image.Image      )

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

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processor_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.image_processor_tester.get_expected_values(A		, batched=A      )

          lowerCamelCase__       :						Any        =							image_processing(A		, 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					:     List[str]      )       ->List[str]:
          # Initialize image_processing
          lowerCamelCase__       :						Any        =							self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          lowerCamelCase__       :						int        =							prepare_image_inputs(self.image_processor_tester		, equal_resolution=A		, numpify=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, np.ndarray      )

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

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processor_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       :						List[Any]        =							image_processing(A		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						Optional[Any]        =							self.image_processor_tester.get_expected_values(A		, batched=A      )

          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      )       ->Any:
          # Initialize image_processing
          lowerCamelCase__       :						Optional[Any]        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          lowerCamelCase__       :						List[str]        =							prepare_image_inputs(self.image_processor_tester		, equal_resolution=A		, torchify=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, torch.Tensor      )

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

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processor_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       :						str        =							image_processing(A		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processor_tester.get_expected_values(A		, batched=A      )

          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					:     Tuple      )       ->List[Any]:
          # prepare image and target
          lowerCamelCase__       :						Union[str, Any]        =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
          with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		, '''r'''      ) as f:
             lowerCamelCase__       :						Union[str, Any]        =							json.loads(f.read()      )

          lowerCamelCase__       :						List[str]        =							{'''image_id''': 3_9_7_6_9, '''annotations''': target}

          # encode them
          lowerCamelCase__       :						Optional[int]        =							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''      )
          lowerCamelCase__       :						List[Any]        =							image_processing(images=A		, annotations=A		, return_tensors='''pt'''      )

          # verify pixel values
          lowerCamelCase__       :						Optional[int]        =							torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
          self.assertEqual(encoding['''pixel_values'''].shape		, A      )

          lowerCamelCase__       :						Any        =							torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		, A		, atol=1e-4      )      )

          # verify area
          lowerCamelCase__       :						Dict        =							torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		, A      )      )
          # verify boxes
          lowerCamelCase__       :						Any        =							torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, A      )
          lowerCamelCase__       :						str        =							torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		, A		, atol=1e-3      )      )
          # verify image_id
          lowerCamelCase__       :						str        =							torch.tensor([3_9_7_6_9]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, A      )      )
          # verify is_crowd
          lowerCamelCase__       :						List[str]        =							torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, A      )      )
          # verify class_labels
          lowerCamelCase__       :						List[Any]        =							torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		, A      )      )
          # verify orig_size
          lowerCamelCase__       :						str        =							torch.tensor([4_8_0, 6_4_0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, A      )      )
          # verify size
          lowerCamelCase__       :						str        =							torch.tensor([8_0_0, 1_0_6_6]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, A      )      )







       @slow
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->List[str]:
          # prepare image, target and masks_path
          lowerCamelCase__       :						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__       :						Optional[Any]        =							json.loads(f.read()      )

          lowerCamelCase__       :						Union[str, Any]        =							{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

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

          # encode them
          lowerCamelCase__       :						Any        =							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''      )
          lowerCamelCase__       :						Tuple        =							image_processing(images=A		, annotations=A		, masks_path=A		, return_tensors='''pt'''      )

          # verify pixel values
          lowerCamelCase__       :						Optional[Any]        =							torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
          self.assertEqual(encoding['''pixel_values'''].shape		, A      )

          lowerCamelCase__       :						str        =							torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		, A		, atol=1e-4      )      )

          # verify area
          lowerCamelCase__       :						int        =							torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		, A      )      )
          # verify boxes
          lowerCamelCase__       :						Optional[Any]        =							torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, A      )
          lowerCamelCase__       :						Optional[Any]        =							torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		, A		, atol=1e-3      )      )
          # verify image_id
          lowerCamelCase__       :						int        =							torch.tensor([3_9_7_6_9]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, A      )      )
          # verify is_crowd
          lowerCamelCase__       :						Dict        =							torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, A      )      )
          # verify class_labels
          lowerCamelCase__       :						int        =							torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		, A      )      )
          # verify masks
          lowerCamelCase__       :						Union[str, Any]        =							8_2_2_8_7_3
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		, A      )
          # verify orig_size
          lowerCamelCase__       :						List[Any]        =							torch.tensor([4_8_0, 6_4_0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, A      )      )
          # verify size
          lowerCamelCase__       :						Tuple        =							torch.tensor([8_0_0, 1_0_6_6]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, A      )      )



