"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class        a				(		lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  Optional[Any]     							=							KandinskyVaaControlnetImgaImgPipeline
    _snake_case     :  Optional[int]     							=							['image_embeds', 'negative_image_embeds', 'image', 'hint']
    _snake_case     :  int     							=							['image_embeds', 'negative_image_embeds', 'image', 'hint']
    _snake_case     :  Any     							=							[
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    _snake_case     :  List[Any]     							=							False


    @property
    def      lowerCAmelCase_  (					self	:  int					):
      return 32


    @property
    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      return 32


    @property
    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      return self.time_input_dim


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


    @property
    def      lowerCAmelCase_  (					self	:  Any					):
      return 100


    @property
    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      torch.manual_seed(0					)

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

      _UpperCAmelCase						 =      UNetaDConditionModel(**__lowerCAmelCase					)
      return model


    @property
    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      return {
          "block_out_channels": [32, 32, 64, 64],
          "down_block_types": [
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }


    @property
    def      lowerCAmelCase_  (					self	:  Tuple					):
      torch.manual_seed(0					)
      _UpperCAmelCase						 =      VQModel(**self.dummy_movq_kwargs					)
      return model


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      self.dummy_unet
      _UpperCAmelCase						 =      self.dummy_movq

      _UpperCAmelCase						 =      {
          """num_train_timesteps""": 1000,
          """beta_schedule""": """linear""",
          """beta_start""": 0.00_085,
          """beta_end""": 0.012,
          """clip_sample""": False,
          """set_alpha_to_one""": False,
          """steps_offset""": 0,
          """prediction_type""": """epsilon""",
          """thresholding""": False,
      }

      _UpperCAmelCase						 =      DDIMScheduler(**__lowerCAmelCase					)

      _UpperCAmelCase						 =      {
          """unet""": unet,
          """scheduler""": scheduler,
          """movq""": movq,
      }

      return components


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  List[Any]=0					):
      _UpperCAmelCase						 =      floats_tensor((1, self.text_embedder_hidden_size)   ,				rng=random.Random(__lowerCAmelCase					)					).to(__lowerCAmelCase					)
      _UpperCAmelCase						 =      floats_tensor((1, self.text_embedder_hidden_size)   ,				rng=random.Random(seed + 1					)					).to(
          __lowerCAmelCase					)
      # create init_image
      _UpperCAmelCase						 =      floats_tensor((1, 3, 64, 64)   ,				rng=random.Random(__lowerCAmelCase					)					).to(__lowerCAmelCase					)
      _UpperCAmelCase						 =      image.cpu().permute(0   ,				2   ,				3   ,				1					)[0]
      _UpperCAmelCase						 =      Image.fromarray(np.uinta(__lowerCAmelCase					)					).convert("""RGB"""					).resize((256, 256)					)
      # create hint
      _UpperCAmelCase						 =      floats_tensor((1, 3, 64, 64)   ,				rng=random.Random(__lowerCAmelCase					)					).to(__lowerCAmelCase					)

      if str(__lowerCAmelCase					).startswith("""mps"""					):
        _UpperCAmelCase						 =      torch.manual_seed(__lowerCAmelCase					)
      else:
        _UpperCAmelCase						 =      torch.Generator(device=__lowerCAmelCase					).manual_seed(__lowerCAmelCase					)
      _UpperCAmelCase						 =      {
          """image""": init_image,
          """image_embeds""": image_embeds,
          """negative_image_embeds""": negative_image_embeds,
          """hint""": hint,
          """generator""": generator,
          """height""": 64,
          """width""": 64,
          """num_inference_steps""": 10,
          """guidance_scale""": 7.0,
          """strength""": 0.2,
          """output_type""": """np""",
      }
      return inputs


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      """cpu"""

      _UpperCAmelCase						 =      self.get_dummy_components()

      _UpperCAmelCase						 =      self.pipeline_class(**__lowerCAmelCase					)
      _UpperCAmelCase						 =      pipe.to(__lowerCAmelCase					)

      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)

      _UpperCAmelCase						 =      pipe(**self.get_dummy_inputs(__lowerCAmelCase					)					)
      _UpperCAmelCase						 =      output.images

      _UpperCAmelCase						 =      pipe(
          **self.get_dummy_inputs(__lowerCAmelCase					)   ,				return_dict=__lowerCAmelCase   ,				)[0]

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

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

      _UpperCAmelCase						 =      np.array(
          [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]					)
      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()}'''



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


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""					)

      _UpperCAmelCase						 =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
      _UpperCAmelCase						 =      init_image.resize((512, 512)					)

      _UpperCAmelCase						 =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinskyv22/hint_image_cat.png"""					)
      _UpperCAmelCase						 =      torch.from_numpy(np.array(__lowerCAmelCase					)					).float() / 255.0
      _UpperCAmelCase						 =      hint.permute(2   ,				0   ,				1					).unsqueeze(0					)

      _UpperCAmelCase						 =      """A robot, 4k photo"""

      _UpperCAmelCase						 =      KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-prior"""   ,				torch_dtype=torch.floataa					)
      pipe_prior.to(__lowerCAmelCase					)

      _UpperCAmelCase						 =      KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-controlnet-depth"""   ,				torch_dtype=torch.floataa					)
      _UpperCAmelCase						 =      pipeline.to(__lowerCAmelCase					)

      pipeline.set_progress_bar_config(disable=__lowerCAmelCase					)

      _UpperCAmelCase						 =      torch.Generator(device="""cpu"""					).manual_seed(0					)

      _UpperCAmelCase ,				_UpperCAmelCase						 =      pipe_prior(
          __lowerCAmelCase   ,				image=__lowerCAmelCase   ,				strength=0.85   ,				generator=__lowerCAmelCase   ,				negative_prompt=""""""   ,				).to_tuple()

