'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_A								=   logging.get_logger(__name__)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=False							):
     lowercase_					:       Tuple     				=					[]
     for i in range(config.num_hidden_layers							):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')							)
          rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')							)
          rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')							)
          rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')							)
          rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')							)
          rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')							)
          rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')							)
          rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')							)
          rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')							)
          rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')							)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('cls_token', 'vit.embeddings.cls_token'),
             ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
             ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
             ('pos_embed', 'vit.embeddings.position_embeddings'),
         ]							)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
              ]							)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          lowercase_					:       Dict     				=					[(pair[0], pair[1][4:]) if pair[1].startswith('vit'							) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('norm.weight', 'vit.layernorm.weight'),
                  ('norm.bias', 'vit.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]							)

     return rename_keys

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=False							):
     for i in range(config.num_hidden_layers							):
          if base_model:
               lowercase_					:       Dict     				=					''
          else:
               lowercase_					:       int     				=					'vit.'
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          lowercase_					:       Any     				=					state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''							)
          lowercase_					:       Any     				=					state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''							)
          # next, add query, keys and values (in that order) to the state dict
          lowercase_					:       Union[str, Any]     				=					in_proj_weight[
              : config.hidden_size, :
          ]
          lowercase_					:       Union[str, Any]     				=					in_proj_bias[: config.hidden_size]
          lowercase_					:       List[str]     				=					in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          lowercase_					:       List[Any]     				=					in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          lowercase_					:       List[Any]     				=					in_proj_weight[
              -config.hidden_size :, :
          ]
          lowercase_					:       List[Any]     				=					in_proj_bias[-config.hidden_size :]

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       int     				=					['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					dct.pop(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[Any]     				=					val

def _UpperCamelCase   (       ):
     lowercase_					:       int     				=					'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase_					:       int     				=					Image.open(requests.get(SCREAMING_SNAKE_CASE_  ,       stream=SCREAMING_SNAKE_CASE_							).raw							)
     return im

@torch.no_grad()
def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=True							):
     lowercase_					:       str     				=					ViTConfig()
     # patch_size
     if model_name[-1] == "8":
          lowercase_					:       Tuple     				=					8
     # set labels if required
     if not base_model:
          lowercase_					:       Any     				=					1_000
          lowercase_					:       Tuple     				=					'huggingface/label-files'
          lowercase_					:       Union[str, Any]     				=					'imagenet-1k-id2label.json'
          lowercase_					:       int     				=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       repo_type='dataset'							)  ,       'r'							)							)
          lowercase_					:       Union[str, Any]     				=					{int(SCREAMING_SNAKE_CASE_							): v for k, v in idalabel.items()}
          lowercase_					:       Union[str, Any]     				=					idalabel
          lowercase_					:       str     				=					{v: k for k, v in idalabel.items()}
     # size of the architecture
     if model_name in ["dino_vits8", "dino_vits16"]:
          lowercase_					:       Union[str, Any]     				=					384
          lowercase_					:       Optional[int]     				=					1_536
          lowercase_					:       List[Any]     				=					12
          lowercase_					:       str     				=					6

     # load original model from torch hub
     lowercase_					:       Optional[int]     				=					torch.hub.load('facebookresearch/dino:main'  ,       SCREAMING_SNAKE_CASE_							)
     original_model.eval()

     # load state_dict of original model, remove and rename some keys
     lowercase_					:       int     				=					original_model.state_dict()
     if base_model:
          remove_classification_head_(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       int     				=					create_rename_keys(SCREAMING_SNAKE_CASE_  ,       base_model=SCREAMING_SNAKE_CASE_							)
     for src, dest in rename_keys:
          rename_key(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     read_in_q_k_v(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     # load HuggingFace model
     if base_model:
          lowercase_					:       Any     				=					ViTModel(SCREAMING_SNAKE_CASE_  ,       add_pooling_layer=SCREAMING_SNAKE_CASE_							).eval()
     else:
          lowercase_					:       Optional[Any]     				=					ViTForImageClassification(SCREAMING_SNAKE_CASE_							).eval()
     model.load_state_dict(SCREAMING_SNAKE_CASE_							)

