






'''simple docstring'''

import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


SCREAMING_SNAKE_CASE__     =       {
    '169M': 1_2,
    '430M': 2_4,
    '1B5': 2_4,
    '3B': 3_2,
    '7B': 3_2,
    '14B': 4_0,
}

SCREAMING_SNAKE_CASE__     =       {
    '169M': 7_6_8,
    '430M': 1_0_2_4,
    '1B5': 2_0_4_8,
    '3B': 2_5_6_0,
    '7B': 4_0_9_6,
    '14B': 5_1_2_0,
}







def    lowercase__			(				__UpperCamelCase   )->			Dict:
      UpperCamelCase      = list(state_dict.keys()   )
      for name in state_dict_keys:
            UpperCamelCase      = state_dict.pop(__UpperCamelCase   )
            # emb -> embedding
            if name.startswith("""emb."""   ):
                  UpperCamelCase      = name.replace("""emb."""		,						"""embeddings."""   )
            # ln_0 -> pre_ln (only present at block 0)
            if name.startswith("""blocks.0.ln0"""   ):
                  UpperCamelCase      = name.replace("""blocks.0.ln0"""		,						"""blocks.0.pre_ln"""   )
            # att -> attention
            UpperCamelCase      = re.sub(R"""blocks\.(\d+)\.att"""		,						R"""blocks.\1.attention"""		,						__UpperCamelCase   )
            # ffn -> feed_forward
            UpperCamelCase      = re.sub(R"""blocks\.(\d+)\.ffn"""		,						R"""blocks.\1.feed_forward"""		,						__UpperCamelCase   )
            # time_mix_k -> time_mix_key and reshape
            if name.endswith(""".time_mix_k"""   ):
                  UpperCamelCase      = name.replace(""".time_mix_k"""		,						""".time_mix_key"""   )
            # time_mix_v -> time_mix_value and reshape
            if name.endswith(""".time_mix_v"""   ):
                  UpperCamelCase      = name.replace(""".time_mix_v"""		,						""".time_mix_value"""   )
            # time_mix_r -> time_mix_key and reshape
            if name.endswith(""".time_mix_r"""   ):
                  UpperCamelCase      = name.replace(""".time_mix_r"""		,						""".time_mix_receptance"""   )

            if name != "head.weight":
                  UpperCamelCase      = """rwkv.""" + name

            UpperCamelCase      = weight
      return state_dict







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None		,						__UpperCamelCase=None		,						__UpperCamelCase=False		,						__UpperCamelCase=None   )->			List[str]:
      # 1. If possible, build the tokenizer.
      if tokenizer_file is None:
            print("""No `--tokenizer_file` provided, we will use the default tokenizer."""   )
            UpperCamelCase      = 50277
            UpperCamelCase      = AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""   )
      else:
            UpperCamelCase      = PreTrainedTokenizerFast(tokenizer_file=__UpperCamelCase   )
            UpperCamelCase      = len(__UpperCamelCase   )
      tokenizer.save_pretrained(__UpperCamelCase   )

      # 2. Build the config
      UpperCamelCase      = list(NUM_HIDDEN_LAYERS_MAPPING.keys()   )
      if size is None:
            # Try to infer size from the checkpoint name
            for candidate in possible_sizes:
                  if candidate in checkpoint_file:
                        UpperCamelCase      = candidate
                        break
            if size is None:
                  raise ValueError("""Could not infer the size, please provide it with the `--size` argument."""   )
      if size not in possible_sizes:
            raise ValueError(F"`size` should be one of {possible_sizes}, got {size}."   )

      UpperCamelCase      = RwkvConfig(
          vocab_size=__UpperCamelCase		,						num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]		,						hidden_size=HIDEN_SIZE_MAPPING[size]		,						)
      config.save_pretrained(__UpperCamelCase   )

      # 3. Download model file then convert state_dict
      UpperCamelCase      = hf_hub_download(__UpperCamelCase		,						__UpperCamelCase   )
      UpperCamelCase      = torch.load(__UpperCamelCase		,						map_location="""cpu"""   )
      UpperCamelCase      = convert_state_dict(__UpperCamelCase   )

