'''simple docstring'''




import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_A								=   1_6
_A								=   3_2

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_ = 16  ,       SCREAMING_SNAKE_CASE_ = "bert-base-cased"							):
     lowercase_					:       str     				=					AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       str     				=					load_dataset('glue'  ,       'mrpc'							)

     def tokenize_function(SCREAMING_SNAKE_CASE_							):
          # max_length=None => use the model max length (it's actually the default)
          lowercase_					:       Dict     				=					tokenizer(examples['sentence1']  ,       examples['sentence2']  ,       truncation=SCREAMING_SNAKE_CASE_  ,       max_length=SCREAMING_SNAKE_CASE_							)
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     lowercase_					:       int     				=					datasets.map(
         SCREAMING_SNAKE_CASE_  ,       batched=SCREAMING_SNAKE_CASE_  ,       remove_columns=['idx', 'sentence1', 'sentence2']  ,       load_from_cache_file=SCREAMING_SNAKE_CASE_							)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     lowercase_					:       str     				=					tokenized_datasets.rename_column('label'  ,       'labels'							)

     def collate_fn(SCREAMING_SNAKE_CASE_							):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(SCREAMING_SNAKE_CASE_  ,       padding='max_length'  ,       max_length=128  ,       return_tensors='pt'							)
          return tokenizer.pad(SCREAMING_SNAKE_CASE_  ,       padding='longest'  ,       return_tensors='pt'							)

     # Instantiate dataloaders.
     lowercase_					:       Tuple     				=					DataLoader(
         tokenized_datasets['train']  ,       shuffle=SCREAMING_SNAKE_CASE_  ,       collate_fn=SCREAMING_SNAKE_CASE_  ,       batch_size=SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Dict     				=					DataLoader(
         tokenized_datasets['validation']  ,       shuffle=SCREAMING_SNAKE_CASE_  ,       collate_fn=SCREAMING_SNAKE_CASE_  ,       batch_size=SCREAMING_SNAKE_CASE_							)

     return train_dataloader, eval_dataloader

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     # Initialize accelerator
     lowercase_					:       Union[str, Any]     				=					Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase_					:       Tuple     				=					config['lr']
     lowercase_					:       Dict     				=					int(config['num_epochs']							)
     lowercase_					:       str     				=					int(config['seed']							)
     lowercase_					:       List[Any]     				=					int(config['batch_size']							)
     lowercase_					:       List[Any]     				=					args.model_name_or_path

     set_seed(SCREAMING_SNAKE_CASE_							)
     lowercase_				,lowercase_					:       List[str]     				=					get_dataloaders(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase_					:       Tuple     				=					AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_  ,       return_dict=SCREAMING_SNAKE_CASE_							)

     # Instantiate optimizer
     lowercase_					:       List[str]     				=					(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     lowercase_					:       int     				=					optimizer_cls(params=model.parameters()  ,       lr=SCREAMING_SNAKE_CASE_							)

     if accelerator.state.deepspeed_plugin is not None:
          lowercase_					:       Dict     				=					accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          lowercase_					:       Dict     				=					1
     lowercase_					:       Tuple     				=					(len(SCREAMING_SNAKE_CASE_							) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          lowercase_					:       Optional[int]     				=					get_linear_schedule_with_warmup(
              optimizer=SCREAMING_SNAKE_CASE_  ,       num_warmup_steps=0  ,       num_training_steps=SCREAMING_SNAKE_CASE_  ,       )
     else:
          lowercase_					:       str     				=					DummyScheduler(SCREAMING_SNAKE_CASE_  ,       total_num_steps=SCREAMING_SNAKE_CASE_  ,       warmup_num_steps=0							)

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     lowercase_				,lowercase_				,lowercase_				,lowercase_				,lowercase_					:       Optional[Any]     				=					accelerator.prepare(
         SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     # We need to keep track of how many total steps we have iterated over
     lowercase_					:       Optional[Any]     				=					0
     # We also need to keep track of the stating epoch so files are named properly
     lowercase_					:       Dict     				=					0

     # Now we train the model
     lowercase_					:       Tuple     				=					evaluate.load('glue'  ,       'mrpc'							)
     lowercase_					:       int     				=					0
     lowercase_					:       Any     				=					{}
     for epoch in range(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
          model.train()
          for step, batch in enumerate(SCREAMING_SNAKE_CASE_							):
               lowercase_					:       int     				=					model(**SCREAMING_SNAKE_CASE_							)
               lowercase_					:       Optional[int]     				=					outputs.loss
               lowercase_					:       List[str]     				=					loss / gradient_accumulation_steps
               accelerator.backward(SCREAMING_SNAKE_CASE_							)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

          model.eval()
          lowercase_					:       Union[str, Any]     				=					0
          for step, batch in enumerate(SCREAMING_SNAKE_CASE_							):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device							)
               with torch.no_grad():
                    lowercase_					:       int     				=					model(**SCREAMING_SNAKE_CASE_							)
               lowercase_					:       Union[str, Any]     				=					outputs.logits.argmax(dim=-1							)
               # It is slightly faster to call this once, than multiple times
               lowercase_				,lowercase_					:       Optional[Any]     				=					accelerator.gather(
                   (predictions, batch['labels'])							)  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(SCREAMING_SNAKE_CASE_							) - 1:
                         lowercase_					:       List[str]     				=					predictions[: len(eval_dataloader.dataset							) - samples_seen]
                         lowercase_					:       Union[str, Any]     				=					references[: len(eval_dataloader.dataset							) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=SCREAMING_SNAKE_CASE_  ,       references=SCREAMING_SNAKE_CASE_  ,       )

          lowercase_					:       str     				=					metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''  ,       SCREAMING_SNAKE_CASE_							)
          lowercase_					:       Any     				=					eval_metric['accuracy']

          if best_performance < eval_metric["accuracy"]:
               lowercase_					:       List[Any]     				=					eval_metric['accuracy']

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir  ,       'all_results.json'							)  ,       'w'							) as f:
               json.dump(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

def _UpperCamelCase   (       ):
     lowercase_					:       Tuple     				=					argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'							)
     parser.add_argument(
         '--model_name_or_path'  ,       type=SCREAMING_SNAKE_CASE_  ,       default='bert-base-cased'  ,       help='Path to pretrained model or model identifier from huggingface.co/models.'  ,       required=SCREAMING_SNAKE_CASE_  ,       )
     parser.add_argument(
         '--output_dir'  ,       type=SCREAMING_SNAKE_CASE_  ,       default='.'  ,       help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'  ,       )
     parser.add_argument(
         '--performance_lower_bound'  ,       type=SCREAMING_SNAKE_CASE_  ,       default=SCREAMING_SNAKE_CASE_  ,       help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'  ,       )
     parser.add_argument(
         '--num_epochs'  ,       type=SCREAMING_SNAKE_CASE_  ,       default=3  ,       help='Number of train epochs.'  ,       )
     lowercase_					:       Dict     				=					parser.parse_args()
     lowercase_					:       Optional[int]     				=					{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
     training_function(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
 main()





'''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)				)




