
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       unittest.TestCase			):
      _A    :		Optional[int]     						=  KandinskyInpaintPipeline
      _A    :		List[str]     						=  ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
      _A    :		List[str]     						=  [
          'prompt',
          'negative_prompt',
          'image_embeds',
          'negative_image_embeds',
          'image',
          'mask_image',
      ]
      _A    :		Tuple     						=  [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'negative_prompt',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _A    :		List[str]     						=  False
      @property
      def A_      (      self			):
          return 32
      @property
      def A_      (      self			):
          return 32
      @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_00
      @property
      def A_      (      self			):
          snake_case__    =					XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"			)
          return tokenizer
      @property
      def A_      (      self			):
          torch.manual_seed(0			)
          snake_case__    =					MCLIPConfig(
              numDims=self.cross_attention_dim		,						transformerDimensions=self.text_embedder_hidden_size		,						hidden_size=self.text_embedder_hidden_size		,						intermediate_size=37		,						num_attention_heads=4		,						num_hidden_layers=5		,						vocab_size=10_05		,						)

          snake_case__    =					MultilingualCLIP(lowerCamelCase			)
          snake_case__    =					text_encoder.eval()

          return text_encoder
      @property
      def A_      (      self			):
          torch.manual_seed(0			)

          snake_case__    =					{
              "in_channels": 9,
              # Out channels is double in channels because predicts mean and variance
              "out_channels": 8,
              "addition_embed_type": "text_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": "text_image_proj",
              "cross_attention_dim": self.cross_attention_dim,
              "attention_head_dim": 4,
              "resnet_time_scale_shift": "scale_shift",
              "class_embed_type": None,
          }

          snake_case__    =					UNetaDConditionModel(**lowerCamelCase			)
          return model
      @property
      def A_      (      self			):
          return {
              "block_out_channels": [32, 64],
              "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": 12,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }
      @property
      def A_      (      self			):
          torch.manual_seed(0			)
          snake_case__    =					VQModel(**self.dummy_movq_kwargs			)
          return model
      def A_      (      self			):
          snake_case__    =					self.dummy_text_encoder
          snake_case__    =					self.dummy_tokenizer
          snake_case__    =					self.dummy_unet
          snake_case__    =					self.dummy_movq

          snake_case__    =					DDIMScheduler(
              num_train_timesteps=10_00		,						beta_schedule="linear"		,						beta_start=0.0_0_0_8_5		,						beta_end=0.0_1_2		,						clip_sample=lowerCamelCase		,						set_alpha_to_one=lowerCamelCase		,						steps_offset=1		,						prediction_type="epsilon"		,						thresholding=lowerCamelCase		,						)

          snake_case__    =					{
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "movq": movq,
          }

          return components
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase=0			):
          snake_case__    =					floats_tensor((1, self.cross_attention_dim)		,						rng=random.Random(lowerCamelCase			)			).to(lowerCamelCase			)
          snake_case__    =					floats_tensor((1, self.cross_attention_dim)		,						rng=random.Random(seed + 1			)			).to(lowerCamelCase			)
          # create init_image
          snake_case__    =					floats_tensor((1, 3, 64, 64)		,						rng=random.Random(lowerCamelCase			)			).to(lowerCamelCase			)
          snake_case__    =					image.cpu().permute(0		,						2		,						3		,						1			)[0]
          snake_case__    =					Image.fromarray(np.uinta(lowerCamelCase			)			).convert("RGB"			).resize((2_56, 2_56)			)
          # create mask
          snake_case__    =					np.ones((64, 64)		,						dtype=np.floataa			)
          snake_case__    =					0

          if str(lowerCamelCase			).startswith("mps"			):
              snake_case__    =					torch.manual_seed(lowerCamelCase			)
          else:
              snake_case__    =					torch.Generator(device=lowerCamelCase			).manual_seed(lowerCamelCase			)
          snake_case__    =					{
              "prompt": "horse",
              "image": init_image,
              "mask_image": mask,
              "image_embeds": image_embeds,
              "negative_image_embeds": negative_image_embeds,
              "generator": generator,
              "height": 64,
              "width": 64,
              "num_inference_steps": 2,
              "guidance_scale": 4.0,
              "output_type": "np",
          }
          return inputs
      def A_      (      self			):
          snake_case__    =					"cpu"

          snake_case__    =					self.get_dummy_components()

          snake_case__    =					self.pipeline_class(**lowerCamelCase			)
          snake_case__    =					pipe.to(lowerCamelCase			)

          pipe.set_progress_bar_config(disable=lowerCamelCase			)

          snake_case__    =					pipe(**self.get_dummy_inputs(lowerCamelCase			)			)
          snake_case__    =					output.images

          snake_case__    =					pipe(
              **self.get_dummy_inputs(lowerCamelCase			)		,						return_dict=lowerCamelCase		,						)[0]

