


'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 							__UpperCamelCase    (		lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   StableUnCLIPImgaImgPipeline
	A_           =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
	A_           =   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	A_           =   frozenset(
	    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	A_           =   frozenset([] )




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  32
						__a      :	List[Any]    =  embedder_hidden_size

						# image encoding components

						__a      :	List[Any]    =  CLIPImageProcessor(crop_size=32						,    size=32							)

						torch.manual_seed(0							)
						__a      :	Union[str, Any]    =  CLIPVisionModelWithProjection(
						    CLIPVisionConfig(
						        hidden_size=__a						,    projection_dim=__a						,    num_hidden_layers=5						,    num_attention_heads=4						,    image_size=32						,    intermediate_size=37						,    patch_size=1						,    )							)

						# regular denoising components

						torch.manual_seed(0							)
						__a      :	Optional[int]    =  StableUnCLIPImageNormalizer(embedding_dim=__a							)
						__a      :	Any    =  DDPMScheduler(beta_schedule='squaredcos_cap_v2'							)

						torch.manual_seed(0							)
						__a      :	Tuple    =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

						torch.manual_seed(0							)
						__a      :	Union[str, Any]    =  CLIPTextModel(
						    CLIPTextConfig(
						        bos_token_id=0						,    eos_token_id=2						,    hidden_size=__a						,    projection_dim=32						,    intermediate_size=37						,    layer_norm_eps=1E-0_5						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1000						,    )							)

						torch.manual_seed(0							)
						__a      :	Tuple    =  UNetaDConditionModel(
						    sample_size=32						,    in_channels=4						,    out_channels=4						,    down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')						,    up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')						,    block_out_channels=(32, 64)						,    attention_head_dim=(2, 4)						,    class_embed_type='projection'						,    projection_class_embeddings_input_dim=embedder_projection_dim * 2						,    cross_attention_dim=__a						,    layers_per_block=1						,    upcast_attention=__a						,    use_linear_projection=__a						,    )

						torch.manual_seed(0							)
						__a      :	Any    =  DDIMScheduler(
						    beta_schedule='scaled_linear'						,    beta_start=0.00085						,    beta_end=0.012						,    prediction_type='v_prediction'						,    set_alpha_to_one=__a						,    steps_offset=1						,    )

						torch.manual_seed(0							)
						__a      :	Optional[Any]    =  AutoencoderKL()

						__a      :	Optional[int]    =  {
						    # image encoding components
						    'feature_extractor': feature_extractor,
						    'image_encoder': image_encoder.eval(),
						    # image noising components
						    'image_normalizer': image_normalizer.eval(),
						    'image_noising_scheduler': image_noising_scheduler,
						    # regular denoising components
						    'tokenizer': tokenizer,
						    'text_encoder': text_encoder.eval(),
						    'unet': unet.eval(),
						    'scheduler': scheduler,
						    'vae': vae.eval(),
						}

						return components




	def 				__UpperCAmelCase							(       self						,    __a						,    __a=0						,    __a=True							):

						'''simple docstring'''





						if str(__a							).startswith('mps'							):
											__a      :	Optional[int]    =  torch.manual_seed(__a							)
						else:
											__a      :	List[Any]    =  torch.Generator(device=__a							).manual_seed(__a							)

						__a      :	List[Any]    =  floats_tensor((1, 3, 32, 32)						,    rng=random.Random(__a							)							).to(__a							)

						if pil_image:
											__a      :	List[str]    =  input_image * 0.5 + 0.5
											__a      :	Optional[Any]    =  input_image.clamp(0						,    1							)
											__a      :	str    =  input_image.cpu().permute(0						,    2						,    3						,    1							).float().numpy()
											__a      :	Any    =  DiffusionPipeline.numpy_to_pil(__a							)[0]

						return {
						    "prompt": "An anime racoon running a marathon",
						    "image": input_image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "output_type": "np",
						}




	@skip_mps
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Dict    =  'cpu'  # ensure determinism for the device-dependent torch.Generator
						__a      :	List[Any]    =  self.get_dummy_components()
						__a      :	Any    =  StableUnCLIPImgaImgPipeline(**__a							)
						__a      :	int    =  sd_pipe.to(__a							)
						sd_pipe.set_progress_bar_config(disable=__a							)

						__a      :	List[str]    =  self.get_dummy_inputs(__a							)
						inputs.update({'image_embeds': None}							)
						__a      :	Optional[int]    =  sd_pipe(**__a							).images
						__a      :	Union[str, Any]    =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						__a      :	List[Any]    =  np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078]							)

						assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  torch_device in ['cpu', 'mps']

						self._test_attention_slicing_forward_pass(test_max_difference=__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  torch_device in ['cpu', 'mps']

						self._test_inference_batch_single_identical(test_max_difference=__a							)





	@unittest.skipIf(
	    torch_device != 'cuda' or not is_xformers_available()						,    reason='XFormers attention is only available with CUDA and `xformers` installed'						,    )
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__a							)





