



import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a_    :  Optional[int]						  =		logging.getLogger(__name__)





@dataclass
class  lowerCamelCase__	:
     """simple docstring"""



     _A      =      field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Pretrained config name or path if not the same as model_name'})
     _A      =      field(
         default='NER'							,     metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'})
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
     _A      =      field(default=UpperCAmelCase_							,     metadata={'help': 'Set this flag to use fast tokenization.'})
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}							,     )





@dataclass
class  lowerCamelCase__	:
     """simple docstring"""



     _A      =      field(
         metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'})
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}							,     )
     _A      =      field(
         default=1_28							,     metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }							,     )
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Overwrite the cached training and evaluation sets'})



def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
      if len(sys.argv			) == 2 and sys.argv[1].endswith(".json"			):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
      else:
            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir			)
          and os.listdir(training_args.output_dir			)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
                " --overwrite_output_dir to overcome."			)

      lowerCamelCase             =		import_module("tasks"			)
      try:
            lowerCamelCase             =		getattr(UpperCAmelCase__ , model_args.task_type			)
            lowerCamelCase             =		token_classification_task_clazz()
      except AttributeError:
            raise ValueError(
                F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
                F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""			)

      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1			) , training_args.fpaa , )
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank			):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info("Training/evaluation parameters %s" , UpperCAmelCase__			)

      # Set seed
      set_seed(training_args.seed			)

      # Prepare CONLL-2003 task
      lowerCamelCase             =		token_classification_task.get_labels(data_args.labels			)
      lowerCamelCase             =		dict(enumerate(UpperCAmelCase__			)			)
      lowerCamelCase             =		len(UpperCAmelCase__			)

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      lowerCamelCase             =		AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid={label: i for i, label in enumerate(UpperCAmelCase__			)} , cache_dir=model_args.cache_dir , )
      lowerCamelCase             =		AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
      lowerCamelCase             =		AutoModelForTokenClassification.from_pretrained(
          model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path			) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )

      # Get datasets
      lowerCamelCase             =		(
          TokenClassificationDataset(
              token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
          if training_args.do_train
          else None
      )
      lowerCamelCase             =		(
          TokenClassificationDataset(
              token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
          if training_args.do_eval
          else None
      )

      def align_predictions(UpperCAmelCase__ , UpperCAmelCase__			) -> Tuple[List[int], List[int]]:
            lowerCamelCase             =		np.argmax(UpperCAmelCase__ , axis=2			)

            lowerCamelCase      ,				lowerCamelCase             =		preds.shape

            lowerCamelCase             =		[[] for _ in range(UpperCAmelCase__			)]
            lowerCamelCase             =		[[] for _ in range(UpperCAmelCase__			)]

            for i in range(UpperCAmelCase__			):
                  for j in range(UpperCAmelCase__			):
                        if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                              out_label_list[i].append(label_map[label_ids[i][j]]			)
                              preds_list[i].append(label_map[preds[i][j]]			)

            return preds_list, out_label_list

      def compute_metrics(UpperCAmelCase__			) -> Dict:
            lowerCamelCase      ,				lowerCamelCase             =		align_predictions(p.predictions , p.label_ids			)
            return {
                "accuracy_score": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__			),
                "precision": precision_score(UpperCAmelCase__ , UpperCAmelCase__			),
                "recall": recall_score(UpperCAmelCase__ , UpperCAmelCase__			),
                "f1": fa_score(UpperCAmelCase__ , UpperCAmelCase__			),
            }

      # Data collator
      lowerCamelCase             =		DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8			) if training_args.fpaa else None

      # Initialize our Trainer
      lowerCamelCase             =		Trainer(
          model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_process_zero():
                  tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
      lowerCamelCase             =		{}
      if training_args.do_eval:
            logger.info("*** Evaluate ***"			)

            lowerCamelCase             =		trainer.evaluate()

            lowerCamelCase             =		os.path.join(training_args.output_dir , "eval_results.txt"			)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase__ , "w"			) as writer:
                        logger.info("***** Eval results *****"			)
                        for key, value in result.items():
                              logger.info("  %s = %s" , UpperCAmelCase__ , UpperCAmelCase__			)
                              writer.write("%s = %s\n" % (key, value)			)

                  results.update(UpperCAmelCase__			)

    # Predict
      if training_args.do_predict:
            lowerCamelCase             =		TokenClassificationDataset(
                token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )

            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		trainer.predict(UpperCAmelCase__			)
            lowerCamelCase      ,				lowerCamelCase             =		align_predictions(UpperCAmelCase__ , UpperCAmelCase__			)

            lowerCamelCase             =		os.path.join(training_args.output_dir , "test_results.txt"			)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase__ , "w"			) as writer:
                        for key, value in metrics.items():
                              logger.info("  %s = %s" , UpperCAmelCase__ , UpperCAmelCase__			)
                              writer.write("%s = %s\n" % (key, value)			)

        # Save predictions
            lowerCamelCase             =		os.path.join(training_args.output_dir , "test_predictions.txt"			)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase__ , "w"			) as writer:
                        with open(os.path.join(data_args.data_dir , "test.txt"			) , "r"			) as f:
                              token_classification_task.write_predictions_to_file(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)

      return results

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      main()


if __name__ == "__main__":
     main()




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					,      __a					,      __a=7					,      __a=3					,      __a=18					,      __a=30					,      __a=4_00					,      __a=True					,      __a=None					,      __a=True					,      ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''simple docstring'''



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





@require_torch
@require_pytesseract
class  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      LayoutLMvaImageProcessor if is_pytesseract_available() else None







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		LayoutLMvaImageProcessingTester(self     )







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self.image_processor_tester.prepare_image_processor_dict()







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		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     _a   (self     ):






           '''simple docstring'''



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

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







     def     _a   (self     ):






           '''simple docstring'''



           pass







     def     _a   (self     ):






           '''simple docstring'''



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

           # Test not batched input
           lowerCamelCase             =		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
           lowerCamelCase             =		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     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.image_processing_class(**self.image_processor_dict     )
           # create random numpy tensors
           lowerCamelCase             =		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
           lowerCamelCase             =		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
           lowerCamelCase             =		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     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.image_processing_class(**self.image_processor_dict     )
           # create random PyTorch tensors
           lowerCamelCase             =		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
           lowerCamelCase             =		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
           lowerCamelCase             =		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     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		LayoutLMvaImageProcessor()

           from datasets import load_dataset

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

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

           lowerCamelCase             =		image_processing(__a					,      return_tensors="pt"     )

           self.assertEqual(encoding.pixel_values.shape					,      (1, 3, 2_24, 2_24)     )
           self.assertEqual(len(encoding.words     )					,      len(encoding.boxes     )     )

           # fmt: off
           # the words and boxes were obtained with Tesseract 4.1.1
           lowerCamelCase             =		[["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
           lowerCamelCase             =		[[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
           # fmt: on

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

           # with apply_OCR = False
           lowerCamelCase             =		LayoutLMvaImageProcessor(apply_ocr=__a     )

           lowerCamelCase             =		image_processing(__a					,      return_tensors="pt"     )

           self.assertEqual(encoding.pixel_values.shape					,      (1, 3, 2_24, 2_24)     )