     # Check outputs on an image, prepared by ViTImageProcessor
     lowercase_					:       Tuple     				=					ViTImageProcessor()
     lowercase_					:       Tuple     				=					image_processor(images=prepare_img()  ,       return_tensors='pt'							)
     lowercase_					:       Tuple     				=					encoding['pixel_values']
     lowercase_					:       Tuple     				=					model(SCREAMING_SNAKE_CASE_							)

     if base_model:
          lowercase_					:       Any     				=					original_model(SCREAMING_SNAKE_CASE_							)
          assert torch.allclose(SCREAMING_SNAKE_CASE_  ,       outputs.last_hidden_state[:, 0, :]  ,       atol=1e-1							)
     else:
          lowercase_					:       Optional[Any]     				=					original_model(SCREAMING_SNAKE_CASE_							)
          assert logits.shape == outputs.logits.shape
          assert torch.allclose(SCREAMING_SNAKE_CASE_  ,       outputs.logits  ,       atol=1e-3							)

     Path(SCREAMING_SNAKE_CASE_							).mkdir(exist_ok=SCREAMING_SNAKE_CASE_							)
     print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''							)
     model.save_pretrained(SCREAMING_SNAKE_CASE_							)
     print(f'''Saving image processor to {pytorch_dump_folder_path}'''							)
     image_processor.save_pretrained(SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
 _A								=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default='dino_vitb16',
     type=str,
     help='Name of the model trained with DINO you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )
 parser.add_argument(
     '--base_model',
     action='store_true',
     help='Whether to only convert the base model (no projection head weights).',
 )

 parser.set_defaults(base_model=True)
 _A								=   parser.parse_args()
 convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)

'''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			):






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





   def 		_lowerCamelCase (self				)    ->     List[str]:
        return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}



@require_torch
@require_pytesseract
class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       int     =    LayoutLMvaImageProcessor if is_pytesseract_available() else None
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       str     				=					LayoutLMvaImageProcessingTester(self				)
   @property
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        return self.image_processor_tester.prepare_image_processor_dict()
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Optional[Any]     				=					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 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Any     				=					self.image_processing_class.from_dict(self.image_processor_dict				)
        self.assertEqual(image_processor.size       ,				{'height': 18, 'width': 18}				)

        lowercase_					:       Tuple     				=					self.image_processing_class.from_dict(self.image_processor_dict       ,				size=42				)
        self.assertEqual(image_processor.size       ,				{'height': 42, 'width': 42}				)
   def 		_lowerCamelCase (self				)    ->     int:
        pass
   def 		_lowerCamelCase (self				)    ->     Dict:
        # Initialize image_processing
        lowercase_					:       int     				=					self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        lowercase_					:       Tuple     				=					prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_a				)
        for image in image_inputs:
             self.assertIsInstance(_a       ,				Image.Image				)

        # Test not batched input
        lowercase_					:       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
        lowercase_					:       Union[str, 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 		_lowerCamelCase (self				)    ->     Tuple:
        # Initialize image_processing
        lowercase_					:       List[Any]     				=					self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        lowercase_					:       str     				=					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
        lowercase_					:       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
        lowercase_					:       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 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        # Initialize image_processing
        lowercase_					:       List[Any]     				=					self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        lowercase_					:       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
        lowercase_					:       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
        lowercase_					:       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 		_lowerCamelCase (self				)    ->     str:
        # with apply_OCR = True
        lowercase_					:       Optional[int]     				=					LayoutLMvaImageProcessor()

        from datasets import load_dataset

        lowercase_					:       List[Any]     				=					load_dataset('hf-internal-testing/fixtures_docvqa'       ,				split='test'				)

        lowercase_					:       Union[str, Any]     				=					Image.open(ds[0]['file']				).convert('RGB'				)

        lowercase_					:       List[str]     				=					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
        lowercase_					:       str     				=					[['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
        lowercase_					:       Optional[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
        lowercase_					:       Optional[int]     				=					LayoutLMvaImageProcessor(apply_ocr=_a				)

        lowercase_					:       Dict     				=					image_processing(_a       ,				return_tensors='pt'				)

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




