





import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch

class      A       (	unittest.TestCase						):
      _snake_case							     =JukeboxTokenizer
      _snake_case							     ={
          '''artist''': '''Zac Brown Band''',
          '''genres''': '''Country''',
          '''lyrics''': '''I met a traveller from an antique land,
        Who said "Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        ''',
      }



      @require_torch
      def 	lowerCAmelCase__    (	self:						List[str]       )						->    List[str]:
         '''simple docstring'''

         import torch

         UpperCAmelCase_											=JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics"       )
         UpperCAmelCase_											=tokenizer(**self.metas       )["input_ids"]
         # fmt: off
         UpperCAmelCase_											=[
             torch.tensor([[
                 0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
                 76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                 44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                 47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                 76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                 30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
                 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
                 45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
                 41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                 19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                 76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                 76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                 64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                 27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                 34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                 27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                 41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                 44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                 76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                 32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                 40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                 20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                 45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                 31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                 45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                 76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                 34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                 31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                 40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                 38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                 78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                 76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                 41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                 27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                 46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                 76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                 41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                 46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                 41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                 78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                 40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                 27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                 76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                 76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                 41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                 76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                 27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                 78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                 34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                 44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                 40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                 78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                 46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                 38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                 40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                 27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                 20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                 76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                 76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                 76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                 76, 76]]       ),
             torch.tensor([[0, 0, 0, 1069, 11]]       ),
             torch.tensor([[0, 0, 0, 1069, 11]]       ),
         ]
         # fmt: on
         self.assertTrue(torch.allclose(tokens[0]		,  EXPECTED_OUTPUT[0]       )       )
         self.assertTrue(torch.allclose(tokens[1]		,  EXPECTED_OUTPUT[1]       )       )
         self.assertTrue(torch.allclose(tokens[2]		,  EXPECTED_OUTPUT[2]       )       )






      @require_torch
      def 	lowerCAmelCase__    (	self:						Dict       )						->    Tuple:
         '''simple docstring'''

         import torch

         UpperCAmelCase_											=JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics"       )
         UpperCAmelCase_											=tokenizer(**self.metas       )["input_ids"]
         # fmt: off
         UpperCAmelCase_											=[
             torch.tensor([[
                 0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
                 31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                 31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                 40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                 79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                 77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                 27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                 37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                 32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                 77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                 77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                 77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                 46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                 77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                 77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                 77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                 77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                 64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                 40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                 40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                 38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                 31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                 41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                 77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                 46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                 41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                 31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                 31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                 23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                 44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                 31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                 38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                 40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                 77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                 27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                 31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                 34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                 31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                 1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                 31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                 45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                 31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                 77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                 15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                 11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                 45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                 41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                 44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                 46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                 27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                 77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                 35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                 77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                 31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                 77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                 41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                 77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                 40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                 77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                 77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                 27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                 77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                 77, 77, 77, 77, 77, 77]]       ),
             torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]       ),
             torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]       ),
         ]
         # fmt: on
         self.assertTrue(torch.allclose(tokens[0]		,  EXPECTED_OUTPUT[0]       )       )
         self.assertTrue(torch.allclose(tokens[1]		,  EXPECTED_OUTPUT[1]       )       )
         self.assertTrue(torch.allclose(tokens[2]		,  EXPECTED_OUTPUT[2]       )       )











import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_pytesseract_available():
     from PIL import Image

     from transformers import LayoutLMvaImageProcessor

class      A       (	unittest.TestCase						):



      def __init__(	self:						Optional[Any]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Dict=7		,  _lowerCAmelCase:						int=3		,  _lowerCAmelCase:						int=18		,  _lowerCAmelCase:						Optional[int]=30		,  _lowerCAmelCase:						List[str]=400		,  _lowerCAmelCase:						Tuple=True		,  _lowerCAmelCase:						Optional[Any]=None		,  _lowerCAmelCase:						Optional[int]=True		,  )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=size if size is not None else {"height": 18, "width": 18}
         UpperCAmelCase_											=parent
         UpperCAmelCase_											=batch_size
         UpperCAmelCase_											=num_channels
         UpperCAmelCase_											=image_size
         UpperCAmelCase_											=min_resolution
         UpperCAmelCase_											=max_resolution
         UpperCAmelCase_											=do_resize
         UpperCAmelCase_											=size
         UpperCAmelCase_											=apply_ocr






      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    List[str]:
         '''simple docstring'''

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

@require_torch
@require_pytesseract
class      A       (	__lowercase		,      unittest.TestCase						):
      _snake_case							     =LayoutLMvaImageProcessor if is_pytesseract_available() else None



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=LayoutLMvaImageProcessingTester(self       )



      @property
      def 	lowerCAmelCase__    (	self:						str       )						->    int:
         '''simple docstring'''

         return self.image_processor_tester.prepare_image_processor_dict()



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "do_resize"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "size"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "apply_ocr"       )       )



      def 	lowerCAmelCase__    (	self:						str       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size		,  {"height": 18, "width": 18}       )

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



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    List[Any]:
         '''simple docstring'''

         pass



      def 	lowerCAmelCase__    (	self:						Dict       )						->    Optional[Any]:
         '''simple docstring'''

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

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

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

         # Test batched
         UpperCAmelCase_											=image_processing(_lowerCAmelCase		,  return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_images.shape		,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             )		,  )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         UpperCAmelCase_											=prepare_image_inputs(self.image_processor_tester		,  equal_resolution=_lowerCAmelCase		,  numpify=_lowerCAmelCase       )
         for image in image_inputs:
            self.assertIsInstance(_lowerCAmelCase		,  np.ndarray       )

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

         # Test batched
         UpperCAmelCase_											=image_processing(_lowerCAmelCase		,  return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_images.shape		,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             )		,  )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         UpperCAmelCase_											=prepare_image_inputs(self.image_processor_tester		,  equal_resolution=_lowerCAmelCase		,  torchify=_lowerCAmelCase       )
         for image in image_inputs:
            self.assertIsInstance(_lowerCAmelCase		,  torch.Tensor       )

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

         # Test batched
         UpperCAmelCase_											=image_processing(_lowerCAmelCase		,  return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_images.shape		,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             )		,  )






      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=LayoutLMvaImageProcessor()

         from datasets import load_dataset

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

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

         UpperCAmelCase_											=image_processing(_lowerCAmelCase		,  return_tensors="pt"       )

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

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

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

         # with apply_OCR = False
         UpperCAmelCase_											=LayoutLMvaImageProcessor(apply_ocr=_lowerCAmelCase       )

         UpperCAmelCase_											=image_processing(_lowerCAmelCase		,  return_tensors="pt"       )

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