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'							)

						__a      :	Optional[Any]    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'							)

						__a      :	Optional[Any]    =  StableUnCLIPImgaImgPipeline.from_pretrained(
						    'fusing/stable-unclip-2-1-l-img2img'						,    torch_dtype=torch.floataa							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						# stable unclip will oom when integration tests are run on a V100,
						# so turn on memory savings
						pipe.enable_attention_slicing()
						pipe.enable_sequential_cpu_offload()

						__a      :	Any    =  torch.Generator(device='cpu'							).manual_seed(0							)
						__a      :	int    =  pipe(__a						,    'anime turle'						,    generator=__a						,    output_type='np'							)

						__a      :	int    =  output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(__a						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'							)

						__a      :	int    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'							)

						__a      :	List[str]    =  StableUnCLIPImgaImgPipeline.from_pretrained(
						    'fusing/stable-unclip-2-1-h-img2img'						,    torch_dtype=torch.floataa							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						# stable unclip will oom when integration tests are run on a V100,
						# so turn on memory savings
						pipe.enable_attention_slicing()
						pipe.enable_sequential_cpu_offload()

						__a      :	Dict    =  torch.Generator(device='cpu'							).manual_seed(0							)
						__a      :	Tuple    =  pipe(__a						,    'anime turle'						,    generator=__a						,    output_type='np'							)

						__a      :	List[Any]    =  output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'							)

						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						__a      :	List[Any]    =  StableUnCLIPImgaImgPipeline.from_pretrained(
						    'fusing/stable-unclip-2-1-h-img2img'						,    torch_dtype=torch.floataa							)
						__a      :	Tuple    =  pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()
						pipe.enable_sequential_cpu_offload()

						__a      :	str    =  pipe(
						    __a						,    'anime turtle'						,    num_inference_steps=2						,    output_type='np'						,    )

						__a      :	List[str]    =  torch.cuda.max_memory_allocated()
						# make sure that less than 7 GB is allocated
						assert mem_bytes < 7 * 10**9







'''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 							__UpperCamelCase    (		unittest.TestCase ):




	def __init__(       self						,    __a						,    __a=7						,    __a=3						,    __a=18						,    __a=30						,    __a=400						,    __a=True						,    __a=None						,    __a=True						,    ):

						'''simple docstring'''





						__a      :	List[Any]    =  size if size is not None else {'height': 18, 'width': 18}
						__a      :	int    =  parent
						__a      :	Dict    =  batch_size
						__a      :	Optional[int]    =  num_channels
						__a      :	List[Any]    =  image_size
						__a      :	Tuple    =  min_resolution
						__a      :	str    =  max_resolution
						__a      :	str    =  do_resize
						__a      :	Optional[Any]    =  size
						__a      :	str    =  apply_ocr





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class 							__UpperCamelCase    (		lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   LayoutLMvaImageProcessor if is_pytesseract_available() else None




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Dict    =  LayoutLMvaImageProcessingTester(self							)




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return self.image_processor_tester.prepare_image_processor_dict()




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(__a						,    'do_resize'							)							)
						self.assertTrue(hasattr(__a						,    'size'							)							)
						self.assertTrue(hasattr(__a						,    'apply_ocr'							)							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  self.image_processing_class.from_dict(self.image_processor_dict							)
						self.assertEqual(image_processor.size						,    {'height': 18, 'width': 18}							)

						__a      :	Optional[int]    =  self.image_processing_class.from_dict(self.image_processor_dict						,    size=42							)
						self.assertEqual(image_processor.size						,    {'height': 42, 'width': 42}							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						# Test not batched input
						__a      :	Union[str, Any]    =  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						,    __a							)
						self.assertIsInstance(encoding.boxes						,    __a							)

						# Test batched
						__a      :	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,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )						,    )




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						__a      :	Union[str, Any]    =  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
						__a      :	Optional[Any]    =  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
						__a      :	Tuple    =  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,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )						,    )




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.image_processing_class(**self.image_processor_dict							)
						# create random PyTorch tensors
						__a      :	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
						__a      :	List[Any]    =  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
						__a      :	List[str]    =  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,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )						,    )





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  LayoutLMvaImageProcessor()

						from datasets import load_dataset

						__a      :	str    =  load_dataset('hf-internal-testing/fixtures_docvqa'						,    split='test'							)

						__a      :	Tuple    =  Image.open(ds[0]['file']							).convert('RGB'							)

						__a      :	Optional[Any]    =  image_processing(__a						,    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
						__a      :	Optional[Any]    =  [['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
						__a      :	Union[str, Any]    =  [[[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						,    __a							)
						self.assertListEqual(encoding.boxes						,    __a							)

						# with apply_OCR = False
						__a      :	List[Any]    =  LayoutLMvaImageProcessor(apply_ocr=__a							)

						__a      :	List[Any]    =  image_processing(__a						,    return_tensors='pt'							)

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