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

          print(F"""image.shape {image.shape}"""			)

          assert image.shape == (1, 64, 64, 3)

          snake_case__    =					np.array(
              [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]			)

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



      def A_      (      self			):
          snake_case__    =					load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"			)

          snake_case__    =					load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"			)
          snake_case__    =					np.ones((7_68, 7_68)		,						dtype=np.floataa			)
          snake_case__    =					0

          snake_case__    =					"a hat"

          snake_case__    =					KandinskyPriorPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-1-prior"		,						torch_dtype=torch.floataa			)
          pipe_prior.to(lowerCamelCase			)

          snake_case__    =					KandinskyInpaintPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-1-inpaint"		,						torch_dtype=torch.floataa			)
          snake_case__    =					pipeline.to(lowerCamelCase			)
          pipeline.set_progress_bar_config(disable=lowerCamelCase			)

          snake_case__    =					torch.Generator(device="cpu"			).manual_seed(0			)
          snake_case__    ,       snake_case__    =					pipe_prior(
              lowerCamelCase		,						generator=lowerCamelCase		,						num_inference_steps=5		,						negative_prompt=""		,						).to_tuple()

          snake_case__    =					pipeline(
              lowerCamelCase		,						image=lowerCamelCase		,						mask_image=lowerCamelCase		,						image_embeds=lowerCamelCase		,						negative_image_embeds=lowerCamelCase		,						generator=lowerCamelCase		,						num_inference_steps=1_00		,						height=7_68		,						width=7_68		,						output_type="np"		,						)

          snake_case__    =					output.images[0]

          assert image.shape == (7_68, 7_68, 3)

          assert_mean_pixel_difference(lowerCamelCase		,						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        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase=7		,						lowerCamelCase=3		,						lowerCamelCase=30		,						lowerCamelCase=4_00		,						lowerCamelCase=True		,						lowerCamelCase=None		,						lowerCamelCase=True		,						lowerCamelCase=[0.5, 0.5, 0.5]		,						lowerCamelCase=[0.5, 0.5, 0.5]		,						lowerCamelCase=True		,						lowerCamelCase=1 / 2_55		,						lowerCamelCase=True		,						):
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          snake_case__    =					size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
          snake_case__    =					parent
          snake_case__    =					batch_size
          snake_case__    =					num_channels
          snake_case__    =					min_resolution
          snake_case__    =					max_resolution
          snake_case__    =					do_resize
          snake_case__    =					size
          snake_case__    =					do_normalize
          snake_case__    =					image_mean
          snake_case__    =					image_std
          snake_case__    =					do_rescale
          snake_case__    =					rescale_factor
          snake_case__    =					do_pad
      def A_      (      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 A_      (      self		,						lowerCamelCase		,						lowerCamelCase=False			):
          if not batched:
              snake_case__    =					image_inputs[0]
              if isinstance(lowerCamelCase		,						Image.Image			):
                  snake_case__    ,       snake_case__    =					image.size
              else:
                  snake_case__    ,       snake_case__    =					image.shape[1], image.shape[2]
              if w < h:
                  snake_case__    =					int(self.size["shortest_edge"] * h / w			)
                  snake_case__    =					self.size["shortest_edge"]
              elif w > h:
                  snake_case__    =					self.size["shortest_edge"]
                  snake_case__    =					int(self.size["shortest_edge"] * w / h			)
              else:
                  snake_case__    =					self.size["shortest_edge"]
                  snake_case__    =					self.size["shortest_edge"]