      # 4. Split in shards and save
      UpperCamelCase  ,UpperCamelCase      = shard_checkpoint(__UpperCamelCase   )
      for shard_file, shard in shards.items():
            torch.save(__UpperCamelCase		,						os.path.join(__UpperCamelCase		,						__UpperCamelCase   )   )

      if index is not None:
            UpperCamelCase      = os.path.join(__UpperCamelCase		,						__UpperCamelCase   )
            # Save the index as well
            with open(__UpperCamelCase		,						"""w"""		,						encoding="""utf-8"""   ) as f:
                  UpperCamelCase      = json.dumps(__UpperCamelCase		,						indent=2		,						sort_keys=__UpperCamelCase   ) + """\n"""
                  f.write(__UpperCamelCase   )

            # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
            print(
                """Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."""   )
            UpperCamelCase      = list(shards.keys()   )

            del state_dict
            del shards
            gc.collect()

            for shard_file in shard_files:
                  UpperCamelCase      = torch.load(os.path.join(__UpperCamelCase		,						__UpperCamelCase   )   )
                  torch.save({k: v.cpu().clone() for k, v in state_dict.items()}		,						os.path.join(__UpperCamelCase		,						__UpperCamelCase   )   )

      del state_dict
      gc.collect()

      if push_to_hub:
            if model_name is None:
                  raise ValueError("""Please provide a `model_name` to push the model to the Hub."""   )
            UpperCamelCase      = AutoModelForCausalLM.from_pretrained(__UpperCamelCase   )
            model.push_to_hub(__UpperCamelCase		,						max_shard_size="""2GB"""   )
            tokenizer.push_to_hub(__UpperCamelCase   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
     )
     parser.add_argument(
         '--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
     )
     parser.add_argument(
         '--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
     )
     parser.add_argument(
         '--tokenizer_file',
         default=None,
         type=str,
         help='Path to the tokenizer file to use (if not provided, only the model is converted).',
     )
     parser.add_argument(
         '--size',
         default=None,
         type=str,
         help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
     )
     parser.add_argument(
         '--push_to_hub',
         action='store_true',
         help='Push to the Hub the converted model.',
     )
     parser.add_argument(
         '--model_name',
         default=None,
         type=str,
         help='Name of the pushed model on the Hub, including the username / organization.',
     )

     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     convert_rmkv_checkpoint_to_hf_format(
         args.repo_id,
         args.checkpoint_file,
         args.output_dir,
         size=args.size,
         tokenizer_file=args.tokenizer_file,
         push_to_hub=args.push_to_hub,
         model_name=args.model_name,
     )








'''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 ConditionalDetrImageProcessor






class     a_						(      unittest.TestCase    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=7      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=30      ,		_SCREAMING_SNAKE_CASE=400      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=1 / 255      ,		_SCREAMING_SNAKE_CASE=True      ,		)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
        UpperCamelCase      = parent
        UpperCamelCase      = batch_size
        UpperCamelCase      = num_channels
        UpperCamelCase      = min_resolution
        UpperCamelCase      = max_resolution
        UpperCamelCase      = do_resize
        UpperCamelCase      = size
        UpperCamelCase      = do_normalize
        UpperCamelCase      = image_mean
        UpperCamelCase      = image_std
        UpperCamelCase      = do_rescale
        UpperCamelCase      = rescale_factor
        UpperCamelCase      = do_pad

  def   A__						(				self				)  ->				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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=False				)  ->				Optional[int]:



