


"""simple docstring"""







import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD


def 					lowercase      (    )->				Any:
  '''simple docstring'''







  a						:		Optional[int]											=      "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
  a						:		Union[str, Any]											=      Image.open(requests.get(A_  ,       stream=A_	).raw	).convert("RGB"	)

  return image


def 					lowercase      (    A_	)->				List[str]:
  '''simple docstring'''







  a						:		Union[str, Any]											=      []
  # fmt: off

  # vision encoder
  rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")	)
  rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")	)
  rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")	)
  rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")	)
  rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")	)
  rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")	)

  for i in range(config.vision_config.num_hidden_layers	):
    rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')	)
    rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')	)
    rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')	)
    rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')	)
    rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')	)
    rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)	)
    rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')	)
    rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')	)
    rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')	)
    rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')	)
    rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')	)

  # QFormer
  rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.embeddings.layernorm.weight")	)
  rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias")	)

  # fmt: on
  return rename_keys


def 					lowercase      (    A_  ,       A_  ,       A_	)->				Optional[int]:
  '''simple docstring'''







  a						:		int											=      dct.pop(A_	)
  a						:		str											=      val


def 					lowercase      (    A_  ,       A_	)->				int:
  '''simple docstring'''







  for i in range(config.vision_config.num_hidden_layers	):
    # read in original q and v biases
    a						:		List[Any]											=      state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''	)
    a						:		Tuple											=      state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''	)

    # next, set bias in the state dict
    a						:		Union[str, Any]											=      torch.cat((q_bias, torch.zeros_like(A_  ,       requires_grad=A_	), v_bias)	)
    a						:		str											=      qkv_bias


def 					lowercase      (    A_	)->				Dict:
  '''simple docstring'''







  a						:		List[str]											=      364 if "coco" in model_name else 224
  a						:		Dict											=      InstructBlipVisionConfig(image_size=A_	).to_dict()

  # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
  # seems like flan-T5 models don't have bos_token_id properly set?
  if "t5-xl" in model_name:
    a						:		List[str]											=      TaConfig.from_pretrained("google/flan-t5-xl"  ,       dense_act_fn="gelu"  ,       bos_token_id=1	).to_dict()
  elif "t5-xxl" in model_name:
    a						:		int											=      TaConfig.from_pretrained("google/flan-t5-xxl"  ,       dense_act_fn="gelu"  ,       bos_token_id=1	).to_dict()
  elif "vicuna-7b" in model_name:
    a						:		Tuple											=      LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"  ,       vocab_size=32_001	).to_dict()
  elif "vicuna-13b" in model_name:
    a						:		Optional[int]											=      LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf"  ,       vocab_size=32_001	).to_dict()
  else:
    raise ValueError("Model name not supported"	)

  # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
  a						:		Dict											=      InstructBlipQFormerConfig(vocab_size=30_523	).to_dict()
  a						:		Optional[int]											=      InstructBlipConfig(vision_config=A_  ,       text_config=A_  ,       qformer_config=A_	)

  return config, image_size


@torch.no_grad()
def 					lowercase      (    A_  ,       A_=None  ,       A_=False	)->				Union[str, Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      AutoTokenizer.from_pretrained("bert-base-uncased"  ,       truncation_side="left"	)
  qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"}	)

  if "t5" in model_name:
    a						:		int											=      TaTokenizerFast.from_pretrained("google/flan-t5-xl"  ,       truncation_side="left"	)
  elif "vicuna" in model_name:
    # the following was used in the original implementation:
    # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
    # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
    # tokenizer.add_special_tokens({"bos_token": "</s>"})
    # tokenizer.add_special_tokens({"eos_token": "</s>"})
    # tokenizer.add_special_tokens({"unk_token": "</s>"})
    a						:		Union[str, Any]											=      LlamaTokenizerFast.from_pretrained(
        "huggyllama/llama-7b"  ,       truncation_side="left"  ,       bos_token="</s>"  ,       unk_token="</s>"	)
    tokenizer.add_special_tokens({"pad_token": "[PAD]"}	)

  a							,		a						:		int											=      get_blipa_config(A_	)
  a						:		Tuple											=      InstructBlipForConditionalGeneration(A_	).eval()

  a						:		Optional[Any]											=      {
      "instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
      "instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
      "instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
      "instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
  }

  a							,		a						:		Optional[int]											=      model_name_to_original[model_name]

  # load original model
  print("Loading original model..."	)
  a						:		str											=      "cuda:1" if torch.cuda.is_available() else "cpu"
  a						:		Dict											=      "cuda:2" if torch.cuda.is_available() else "cpu"
  a							,		a							,		a						:		Tuple											=      load_model_and_preprocess(
      name=A_  ,       model_type=A_  ,       is_eval=A_  ,       device=A_	)
  original_model.eval()
  print("Done!"	)

