



import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__A   			=logging.get_logger(__name__)

@dataclass
class 		UpperCAmelCase__			:




			'''simple docstring'''






			UpperCamelCase									=       field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()   )}   )
			UpperCamelCase									=       field(
			    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
			UpperCamelCase									=       field(
			    default=128						,metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated, sequences shorter will be padded."""
			        )
			    }						,)
			UpperCamelCase									=       field(
			    default=__UpperCamelCase						,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	self.task_name.lower()

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       """train"""
			UpperCamelCase									=       """dev"""
			UpperCamelCase									=       """test"""

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       42
			UpperCamelCase									=       42
			UpperCamelCase									=       42




			def __init__(   self							: Dict       ,       a_							: GlueDataTrainingArguments       ,       a_							: PreTrainedTokenizerBase       ,       a_							: Optional[int] = None       ,       a_							: Union[str, Split] = Split.train       ,       a_							: Optional[str] = None       ,       ):
					'''simple docstring'''





					warnings.warn(
					    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
					    '''library. You can have a look at this example script for pointers: '''
					    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''       ,       a_       ,       )
					__UpperCAmelCase    :     List[Any]													=	args
					__UpperCAmelCase    :     int													=	glue_processors[args.task_name]()
					__UpperCAmelCase    :     Optional[int]													=	glue_output_modes[args.task_name]
					if isinstance(a_       ,       a_ ):
							try:
									__UpperCAmelCase    :     str													=	Split[mode]
							except KeyError:
									raise KeyError('''mode is not a valid split name''' )
        # Load data features from cache or dataset file
					__UpperCAmelCase    :     int													=	os.path.join(
					    cache_dir if cache_dir is not None else args.data_dir       ,       F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'       ,       )
					__UpperCAmelCase    :     Tuple													=	self.processor.get_labels()
					if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
					    "RobertaTokenizer",
					    "RobertaTokenizerFast",
					    "XLMRobertaTokenizer",
					    "BartTokenizer",
					    "BartTokenizerFast",
					):
							# HACK(label indices are swapped in RoBERTa pretrained model)
							__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[int]													=	label_list[2], label_list[1]
					__UpperCAmelCase    :     List[Any]													=	label_list

					# Make sure only the first process in distributed training processes the dataset,
					# and the others will use the cache.
					__UpperCAmelCase    :     int													=	cached_features_file + '''.lock'''
					with FileLock(a_ ):
							if os.path.exists(a_ ) and not args.overwrite_cache:
									__UpperCAmelCase    :     int													=	time.time()
									__UpperCAmelCase    :     Tuple													=	torch.load(a_ )
									logger.info(
									    F'Loading features from cached file {cached_features_file} [took %.3f s]'       ,       time.time() - start )
							else:
									logger.info(F'Creating features from dataset file at {args.data_dir}' )

									if mode == Split.dev:
											__UpperCAmelCase    :     Union[str, Any]													=	self.processor.get_dev_examples(args.data_dir )
									elif mode == Split.test:
											__UpperCAmelCase    :     Optional[Any]													=	self.processor.get_test_examples(args.data_dir )
									else:
											__UpperCAmelCase    :     Union[str, Any]													=	self.processor.get_train_examples(args.data_dir )
									if limit_length is not None:
											__UpperCAmelCase    :     Any													=	examples[:limit_length]
									__UpperCAmelCase    :     int													=	glue_convert_examples_to_features(
									    a_       ,       a_       ,       max_length=args.max_seq_length       ,       label_list=a_       ,       output_mode=self.output_mode       ,       )
									__UpperCAmelCase    :     List[Any]													=	time.time()
									torch.save(self.features       ,       a_ )
									# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
									logger.info(
									    F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )




			def __len__(   self							: List[str] ):
					'''simple docstring'''





					return len(self.features )




			def __getitem__(   self							: int       ,       a_							: str ):
					'''simple docstring'''





					return self.features[i]




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					return self.label_list










import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_pytesseract_available():
				from PIL import Image

				from transformers import LayoutLMvaImageProcessor

class 		UpperCAmelCase__			(			unittest.TestCase   ):




			'''simple docstring'''






			def __init__(   self							: Dict       ,       a_							: Dict       ,       a_							: Union[str, Any]=7       ,       a_							: Optional[Any]=3       ,       a_							: List[str]=18       ,       a_							: Union[str, Any]=30       ,       a_							: Union[str, Any]=4_00       ,       a_							: Union[str, Any]=True       ,       a_							: Tuple=None       ,       a_							: Optional[int]=True       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	size if size is not None else {'''height''': 18, '''width''': 18}
					__UpperCAmelCase    :     Dict													=	parent
					__UpperCAmelCase    :     str													=	batch_size
					__UpperCAmelCase    :     List[str]													=	num_channels
					__UpperCAmelCase    :     Union[str, Any]													=	image_size
					__UpperCAmelCase    :     Optional[int]													=	min_resolution
					__UpperCAmelCase    :     Union[str, Any]													=	max_resolution
					__UpperCAmelCase    :     Tuple													=	do_resize
					__UpperCAmelCase    :     List[str]													=	size
					__UpperCAmelCase    :     List[Any]													=	apply_ocr




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





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

@require_torch
@require_pytesseract
class 		UpperCAmelCase__			(			__UpperCamelCase						,unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       LayoutLMvaImageProcessor if is_pytesseract_available() else None




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	LayoutLMvaImageProcessingTester(self )




			@property
			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					return self.image_processor_tester.prepare_image_processor_dict()




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	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  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size       ,       {'''height''': 18, '''width''': 18} )

					__UpperCAmelCase    :     int													=	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							: int ):
					'''simple docstring'''





					pass




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





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

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

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

					# Test batched
					__UpperCAmelCase    :     Optional[Any]													=	image_processing(a_       ,       return_tensors='''pt''' ).pixel_values
					self.assertEqual(
					    encoded_images.shape       ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,       )




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					__UpperCAmelCase    :     Optional[Any]													=	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
					__UpperCAmelCase    :     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
					__UpperCAmelCase    :     int													=	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  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					__UpperCAmelCase    :     Tuple													=	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
					__UpperCAmelCase    :     str													=	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    :     List[Any]													=	image_processing(a_       ,       return_tensors='''pt''' ).pixel_values
					self.assertEqual(
					    encoded_images.shape       ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,       )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	LayoutLMvaImageProcessor()

					from datasets import load_dataset

					__UpperCAmelCase    :     Any													=	load_dataset('''hf-internal-testing/fixtures_docvqa'''       ,       split='''test''' )

					__UpperCAmelCase    :     Optional[int]													=	Image.open(ds[0]['''file'''] ).convert('''RGB''' )

					__UpperCAmelCase    :     Any													=	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
					__UpperCAmelCase    :     Any													=	[['''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    :     Tuple													=	[[[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
					__UpperCAmelCase    :     List[str]													=	LayoutLMvaImageProcessor(apply_ocr=a_ )

					__UpperCAmelCase    :     List[Any]													=	image_processing(a_       ,       return_tensors='''pt''' )

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