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

          return expected_height, expected_width





@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       unittest.TestCase			):
      _A    :		List[str]     						=  DetaImageProcessor if is_vision_available() else None
      def A_      (      self			):
          snake_case__    =					DetaImageProcessingTester(self			)
      @property
      def A_      (      self			):
          return self.image_processor_tester.prepare_image_processor_dict()
      def A_      (      self			):
          snake_case__    =					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 A_      (      self			):
          snake_case__    =					self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size		,						{"shortest_edge": 18, "longest_edge": 13_33}			)
          self.assertEqual(image_processor.do_pad		,						lowerCamelCase			)
      def A_      (      self			):
          pass
      def A_      (      self			):
          # Initialize image_processing
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          snake_case__    =					prepare_image_inputs(self.image_processor_tester		,						equal_resolution=lowerCamelCase			)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,						Image.Image			)

          # Test not batched input
          snake_case__    =					image_processing(image_inputs[0]		,						return_tensors="pt"			).pixel_values

          snake_case__    ,       snake_case__    =					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
          snake_case__    ,       snake_case__    =					self.image_processor_tester.get_expected_values(lowerCamelCase		,						batched=lowerCamelCase			)

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

          snake_case__    ,       snake_case__    =					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
          snake_case__    =					image_processing(lowerCamelCase		,						return_tensors="pt"			).pixel_values

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

          snake_case__    ,       snake_case__    =					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
          snake_case__    =					image_processing(lowerCamelCase		,						return_tensors="pt"			).pixel_values

          snake_case__    ,       snake_case__    =					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 A_      (      self			):
          # prepare image and target
          snake_case__    =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
          with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"		,						"r"			) as f:
              snake_case__    =					json.loads(f.read()			)

          snake_case__    =					{"image_id": 3_97_69, "annotations": target}

          # encode them
          snake_case__    =					DetaImageProcessor()
          snake_case__    =					image_processing(images=lowerCamelCase		,						annotations=lowerCamelCase		,						return_tensors="pt"			)

          # verify pixel values
          snake_case__    =					torch.Size([1, 3, 8_00, 10_66]			)
          self.assertEqual(encoding["pixel_values"].shape		,						lowerCamelCase			)

          snake_case__    =					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
          snake_case__    =					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
          snake_case__    =					torch.Size([6, 4]			)
          self.assertEqual(encoding["labels"][0]["boxes"].shape		,						lowerCamelCase			)
          snake_case__    =					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
          snake_case__    =					torch.tensor([3_97_69]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,						lowerCamelCase			)			)
          # verify is_crowd
          snake_case__    =					torch.tensor([0, 0, 0, 0, 0, 0]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,						lowerCamelCase			)			)
          # verify class_labels
          snake_case__    =					torch.tensor([75, 75, 63, 65, 17, 17]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,						lowerCamelCase			)			)
          # verify orig_size
          snake_case__    =					torch.tensor([4_80, 6_40]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,						lowerCamelCase			)			)
          # verify size
          snake_case__    =					torch.tensor([8_00, 10_66]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,						lowerCamelCase			)			)



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

          snake_case__    =					{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

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

          # encode them
          snake_case__    =					DetaImageProcessor(format="coco_panoptic"			)
          snake_case__    =					image_processing(images=lowerCamelCase		,						annotations=lowerCamelCase		,						masks_path=lowerCamelCase		,						return_tensors="pt"			)

          # verify pixel values
          snake_case__    =					torch.Size([1, 3, 8_00, 10_66]			)
          self.assertEqual(encoding["pixel_values"].shape		,						lowerCamelCase			)

          snake_case__    =					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
          snake_case__    =					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
          snake_case__    =					torch.Size([6, 4]			)
          self.assertEqual(encoding["labels"][0]["boxes"].shape		,						lowerCamelCase			)
          snake_case__    =					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
          snake_case__    =					torch.tensor([3_97_69]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,						lowerCamelCase			)			)
          # verify is_crowd
          snake_case__    =					torch.tensor([0, 0, 0, 0, 0, 0]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,						lowerCamelCase			)			)
          # verify class_labels
          snake_case__    =					torch.tensor([17, 17, 63, 75, 75, 93]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,						lowerCamelCase			)			)
          # verify masks
          snake_case__    =					82_28_73
          self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,						lowerCamelCase			)
          # verify orig_size
          snake_case__    =					torch.tensor([4_80, 6_40]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,						lowerCamelCase			)			)
          # verify size
          snake_case__    =					torch.tensor([8_00, 10_66]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,						lowerCamelCase			)			)