        """simple docstring"""






        if not batched:
              UpperCamelCase      = image_inputs[0]
              if isinstance(_SCREAMING_SNAKE_CASE      ,		Image.Image				):
                    UpperCamelCase  ,UpperCamelCase      = image.size
              else:
                    UpperCamelCase  ,UpperCamelCase      = image.shape[1], image.shape[2]
              if w < h:
                    UpperCamelCase      = int(self.size["""shortest_edge"""] * h / w				)
                    UpperCamelCase      = self.size["""shortest_edge"""]
              elif w > h:
                    UpperCamelCase      = self.size["""shortest_edge"""]
                    UpperCamelCase      = int(self.size["""shortest_edge"""] * w / h				)
              else:
                    UpperCamelCase      = self.size["""shortest_edge"""]
                    UpperCamelCase      = self.size["""shortest_edge"""]

        else:
              UpperCamelCase      = []
              for image in image_inputs:
                    UpperCamelCase  ,UpperCamelCase      = self.get_expected_values([image]				)
                    expected_values.append((expected_height, expected_width)				)
              UpperCamelCase      = max(_SCREAMING_SNAKE_CASE      ,		key=lambda _SCREAMING_SNAKE_CASE				: item[0]				)[0]
              UpperCamelCase      = max(_SCREAMING_SNAKE_CASE      ,		key=lambda _SCREAMING_SNAKE_CASE				: item[1]				)[1]

        return expected_height, expected_width






@require_torch
@require_vision
class     a_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				ConditionalDetrImageProcessor if is_vision_available() else None

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = ConditionalDetrImageProcessingTester(self				)

  @property
  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        return self.image_processor_tester.prepare_image_processor_dict()

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = 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_resize"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""size"""				)				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = 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      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = 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   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        UpperCamelCase      = 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
        UpperCamelCase      = image_processing(image_inputs[0]      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = 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
        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = 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   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        UpperCamelCase      = 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
        UpperCamelCase      = image_processing(image_inputs[0]      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = 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
        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = 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   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        UpperCamelCase      = 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
        UpperCamelCase      = image_processing(image_inputs[0]      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = 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
        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = 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   A__						(				self				)  ->				Any:



        """simple docstring"""






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

        UpperCamelCase      = {"""image_id""": 39769, """annotations""": target}

        # encode them
        UpperCamelCase      = ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""				)
        UpperCamelCase      = image_processing(images=_SCREAMING_SNAKE_CASE      ,		annotations=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

        # verify pixel values
        UpperCamelCase      = torch.Size([1, 3, 800, 1066]				)
        self.assertEqual(encoding["""pixel_values"""].shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = 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]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)

        # verify area
        UpperCamelCase      = 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"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify boxes
        UpperCamelCase      = torch.Size([6, 4]				)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = 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]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)
        # verify image_id
        UpperCamelCase      = torch.tensor([39769]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify is_crowd
        UpperCamelCase      = torch.tensor([0, 0, 0, 0, 0, 0]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify class_labels
        UpperCamelCase      = torch.tensor([75, 75, 63, 65, 17, 17]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify orig_size
        UpperCamelCase      = torch.tensor([480, 640]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify size
        UpperCamelCase      = torch.tensor([800, 1066]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,		_SCREAMING_SNAKE_CASE				)				)


  @slow
  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






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

        UpperCamelCase      = {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

        UpperCamelCase      = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

        # encode them
        UpperCamelCase      = ConditionalDetrImageProcessor(format="""coco_panoptic"""				)
        UpperCamelCase      = image_processing(images=_SCREAMING_SNAKE_CASE      ,		annotations=_SCREAMING_SNAKE_CASE      ,		masks_path=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

        # verify pixel values
        UpperCamelCase      = torch.Size([1, 3, 800, 1066]				)
        self.assertEqual(encoding["""pixel_values"""].shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = 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]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)

        # verify area
        UpperCamelCase      = 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"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify boxes
        UpperCamelCase      = torch.Size([6, 4]				)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = 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]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)
        # verify image_id
        UpperCamelCase      = torch.tensor([39769]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify is_crowd
        UpperCamelCase      = torch.tensor([0, 0, 0, 0, 0, 0]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify class_labels
        UpperCamelCase      = torch.tensor([17, 17, 63, 75, 75, 93]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify masks
        UpperCamelCase      = 822873
        self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,		_SCREAMING_SNAKE_CASE				)
        # verify orig_size
        UpperCamelCase      = torch.tensor([480, 640]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify size
        UpperCamelCase      = torch.tensor([800, 1066]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