  # update state dict keys
  a						:		Optional[int]											=      original_model.state_dict()
  a						:		Any											=      create_rename_keys(A_	)
  for src, dest in rename_keys:
    rename_key(A_  ,       A_  ,       A_	)

  # some keys can be renamed efficiently
  for key, val in state_dict.copy().items():
    a						:		Dict											=      state_dict.pop(A_	)
    if key.startswith("Qformer.bert"	):
      a						:		Any											=      key.replace("Qformer.bert"  ,       "qformer"	)
    if "attention.self" in key:
      a						:		Dict											=      key.replace("self"  ,       "attention"	)
    if "llm_proj" in key:
      a						:		Union[str, Any]											=      key.replace("llm_proj"  ,       "language_projection"	)
    if "t5_proj" in key:
      a						:		Union[str, Any]											=      key.replace("t5_proj"  ,       "language_projection"	)
    if key.startswith("llm_model"	):
      a						:		Any											=      key.replace("llm_model"  ,       "language_model"	)
    if key.startswith("t5"	):
      a						:		List[str]											=      key.replace("t5"  ,       "language"	)
    a						:		int											=      val

  # read in qv biases
  read_in_q_v_bias(A_  ,       A_	)

  # note: weights get loaded in torch.float32 by default
  hf_model.load_state_dict(A_  ,       strict=A_	)

  a						:		Tuple											=      load_demo_image()
  a						:		int											=      "What is unusual about this image?"

  # create processor
  a						:		str											=      BlipImageProcessor(
      size={"height": image_size, "width": image_size}  ,       image_mean=A_  ,       image_std=A_	)
  a						:		Dict											=      InstructBlipProcessor(
      image_processor=A_  ,       tokenizer=A_  ,       qformer_tokenizer=A_  ,       )
  a						:		List[str]											=      processor(images=A_  ,       text=A_  ,       return_tensors="pt"	).to(A_	)

  # make sure processor creates exact same pixel values
  a						:		int											=      vis_processors["eval"](A_	).unsqueeze(0	).to(A_	)
  a						:		Optional[Any]											=      inputs.pixel_values
  assert torch.allclose(original_pixel_values.to(pixel_values.device	)  ,       A_	)

  original_model.to(A_	)
  hf_model.to(A_	)
  with torch.no_grad():
    if "vicuna" in model_name:
      a						:		int											=      original_model({"image": original_pixel_values, "text_input": [prompt]}	).logits
      a						:		Any											=      hf_model(**A_	).logits
    else:
      a						:		int											=      original_model(
          {"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]}	).logits
      a						:		Optional[Any]											=      tokenizer("\n"  ,       return_tensors="pt"	).input_ids.to(A_	)
      a						:		str											=      label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id  ,       -100	)
      a						:		List[Any]											=      hf_model(**A_  ,       labels=A_	).logits

  print("First values of original logits:"  ,       original_logits[0, :3, :3]	)
  print("First values of HF logits:"  ,       logits[0, :3, :3]	)

  # assert values
  assert original_logits.shape == logits.shape
  a						:		Union[str, Any]											=      1e-4 if "vicuna" in model_name else 1e-5
  assert torch.allclose(original_logits.to(logits.device	)  ,       A_  ,       atol=A_	)
  print("Looks ok!"	)

  print("Generating with original model..."	)
  a						:		Union[str, Any]											=      original_model.generate({"image": original_pixel_values, "prompt": prompt}  ,       num_beams=5	)

  # important: we need to cast the weights of the HF model to the appropriate type
  print("Generating with HF model..."	)
  a						:		Union[str, Any]											=      hf_model.generate(
      **A_  ,       do_sample=A_  ,       num_beams=5  ,       max_length=256  ,       min_length=1  ,       top_p=0.9  ,       repetition_penalty=1.5  ,       length_penalty=1.0  ,       temperature=1  ,       )
  if "vicuna" in model_name:
    # convert output id 0 to 2 (eos_token_id)
    # TODO add this in the generate method?
    a						:		str											=      2
  print("Original generation:"  ,       A_	)
  a						:		List[Any]											=      processor.batch_decode(A_  ,       skip_special_tokens=A_	)
  a						:		List[Any]											=      [text.strip() for text in output_text]
  print("HF generation:"  ,       A_	)

  if pytorch_dump_folder_path is not None:
    processor.save_pretrained(A_	)
    hf_model.save_pretrained(A_	)

  if push_to_hub:
    processor.push_to_hub(F'''Salesforce/{model_name}'''	)
    hf_model.push_to_hub(F'''Salesforce/{model_name}'''	)


if __name__ == "__main__":
 __lowercase    =				argparse.ArgumentParser()
 __lowercase    =				[
     """instructblip-vicuna-7b""",
     """instructblip-vicuna-13b""",
     """instructblip-flan-t5-xl""",
     """instructblip-flan-t5-xxl""",
 ]
 parser.add_argument(
     """--model_name""",
     default="""instructblip-flan-t5-xl""",
     choices=choices,
     type=str,
     help="""Path to hf config.json of model to convert""",
 )
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument(
     """--push_to_hub""",
     action="""store_true""",
     help="""Whether to push the model and processor to the hub after converting""",
 )