      _UpperCAmelCase						 =      pipeline(
          image=__lowerCAmelCase   ,				image_embeds=__lowerCAmelCase   ,				negative_image_embeds=__lowerCAmelCase   ,				hint=__lowerCAmelCase   ,				generator=__lowerCAmelCase   ,				num_inference_steps=100   ,				height=512   ,				width=512   ,				strength=0.5   ,				output_type="""np"""   ,				)

      _UpperCAmelCase						 =      output.images[0]

      assert image.shape == (512, 512, 3)

      assert_mean_pixel_difference(__lowerCAmelCase   ,				__lowerCAmelCase					)

"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_pytesseract_available():
    from PIL import Image

    from transformers import LayoutLMvaImageProcessor


class        a				(		unittest.TestCase   ):


    def __init__(					self	:  int   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  str=7   ,				__lowerCAmelCase	:  Dict=3   ,				__lowerCAmelCase	:  List[Any]=18   ,				__lowerCAmelCase	:  int=30   ,				__lowerCAmelCase	:  Any=400   ,				__lowerCAmelCase	:  Tuple=True   ,				__lowerCAmelCase	:  str=None   ,				__lowerCAmelCase	:  Optional[Any]=True   ,				):
      _UpperCAmelCase						 =      size if size is not None else {"""height""": 18, """width""": 18}
      _UpperCAmelCase						 =      parent
      _UpperCAmelCase						 =      batch_size
      _UpperCAmelCase						 =      num_channels
      _UpperCAmelCase						 =      image_size
      _UpperCAmelCase						 =      min_resolution
      _UpperCAmelCase						 =      max_resolution
      _UpperCAmelCase						 =      do_resize
      _UpperCAmelCase						 =      size
      _UpperCAmelCase						 =      apply_ocr


    def      lowerCAmelCase_  (					self	:  int					):
      return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}



@require_torch
@require_pytesseract
class        a				(		lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  List[str]     							=							LayoutLMvaImageProcessor if is_pytesseract_available() else None


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      LayoutLMvaImageProcessingTester(self					)


    @property
    def      lowerCAmelCase_  (					self	:  Dict					):
      return self.image_processor_tester.prepare_image_processor_dict()


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(__lowerCAmelCase   ,				"""do_resize"""					)					)
      self.assertTrue(hasattr(__lowerCAmelCase   ,				"""size"""					)					)
      self.assertTrue(hasattr(__lowerCAmelCase   ,				"""apply_ocr"""					)					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size   ,				{"""height""": 18, """width""": 18}					)

      _UpperCAmelCase						 =      self.image_processing_class.from_dict(self.image_processor_dict   ,				size=42					)
      self.assertEqual(image_processor.size   ,				{"""height""": 42, """width""": 42}					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      pass


    def      lowerCAmelCase_  (					self	:  List[str]					):
      # Initialize image_processing
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      _UpperCAmelCase						 =      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__lowerCAmelCase					)
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase   ,				Image.Image					)

      # Test not batched input
      _UpperCAmelCase						 =      image_processing(image_inputs[0]   ,				return_tensors="""pt"""					)
      self.assertEqual(
          encoding.pixel_values.shape   ,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )   ,				)

      self.assertIsInstance(encoding.words   ,				__lowerCAmelCase					)
      self.assertIsInstance(encoding.boxes   ,				__lowerCAmelCase					)

      # Test batched
      _UpperCAmelCase						 =      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,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )   ,				)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      # Initialize image_processing
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      _UpperCAmelCase						 =      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
      _UpperCAmelCase						 =      image_processing(image_inputs[0]   ,				return_tensors="""pt"""					).pixel_values
      self.assertEqual(
          encoded_images.shape   ,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )   ,				)

      # Test batched
      _UpperCAmelCase						 =      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,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )   ,				)


    def      lowerCAmelCase_  (					self	:  Any					):
      # Initialize image_processing
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      _UpperCAmelCase						 =      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
      _UpperCAmelCase						 =      image_processing(image_inputs[0]   ,				return_tensors="""pt"""					).pixel_values
      self.assertEqual(
          encoded_images.shape   ,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )   ,				)

      # Test batched
      _UpperCAmelCase						 =      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,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )   ,				)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      # with apply_OCR = True
      _UpperCAmelCase						 =      LayoutLMvaImageProcessor()

      from datasets import load_dataset

      _UpperCAmelCase						 =      load_dataset("""hf-internal-testing/fixtures_docvqa"""   ,				split="""test"""					)

      _UpperCAmelCase						 =      Image.open(ds[0]["""file"""]					).convert("""RGB"""					)

      _UpperCAmelCase						 =      image_processing(__lowerCAmelCase   ,				return_tensors="""pt"""					)

      self.assertEqual(encoding.pixel_values.shape   ,				(1, 3, 224, 224)					)
      self.assertEqual(len(encoding.words					)   ,				len(encoding.boxes					)					)

      # fmt: off
      # the words and boxes were obtained with Tesseract 4.1.1
      _UpperCAmelCase						 =      [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
      _UpperCAmelCase						 =      [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
      # fmt: on

      self.assertListEqual(encoding.words   ,				__lowerCAmelCase					)
      self.assertListEqual(encoding.boxes   ,				__lowerCAmelCase					)

      # with apply_OCR = False
      _UpperCAmelCase						 =      LayoutLMvaImageProcessor(apply_ocr=__lowerCAmelCase					)

      _UpperCAmelCase						 =      image_processing(__lowerCAmelCase   ,				return_tensors="""pt"""					)

      self.assertEqual(encoding.pixel_values.shape   ,				(1, 3, 224, 224)					)
