

import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler





@dataclass
class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    0
       lowercase_						=    False
       lowercase_						=    3.0





class        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             self.assertDictEqual(MockClass().to_kwargs() ,   {}     )
             self.assertDictEqual(MockClass(a=2     ).to_kwargs() ,   {'''a''': 2}     )
             self.assertDictEqual(MockClass(a=2 ,   b=lowercase__     ).to_kwargs() ,   {'''a''': 2, '''b''': True}     )
             self.assertDictEqual(MockClass(a=2 ,   c=2.25     ).to_kwargs() ,   {'''a''': 2, '''c''': 2.25}     )

       @require_cuda
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =GradScalerKwargs(init_scale=1_0_2_4 ,   growth_factor=2     )
             AcceleratorState._reset_state()
             __A    =Accelerator(mixed_precision='''fp16''' ,   kwargs_handlers=[scaler_handler]     )
             print(accelerator.use_fpaa     )
             __A    =accelerator.scaler

             # Check the kwargs have been applied
             self.assertEqual(scaler._init_scale ,   1024.0     )
             self.assertEqual(scaler._growth_factor ,   2.0     )

             # Check the other values are at the default
             self.assertEqual(scaler._backoff_factor ,   0.5     )
             self.assertEqual(scaler._growth_interval ,   2_0_0_0     )
             self.assertEqual(scaler._enabled ,   lowercase__     )


       @require_multi_gpu
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__     )]
             execute_subprocess_async(lowercase__ ,   env=os.environ.copy()     )


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _lowerCamelCase      :					Union[str, Any]					    =      Accelerator(kwargs_handlers=[ddp_scaler])
   _lowerCamelCase      :					List[str]					    =      torch.nn.Linear(100, 200)
   _lowerCamelCase      :					List[Any]					    =      accelerator.prepare(model)

   # Check the values changed in kwargs
   _lowerCamelCase      :					str					    =      ''''''
   _lowerCamelCase      :					Any					    =      model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)





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        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def __init__(							self ,   lowercase__ ,   lowercase__=7 ,   lowercase__=3 ,   lowercase__=1_8 ,   lowercase__=3_0 ,   lowercase__=4_0_0 ,   lowercase__=True ,   lowercase__=None ,   lowercase__=True ,   ):







             '''simple docstring'''



             __A    =size if size is not None else {'''height''': 1_8, '''width''': 1_8}
             __A    =parent
             __A    =batch_size
             __A    =num_channels
             __A    =image_size
             __A    =min_resolution
             __A    =max_resolution
             __A    =do_resize
             __A    =size
             __A    =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        lowerCAmelCase__  (					__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    LayoutLMvaImageProcessor if is_pytesseract_available() else None

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =LayoutLMvaImageProcessingTester(self     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class(**self.image_processor_dict     )
             self.assertTrue(hasattr(lowercase__ ,   '''do_resize'''     )     )
             self.assertTrue(hasattr(lowercase__ ,   '''size'''     )     )
             self.assertTrue(hasattr(lowercase__ ,   '''apply_ocr'''     )     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class.from_dict(self.image_processor_dict     )
             self.assertEqual(image_processor.size ,   {'''height''': 1_8, '''width''': 1_8}     )

             __A    =self.image_processing_class.from_dict(self.image_processor_dict ,   size=4_2     )
             self.assertEqual(image_processor.size ,   {'''height''': 4_2, '''width''': 4_2}     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             pass

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class(**self.image_processor_dict     )
             # create random PIL images
             __A    =prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowercase__     )
             for image in image_inputs:
                   self.assertIsInstance(lowercase__ ,   Image.Image     )

             # Test not batched input
             __A    =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 ,   lowercase__     )
             self.assertIsInstance(encoding.boxes ,   lowercase__     )

             # Test batched
             __A    =image_processing(lowercase__ ,   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    =self.image_processing_class(**self.image_processor_dict     )
             # create random numpy tensors
             __A    =prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowercase__ ,   numpify=lowercase__     )
             for image in image_inputs:
                   self.assertIsInstance(lowercase__ ,   np.ndarray     )

             # Test not batched input
             __A    =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    =image_processing(lowercase__ ,   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    =self.image_processing_class(**self.image_processor_dict     )
             # create random PyTorch tensors
             __A    =prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowercase__ ,   torchify=lowercase__     )
             for image in image_inputs:
                   self.assertIsInstance(lowercase__ ,   torch.Tensor     )

             # Test not batched input
             __A    =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    =image_processing(lowercase__ ,   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    =LayoutLMvaImageProcessor()

             from datasets import load_dataset

             __A    =load_dataset('''hf-internal-testing/fixtures_docvqa''' ,   split='''test'''     )

             __A    =Image.open(ds[0]['''file''']     ).convert('''RGB'''     )

             __A    =image_processing(lowercase__ ,   return_tensors='''pt'''     )

             self.assertEqual(encoding.pixel_values.shape ,   (1, 3, 2_2_4, 2_2_4)     )
             self.assertEqual(len(encoding.words     ) ,   len(encoding.boxes     )     )

             # fmt: off
             # the words and boxes were obtained with Tesseract 4.1.1
             __A    =[['''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    =[[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
             # fmt: on

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

             # with apply_OCR = False
             __A    =LayoutLMvaImageProcessor(apply_ocr=lowercase__     )

             __A    =image_processing(lowercase__ ,   return_tensors='''pt'''     )

             self.assertEqual(encoding.pixel_values.shape ,   (1, 3, 2_2_4, 2_2_4)     )