 __lowercase    =				parser.parse_args()

 convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""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 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def __init__( self    :   Optional[int]    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Tuple=7    ,			__UpperCAmelCase    :   List[str]=3    ,			__UpperCAmelCase    :   int=18    ,			__UpperCAmelCase    :   int=30    ,			__UpperCAmelCase    :   Optional[int]=400    ,			__UpperCAmelCase    :   int=True    ,			__UpperCAmelCase    :   Optional[Any]=None    ,			__UpperCAmelCase    :   Union[str, Any]=True    ,			):
      a						:		Optional[int]											=      size if size is not None else {"height": 18, "width": 18}
      a						:		Any											=      parent
      a						:		int											=      batch_size
      a						:		str											=      num_channels
      a						:		Dict											=      image_size
      a						:		Dict											=      min_resolution
      a						:		Optional[int]											=      max_resolution
      a						:		Optional[int]											=      do_resize
      a						:		Any											=      size
      a						:		Dict											=      apply_ocr






    def      __snake_case							( self    :   Optional[int]):
      return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class 						_A  (  _a   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							List[str]     =	LayoutLMvaImageProcessor if is_pytesseract_available() else None






    def      __snake_case							( self    :   List[Any]):
      a						:		Optional[int]											=      LayoutLMvaImageProcessingTester(self)






    @property
    def      __snake_case							( self    :   Optional[int]):
      return self.image_processor_tester.prepare_image_processor_dict()






    def      __snake_case							( self    :   List[Any]):
      a						:		Union[str, Any]											=      self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__UpperCAmelCase    ,			"do_resize"))
      self.assertTrue(hasattr(__UpperCAmelCase    ,			"size"))
      self.assertTrue(hasattr(__UpperCAmelCase    ,			"apply_ocr"))






    def      __snake_case							( self    :   str):
      a						:		Dict											=      self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size    ,			{"height": 18, "width": 18})

      a						:		List[str]											=      self.image_processing_class.from_dict(self.image_processor_dict    ,			size=42)
      self.assertEqual(image_processor.size    ,			{"height": 42, "width": 42})






    def      __snake_case							( self    :   Union[str, Any]):
      pass






    def      __snake_case							( self    :   List[str]):
      # Initialize image_processing
      a						:		Any											=      self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      a						:		List[Any]											=      prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=__UpperCAmelCase)
      for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase    ,			Image.Image)

      # Test not batched input
      a						:		str											=      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    ,			__UpperCAmelCase)
      self.assertIsInstance(encoding.boxes    ,			__UpperCAmelCase)

      # Test batched
      a						:		Dict											=      image_processing(__UpperCAmelCase    ,			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      __snake_case							( self    :   Union[str, Any]):
      # Initialize image_processing
      a						:		List[str]											=      self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      a						:		Optional[Any]											=      prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=__UpperCAmelCase    ,			numpify=__UpperCAmelCase)
      for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase    ,			np.ndarray)

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

      # Test batched
      a						:		List[str]											=      image_processing(__UpperCAmelCase    ,			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      __snake_case							( self    :   List[str]):
      # Initialize image_processing
      a						:		str											=      self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      a						:		Tuple											=      prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=__UpperCAmelCase    ,			torchify=__UpperCAmelCase)
      for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase    ,			torch.Tensor)

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

      # Test batched
      a						:		List[str]											=      image_processing(__UpperCAmelCase    ,			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      __snake_case							( self    :   List[str]):
      # with apply_OCR = True
      a						:		List[Any]											=      LayoutLMvaImageProcessor()

      from datasets import load_dataset

      a						:		List[str]											=      load_dataset("hf-internal-testing/fixtures_docvqa"    ,			split="test")

      a						:		int											=      Image.open(ds[0]["file"]).convert("RGB")

      a						:		Dict											=      image_processing(__UpperCAmelCase    ,			return_tensors="pt")

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

      # fmt: off
      # the words and boxes were obtained with Tesseract 4.1.1
      a						:		Tuple											=      [["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						:		str											=      [[[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    ,			__UpperCAmelCase)
      self.assertListEqual(encoding.boxes    ,			__UpperCAmelCase)

      # with apply_OCR = False
      a						:		Optional[int]											=      LayoutLMvaImageProcessor(apply_ocr=__UpperCAmelCase)

      a						:		Dict											=      image_processing(__UpperCAmelCase    ,			return_tensors="pt")

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






